**A Click Chemistry Approach to Tetrazoles: Recent Advances** Provisional chapterA Click Chemistry Approach to Tetrazoles: Recent Advances

DOI: 10.5772/intechopen.75720

Ravi Varala and Bollikolla Hari Babu Ravi Varala and Bollikolla Hari Babu

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75720

#### Abstract

Introduction to tetrazole and click chemistry approaches was briefed in a concise way in order to help the readers have a basic understanding. Tetrazole and its derivatives play very important role in medicinal and pharmaceutical applications. The synthesis of tetrazole derivatives can be approached in ecofriendly approaches such as the use of water as solvent, moderate conditions, nontoxic, easy extractions, easy setup, low cost, etc. with good to excellent yields.

Keywords: click chemistry, tetrazoles, biological activity, synthesis and molecular docking

## 1. Introduction

#### 1.1. Chemistry of tetrazoles

1H-Tetrazole (1) is a crystalline light yellow powder and odorless. Tetrazole shows melting point temperature at 155–157C. On heating, tetrazoles decomposed and emit toxic nitrogen fumes. These are burst vigorously on exposed to shock, fire, and heat on friction.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Tetrazoles easily react with acidic materials and strong oxidizers (acidic chloride, anhydrides, and strong acids) to liberate corrosive and toxic gases and heat. It undergoes reaction with few active metals and produces new compounds which are explosives to shocks. It involves exothermic reactions with reducing agents. On heating or burning, it releases carbon monoxide, carbon dioxide, and harmful nitrogen oxide. Tetrazole dissolves in water, acetonitrile, etc. Generally, dilute 1H-tetrazole in acetonitrile is used for DNA synthesis in biochemistry.

Researchers have opened up the likelihood of hitting specific focuses in complex cell lysates, by developing specific and controllable bio-orthogonal reactions. Recently, they have adjusted snap science for use in live cells, for instance, utilizing little atom tests that find and append to their objectives by click reactions. In spite of difficulties of cell porousness, bio-orthogonality, foundation naming, and response effectiveness, click responses have officially demonstrated valuable in another era of pull-down tests and fluorescence spectrometry. All the more as of late, novel strategies have been utilized to fuse click response accomplices onto and into biomolecules, including the joining of unnatural amino acids containing receptive gatherings into proteins and the change of nucleotides. These strategies speak to a piece of the field of compound science, in which click science assumes a central part by deliberately and particu-

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This refresh outlines the developing use of "click" science in various zones, for example, bioconjugation, sedate disclosure, materials science, and radiochemistry. It additionally talks about snap science responses that continue quickly with high selectivity, specificity, and yield. Two essential qualities make click science so appealing for collecting mixes, reagents, and biomolecules for preclinical and clinical applications. To begin with, click reactions are bioorthogonal. First of all, they are neither reciprocal nor their functional gatherings of different products connect with functionalized biomolecules. Secondly, the responses continue effortlessly under gentle nontoxic conditions. Example is their reaction at the room temperature and, for the most part, in water. The copper-catalyzed Huisgen cycloaddition, azide-alkyne [3+2] dipolar cycloaddition, Staudinger ligation, and azide-phosphine ligation all have these interesting qualities. These responses can be utilized to change one cell part while leaving others

Click chemistry has discovered expanding applications in all parts of medication revelation in restorative science, for example, for producing lead mixes through combinatorial strategies. Through bioconjugation click chemistry is thoroughly utilized in proteomics and nucleic exploration. In radiochemistry, specific radiolabeling of biomolecules in cells and living creatures for imaging and treatment has been acknowledged by this innovation. Bifunctional chelating operators for a few radionuclides are valuable for positron discharge tomography and single-photon emanation processed tomography. They have additionally been set up by click chemistry. This survey reasons that click chemistry is not the ideal conjugation, and gathering innovation for all applications, however, gives a capable, appealing another option to ordinary science. This science has turned out to be prevalent in fulfilling numerous criteria, e.g., biocompatibility, selectivity, yield, stereospecificity, etc. In this way, one can expect that it will subsequently turn into a more normal procedure soon for

Molecular docking (hereafter, MD) is the study of fitting together by two or more molecular components (e.g., drug and enzyme or protein). It is something like a problem of "lock and key" (Figure 1). It is an optimization issue which clearly explains how best a ligand and protein bind based on orientation. As both ligand and protein are flexible, a "hand-in-glove"

larly coupling secluded units to different finishes.

unharmed or untouched.

an extensive variety of uses.

1.3. Introduction to molecular docking

The presence of free N-H causes the acidic nature of tetrazoles and forms both aliphatic and aromatic heterocyclic compounds. Heterocycles of tetrazoles can stabilize the negative charge by delocalization and show corresponding carboxylic acid pKa values. Tetrazole nitrogen electron density results in the formation of so many stable metallic compounds and molecular complexes. This compound shows strong negative inductive effect (I electron withdrawing) and weak positive mesomeric effect (+M electron releasing).

The tetrazole is a five-membered aza compound with 6π electrons, and 5-substituted tetrazole reactivity is similar to aromatic compounds. The Huckel 6π electrons are satisfied by four π electrons of ring and one loan pair of electrons of nitrogen. The acidic nature of tetrazole is similar to corresponding carboxylic acids, but there is a difference in annular tautomerism of ring tetrazoles to carboxylic acids. The acidic nature of tetrazole is mainly affected by substitution compound nature at C-5 position. 5-Phenyltetrazole anion shows high acidic nature like benzoate due to resonance stabilization. A simple method to produce tetrazole anion is the reaction of tetrazole with metal hydroxides and can be stable in aqueous and alcoholic solution at high temperature.

#### 1.2. Introduction to click chemistry

Click chemistry is called as tagging in synthesis of chemicals. It is in the category of nonharmful reactions, proposed initially to unite the base materials of choice with certain bimolecular substance. It also can be termed as a non-peculiar reactive process. Indeed it explains a way of generating products that follow examples in nature. At the same time, it can produce the variety of materials by consolidating small compatible units. Usually, click reactions join a biomolecule and a reporter molecule. Click chemistry is not limited to the state of survival. It is the concept of a "click" reaction that has been used in pharmacological and various biomedical applications. It also can be described as non-single specific reaction etic application. Nevertheless, it is observed to be highly functional in the diagnosis of localization and qualification of bimolecular material.

Click reactions occur in one pot and generally make an evidence of being uninterrupted by water. They produce negligible and innocuous corollary and are spring-loaded. In addition to this, they are distinguished by a high thermodynamic driving force that pushes them rapidly and irrevocably to supply a single reaction product, with high reaction specificity. In few cases, they are created with both regio- and stereospecificity. These click reactions are specifically adaptable in the case of segregating and navigating the molecules in composite biological environments. In such conditions, items in like manner should be physiologically steady, and any side effects should be nonlethal.

Researchers have opened up the likelihood of hitting specific focuses in complex cell lysates, by developing specific and controllable bio-orthogonal reactions. Recently, they have adjusted snap science for use in live cells, for instance, utilizing little atom tests that find and append to their objectives by click reactions. In spite of difficulties of cell porousness, bio-orthogonality, foundation naming, and response effectiveness, click responses have officially demonstrated valuable in another era of pull-down tests and fluorescence spectrometry. All the more as of late, novel strategies have been utilized to fuse click response accomplices onto and into biomolecules, including the joining of unnatural amino acids containing receptive gatherings into proteins and the change of nucleotides. These strategies speak to a piece of the field of compound science, in which click science assumes a central part by deliberately and particularly coupling secluded units to different finishes.

This refresh outlines the developing use of "click" science in various zones, for example, bioconjugation, sedate disclosure, materials science, and radiochemistry. It additionally talks about snap science responses that continue quickly with high selectivity, specificity, and yield. Two essential qualities make click science so appealing for collecting mixes, reagents, and biomolecules for preclinical and clinical applications. To begin with, click reactions are bioorthogonal. First of all, they are neither reciprocal nor their functional gatherings of different products connect with functionalized biomolecules. Secondly, the responses continue effortlessly under gentle nontoxic conditions. Example is their reaction at the room temperature and, for the most part, in water. The copper-catalyzed Huisgen cycloaddition, azide-alkyne [3+2] dipolar cycloaddition, Staudinger ligation, and azide-phosphine ligation all have these interesting qualities. These responses can be utilized to change one cell part while leaving others unharmed or untouched.

Click chemistry has discovered expanding applications in all parts of medication revelation in restorative science, for example, for producing lead mixes through combinatorial strategies. Through bioconjugation click chemistry is thoroughly utilized in proteomics and nucleic exploration. In radiochemistry, specific radiolabeling of biomolecules in cells and living creatures for imaging and treatment has been acknowledged by this innovation. Bifunctional chelating operators for a few radionuclides are valuable for positron discharge tomography and single-photon emanation processed tomography. They have additionally been set up by click chemistry. This survey reasons that click chemistry is not the ideal conjugation, and gathering innovation for all applications, however, gives a capable, appealing another option to ordinary science. This science has turned out to be prevalent in fulfilling numerous criteria, e.g., biocompatibility, selectivity, yield, stereospecificity, etc. In this way, one can expect that it will subsequently turn into a more normal procedure soon for an extensive variety of uses.

#### 1.3. Introduction to molecular docking

Tetrazoles easily react with acidic materials and strong oxidizers (acidic chloride, anhydrides, and strong acids) to liberate corrosive and toxic gases and heat. It undergoes reaction with few active metals and produces new compounds which are explosives to shocks. It involves exothermic reactions with reducing agents. On heating or burning, it releases carbon monoxide, carbon dioxide, and harmful nitrogen oxide. Tetrazole dissolves in water, acetonitrile, etc. Generally, dilute 1H-tetrazole in acetonitrile is used for DNA synthesis in biochemistry.

The presence of free N-H causes the acidic nature of tetrazoles and forms both aliphatic and aromatic heterocyclic compounds. Heterocycles of tetrazoles can stabilize the negative charge by delocalization and show corresponding carboxylic acid pKa values. Tetrazole nitrogen electron density results in the formation of so many stable metallic compounds and molecular complexes. This compound shows strong negative inductive effect (I electron withdrawing)

The tetrazole is a five-membered aza compound with 6π electrons, and 5-substituted tetrazole reactivity is similar to aromatic compounds. The Huckel 6π electrons are satisfied by four π electrons of ring and one loan pair of electrons of nitrogen. The acidic nature of tetrazole is similar to corresponding carboxylic acids, but there is a difference in annular tautomerism of ring tetrazoles to carboxylic acids. The acidic nature of tetrazole is mainly affected by substitution compound nature at C-5 position. 5-Phenyltetrazole anion shows high acidic nature like benzoate due to resonance stabilization. A simple method to produce tetrazole anion is the reaction of tetrazole with metal hydroxides and can be stable in aqueous and alcoholic solution

Click chemistry is called as tagging in synthesis of chemicals. It is in the category of nonharmful reactions, proposed initially to unite the base materials of choice with certain bimolecular substance. It also can be termed as a non-peculiar reactive process. Indeed it explains a way of generating products that follow examples in nature. At the same time, it can produce the variety of materials by consolidating small compatible units. Usually, click reactions join a biomolecule and a reporter molecule. Click chemistry is not limited to the state of survival. It is the concept of a "click" reaction that has been used in pharmacological and various biomedical applications. It also can be described as non-single specific reaction etic application. Nevertheless, it is observed to be highly functional in the diagnosis of localization and qualification of

Click reactions occur in one pot and generally make an evidence of being uninterrupted by water. They produce negligible and innocuous corollary and are spring-loaded. In addition to this, they are distinguished by a high thermodynamic driving force that pushes them rapidly and irrevocably to supply a single reaction product, with high reaction specificity. In few cases, they are created with both regio- and stereospecificity. These click reactions are specifically adaptable in the case of segregating and navigating the molecules in composite biological environments. In such conditions, items in like manner should be physiologically steady, and

and weak positive mesomeric effect (+M electron releasing).

at high temperature.

52 Molecular Docking

bimolecular material.

1.2. Introduction to click chemistry

any side effects should be nonlethal.

Molecular docking (hereafter, MD) is the study of fitting together by two or more molecular components (e.g., drug and enzyme or protein). It is something like a problem of "lock and key" (Figure 1). It is an optimization issue which clearly explains how best a ligand and protein bind based on orientation. As both ligand and protein are flexible, a "hand-in-glove"

Figure 1. Lock and key models for Ligand-Target fitting.

word suit more effective compared to "lock and key" model. Both ligand and protein adapt their confirmation for overall binding, known as "induced effect."

Irbesartan (5), one of the essential tetrazole subsidiaries, has a place with the sort of medication called angiotensin II receptor enemy antihypertensives. This medication is utilized for the treatment of high blood pressure (hypertension) and for kidney issues because of Type 2

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Tetrazolo quinoline has an imminent and empowering new structure for the novel against the

diabetes (noninsulin-dependent).

anti-inflammatory (6) and antibacterial (7) agents [3, 4].

Piperidine-substituted tetrazoles (8) showed antifungal activity.

MD research depends mostly on computationally simulating the molecular recognition process by decreasing the free energy of overall system. Basic awareness on the preferred orientation in turn may be used to predict the binding affinity between two molecules used. Molecular docking is an invaluable tool in the field of molecular biology, computational structural biology, computer-aided drug designing, and pharmacogenomics.

There are two ways of docking approaches, namely, the first matching methodology which explains ligand-enzyme as complementary surfaces and the other simulated docking methodology of protein and ligand pairwise interaction energies. The application of docking in a targeted drug-delivery system is a huge benefit. One can study the size, shape, charge distribution, polarity, hydrogen bonding, and hydrophobic interactions of both ligand (drug) and receptor (target site).

#### 1.4. Aims and significance

The investigation of tetrazoles centers the most imperative organic exercises like antihypertensive, against inflammatory, antibacterial, antifungal, anticancer, antidiabetic, and hypoglycemic activity. Different strategies for synthesis and characterization techniques were discussed.

Throughout the previous couple of years, investigation of tetrazole chemistry has been rapidly expanded in view of its huge applications, for the most part because of the pretended by this heterocyclic usefulness in restorative chemistry. This provides more support to pharma field and metabolically stable swap for carboxylic acid functionalities, particularly, joining of the tetrazole exercises into angiotensin II rival structures, sartans (2–4) [1–4].

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Irbesartan (5), one of the essential tetrazole subsidiaries, has a place with the sort of medication called angiotensin II receptor enemy antihypertensives. This medication is utilized for the treatment of high blood pressure (hypertension) and for kidney issues because of Type 2 diabetes (noninsulin-dependent).

Tetrazolo quinoline has an imminent and empowering new structure for the novel against the anti-inflammatory (6) and antibacterial (7) agents [3, 4].

Piperidine-substituted tetrazoles (8) showed antifungal activity.

word suit more effective compared to "lock and key" model. Both ligand and protein adapt

MD research depends mostly on computationally simulating the molecular recognition process by decreasing the free energy of overall system. Basic awareness on the preferred orientation in turn may be used to predict the binding affinity between two molecules used. Molecular docking is an invaluable tool in the field of molecular biology, computational

There are two ways of docking approaches, namely, the first matching methodology which explains ligand-enzyme as complementary surfaces and the other simulated docking methodology of protein and ligand pairwise interaction energies. The application of docking in a targeted drug-delivery system is a huge benefit. One can study the size, shape, charge distribution, polarity, hydrogen bonding, and hydrophobic interactions of both ligand (drug) and

The investigation of tetrazoles centers the most imperative organic exercises like antihypertensive, against inflammatory, antibacterial, antifungal, anticancer, antidiabetic, and hypoglycemic activity. Different strategies for synthesis and characterization techniques were discussed. Throughout the previous couple of years, investigation of tetrazole chemistry has been rapidly expanded in view of its huge applications, for the most part because of the pretended by this heterocyclic usefulness in restorative chemistry. This provides more support to pharma field and metabolically stable swap for carboxylic acid functionalities, particularly, joining of the

their confirmation for overall binding, known as "induced effect."

Figure 1. Lock and key models for Ligand-Target fitting.

receptor (target site).

54 Molecular Docking

1.4. Aims and significance

structural biology, computer-aided drug designing, and pharmacogenomics.

tetrazole exercises into angiotensin II rival structures, sartans (2–4) [1–4].

Tetrazole derivatives (9) have been chosen and enhanced for their anticancer action on the majority of various human tumor cell lines separated from nine neoplastic disease sorts. The capable anticancer compound was observed to be dynamic with specific impact on ovarian cancer [1–4].

dissolved for efficient delivery. This makes necessary full-fledged characterization of drug position, comprising achieved synthetic strategies. In this chapter we directed on tetrazole biological activities. As a consequence, the need of synthetic routes to prepare tetrazole derivatives that are selective toward specific malfunctioning enzyme connects with illness. The study of good approaches of tetrazoles and medicinal applications will definitely allow to

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Since 1901, regular synthesis of 5-switched-1H-tetrazoles (16) has been accounted for to continuation of [3+2] cycloaddition of azide (14) with nitriles (15). This strategy experiences various disadvantages including utilization of costly and poisonous metal natural azide, exceedingly dampness touchy response conditions, solid Lewis corrosive, and hydrazoic corrosive. The "click" chemistry approach using metal catalysis in fluid arrangement is an outstanding evolution over last strategies, however every so often still requires the monotonous

Tetrazoles as a gathering of heterocyclic compounds are accounted for having an expansive range of organic exercises, for example, antibacterial, antifungal, antiviral, pain-relieving, mitigating, antiulcer, and antihypertensive exercises. Likewise, 5-substituted-1H-tetrazoles can work as lipophilic spacers and carboxylic corrosive surrogates, forte explosives and data recording frameworks in materials ligands, and forerunners of an assortment of nitrogen-

2.1. Synthesis and crystal structures of copper(II), zinc(II), lead(II), and cadmium(II)

A facile method to synthesize Cu(II), Zn(II), Pb(II), and Cd(II) complexes with di-anionic tetrazole-5-carboxylate (ttzCOO2) ligands (18), involving an in situ hydrolysis of 1H-

2.1.1. Tetrazole-5-carboxylate mixtures produced via in situ hydrolysis reaction

tetrazole-5-carboxylic acid ethyl ester sodium salt (17) was described [5–8].

propose more useful drugs.

1.6. History of tetrazoles

and tedious expulsion of metal salts from the acidic items.

containing heterocycles in coordination science.

2. Synthesis of tetrazole and its analogues

The 2,4 thiazolidinedione by-products (10) comprise tetrazole loop for their antidiabetic movement. The greater part of the mixes indicated great antidiabetic action when contrasted to glibenclamide [1–4].

The in vivo hypoglycemic action of tetrazole bears N-glycosides as SGLT2 inhibitors. A progression of 5-[(5-aryl-1H-pyrazol-3-yl)methyl]-1H-tetrazoles (11–13) has been assessed for their in vivo antihyperglycemic action. A portion of the mixture have indicated critical glucose bringing down the movement [1–4].

#### 1.5. Motivation of the chapter

Powerful drugs in opposition to hypertension, cancer, and bacterial and fungal infections have to fulfill a number of requirements like toxicity to tumor cells and are capable of being dissolved for efficient delivery. This makes necessary full-fledged characterization of drug position, comprising achieved synthetic strategies. In this chapter we directed on tetrazole biological activities. As a consequence, the need of synthetic routes to prepare tetrazole derivatives that are selective toward specific malfunctioning enzyme connects with illness. The study of good approaches of tetrazoles and medicinal applications will definitely allow to propose more useful drugs.

#### 1.6. History of tetrazoles

Tetrazole derivatives (9) have been chosen and enhanced for their anticancer action on the majority of various human tumor cell lines separated from nine neoplastic disease sorts. The capable anticancer compound was observed to be dynamic with specific impact on ovarian

The 2,4 thiazolidinedione by-products (10) comprise tetrazole loop for their antidiabetic movement. The greater part of the mixes indicated great antidiabetic action when contrasted to

The in vivo hypoglycemic action of tetrazole bears N-glycosides as SGLT2 inhibitors. A progression of 5-[(5-aryl-1H-pyrazol-3-yl)methyl]-1H-tetrazoles (11–13) has been assessed for their in vivo antihyperglycemic action. A portion of the mixture have indicated critical glucose

Powerful drugs in opposition to hypertension, cancer, and bacterial and fungal infections have to fulfill a number of requirements like toxicity to tumor cells and are capable of being

cancer [1–4].

56 Molecular Docking

glibenclamide [1–4].

bringing down the movement [1–4].

1.5. Motivation of the chapter

Since 1901, regular synthesis of 5-switched-1H-tetrazoles (16) has been accounted for to continuation of [3+2] cycloaddition of azide (14) with nitriles (15). This strategy experiences various disadvantages including utilization of costly and poisonous metal natural azide, exceedingly dampness touchy response conditions, solid Lewis corrosive, and hydrazoic corrosive. The "click" chemistry approach using metal catalysis in fluid arrangement is an outstanding evolution over last strategies, however every so often still requires the monotonous and tedious expulsion of metal salts from the acidic items.

Tetrazoles as a gathering of heterocyclic compounds are accounted for having an expansive range of organic exercises, for example, antibacterial, antifungal, antiviral, pain-relieving, mitigating, antiulcer, and antihypertensive exercises. Likewise, 5-substituted-1H-tetrazoles can work as lipophilic spacers and carboxylic corrosive surrogates, forte explosives and data recording frameworks in materials ligands, and forerunners of an assortment of nitrogencontaining heterocycles in coordination science.

## 2. Synthesis of tetrazole and its analogues

#### 2.1. Synthesis and crystal structures of copper(II), zinc(II), lead(II), and cadmium(II)

#### 2.1.1. Tetrazole-5-carboxylate mixtures produced via in situ hydrolysis reaction

A facile method to synthesize Cu(II), Zn(II), Pb(II), and Cd(II) complexes with di-anionic tetrazole-5-carboxylate (ttzCOO2) ligands (18), involving an in situ hydrolysis of 1Htetrazole-5-carboxylic acid ethyl ester sodium salt (17) was described [5–8].

2.2. Synthesis, characterization, and anti-inflammatory activity of novel N-substituted tetrazoles

5-Phenyl tetrazole (19) responds with acidic anhydride to produce 5-phenyl 1-acetyl tetrazole (20), which can be additionally served with various electronically or structurally divergent aldehydes to shape chalcones (21). Chalcones additionally respond with isonicotinic acid hydrazide to produce pyrazolines (22) [9–12].

2.4. Advances in the synthesis of tetrazoles coordinated to metal ions

Tetrazoles (25) react with metal bases or salts to synthesize tetrazole-containing metal deriva-

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N1-substituted tetrazoles (27) due to the absence of the labile hydrogen iota in the ring, so they don't display acidic properties. In this way, the N1- and N2-substituted tetrazoles (28) are associated with the development of metal subsidiaries only in the unbiased frame [22, 23].

To synthesize tetrazole-containing complexes with anionic ligands (29), tetrazole reacts with

2.4.1. Reactions of tetrazoles with metal bases and salts

2.4.2. Responses of N1-substituted tetrazoles with metal salts

2.4.3. Substitution of ligands for tetrazoles in coordination compounds

another ligand in a coordination compound [24, 25].

tives (26) [17–21].

Reagent conditions: (i) DMF/ammonium chloride; (ii) acetic anhydride, 20 min; (iii) R-CHO, 50% KOH, ethanol; (iv) isonicotinic acid hydrazide/GAA.

#### 2.3. Synthesis of 5-substituted 1H-Tetrazole using nano-ZnO/Co3O4 catalyst

5-Phenyl, 1H-tetrazole (24) is synthesized by reacting 1 mmol benzonitrile (23) and 1.5 mmol NaN3 in the presence of nano-ZnO/Co3O4 catalyst and 3 mL DMF for 12 h at 120–130C [13–16]. A Click Chemistry Approach to Tetrazoles: Recent Advances http://dx.doi.org/10.5772/intechopen.75720 59

#### 2.4. Advances in the synthesis of tetrazoles coordinated to metal ions

#### 2.4.1. Reactions of tetrazoles with metal bases and salts

2.2. Synthesis, characterization, and anti-inflammatory activity of novel N-substituted

5-Phenyl tetrazole (19) responds with acidic anhydride to produce 5-phenyl 1-acetyl tetrazole (20), which can be additionally served with various electronically or structurally divergent aldehydes to shape chalcones (21). Chalcones additionally respond with isonicotinic acid

Reagent conditions: (i) DMF/ammonium chloride; (ii) acetic anhydride, 20 min; (iii) R-CHO,

5-Phenyl, 1H-tetrazole (24) is synthesized by reacting 1 mmol benzonitrile (23) and 1.5 mmol NaN3 in the presence of nano-ZnO/Co3O4 catalyst and 3 mL DMF for 12 h at 120–130C [13–16].

2.3. Synthesis of 5-substituted 1H-Tetrazole using nano-ZnO/Co3O4 catalyst

tetrazoles

58 Molecular Docking

hydrazide to produce pyrazolines (22) [9–12].

50% KOH, ethanol; (iv) isonicotinic acid hydrazide/GAA.

Tetrazoles (25) react with metal bases or salts to synthesize tetrazole-containing metal derivatives (26) [17–21].

#### 2.4.2. Responses of N1-substituted tetrazoles with metal salts

N1-substituted tetrazoles (27) due to the absence of the labile hydrogen iota in the ring, so they don't display acidic properties. In this way, the N1- and N2-substituted tetrazoles (28) are associated with the development of metal subsidiaries only in the unbiased frame [22, 23].

#### 2.4.3. Substitution of ligands for tetrazoles in coordination compounds

To synthesize tetrazole-containing complexes with anionic ligands (29), tetrazole reacts with another ligand in a coordination compound [24, 25].

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#### 2.4.4. Metal-promoted cycle formation

The synthetic protocol involves reaction of inorganic azides and organic nitriles in the presence of Zn(II) salts under hydrothermal conditions to afford 5-substituted-1Н-tetrazoles via 1,3 dipolar cycloaddition [26].

#### 2.5. Synthesis of chosen 5-thio-substituted tetrazole subordinates and assessment of their antimicrobial exercises

To union of 5-thio replaced tetrazole subordinates and assessment of their antibacterial and antifungal properties, industrially accessible benzyl isothiocyanate (30) and sodium azide respond in presence of water to create 1-benzyl-1H-tetrazole-5-thiol (31) in great yield. The untouched mix is served with 1,3-dibromopropane with tetrahydrofuran to give a moderate 1-benzyl-5-[(3-bromopropyl)thio]-1H-tetrazole (32). The synthon is another compound and revealed here for the first time. This compound is treated with relating amines or thiols to manage the cost of the 5-thio-substituted tetrazole derivatives (33) [27–31].

#### 2.6. Synthesis of novel 1H-tetrazoles: spectral characterization and antibacterial activities

The tetrazoles (35, 37) were orchestrated in outstanding reactiveness by the response of sodium azide and triethyl orthoformate with relating amines, viz., 1-[3-(2-amino ethyl)-1H- 60 Molecular Docking A Click Chemistry Approach to Tetrazoles: Recent Advances http://dx.doi.org/10.5772/intechopen.75720 61

2.4.4. Metal-promoted cycle formation

dipolar cycloaddition [26].

antimicrobial exercises

The synthetic protocol involves reaction of inorganic azides and organic nitriles in the presence of Zn(II) salts under hydrothermal conditions to afford 5-substituted-1Н-tetrazoles via 1,3-

2.5. Synthesis of chosen 5-thio-substituted tetrazole subordinates and assessment of their

To union of 5-thio replaced tetrazole subordinates and assessment of their antibacterial and antifungal properties, industrially accessible benzyl isothiocyanate (30) and sodium azide respond in presence of water to create 1-benzyl-1H-tetrazole-5-thiol (31) in great yield. The untouched mix is served with 1,3-dibromopropane with tetrahydrofuran to give a moderate 1-benzyl-5-[(3-bromopropyl)thio]-1H-tetrazole (32). The synthon is another compound and revealed here for the first time. This compound is treated with relating amines or thiols to

2.6. Synthesis of novel 1H-tetrazoles: spectral characterization and antibacterial activities

The tetrazoles (35, 37) were orchestrated in outstanding reactiveness by the response of sodium azide and triethyl orthoformate with relating amines, viz., 1-[3-(2-amino ethyl)-1H-

manage the cost of the 5-thio-substituted tetrazole derivatives (33) [27–31].

indol-5-yl]-N-methyl methanesulfonamide (34) or 4-(4-aminobenzyl)-1,3-oxazolidin-2-one (36) in acidic corrosive or formic corrosive [32–36].

### 2.7. Synthesis of tetrazole-containing 1,2,3-thiadiazole subordinates through U-4CR and their opposition of TMV movement

To prepare tetrazole-containing 1,2,3-thiadiazole derivative (39), take 4-methyl-1,2,3-thiadiazole-5-carbaldehyde (38), and substituted amine is mixed in methanol at room temperature. The imine was precondensated for 0.5–1 h, and afterward cyclohexyl isocyanide and TMSN3 were included. The response blend was mixed for 12–24 h at room temperature until the point when the response was finished (demonstrated by TLC). At that point the natural dissolvable was dissipated in vacuum. The unrefined items were decontaminated by a silica gel segment utilizing ethyl acetic acid derivation/oil ether (1:2–1:3 (v/v), 60–90C) as an eluent to give the corresponding products as white or light yellow solids in direct yields [37–41].

Reagents and conditions: (a) NaBH4 (2.0 equiv.), EtOH, 0C for 1 h, r.t. for 6 h; (b) pyridinium chlorochromate (2.0 equiv.), CH2Cl2, r.t. for 8 h; (c) (i) R-NH2 (1.0 equiv.), CH3OH, r.t. for 0.5– 1 h; and (ii) cyclohexyl isocyanide (1.2 equiv.), TMSN3 (1.5 equiv.), r.t. for 12–24 h.

2.10. Synthesis of 1-substituted-1H-1,2,3,4-tetrazoles catalyzed by methanesulfonic acid

A blend of chosen amine (44), triethyl orthoformate (0.4 ml), and sodium azide (0.13 g) was added to methanesulfonic acid (20 mol%). The blend was mixed for adjusted time, and the advance of the response was checked by TLC. The mixture was stirred for the specified time to

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63

under neat conditions

obtain 1-substituted 1H-1,2,3,4-tetrazole (45) [46–50].

#### 2.8. Synthesis of 2-{[2-(1H-tetrazole-5-yl)ethyl]sulfanyl}-1,3-benzimidazole (3) as antioxidants

10 mmol of 3-(1,3-benzimidazole-2-yl-sulfanyl)propanenitrile, 10 mmol sodium azide (40), 10 mL of DMF, and 10 mmol of zinc chloride were accepted in a flask, and the substances were warmed in an oil bath for 6 h at 125C. After the routine workup, it was recrystallized from equimolar DMF-ethanol blend to get compound (41) [42, 43].

### 2.9. Single-leap synthesis of sterically hindered b1,5-disubstituted tetrazoles from bulky secondary N-benzoyl amides: usage of triazidochlorosilane (TACS)

A mixture of 1-(2-trifluoromethane phenyl)-5-phenyl-1H-tetrazole (42), sodium azide, and tetrachlorosilane in dry acetonitrile was refluxed under dry conditions to give the corresponding tetrazole 43 [44, 45].

indol-5-yl]-N-methyl methanesulfonamide (34) or 4-(4-aminobenzyl)-1,3-oxazolidin-2-one (36)

2.7. Synthesis of tetrazole-containing 1,2,3-thiadiazole subordinates through U-4CR and

nding products as white or light yellow solids in direct yields [37–41].

To prepare tetrazole-containing 1,2,3-thiadiazole derivative (39), take 4-methyl-1,2,3-thiadiazole-5-carbaldehyde (38), and substituted amine is mixed in methanol at room temperature. The imine was precondensated for 0.5–1 h, and afterward cyclohexyl isocyanide and TMSN3 were included. The response blend was mixed for 12–24 h at room temperature until the point when the response was finished (demonstrated by TLC). At that point the natural dissolvable was dissipated in vacuum. The unrefined items were decontaminated by a silica gel segment utilizing ethyl acetic acid derivation/oil ether (1:2–1:3 (v/v), 60–90C) as an eluent to give the correspo-

Reagents and conditions: (a) NaBH4 (2.0 equiv.), EtOH, 0C for 1 h, r.t. for 6 h; (b) pyridinium chlorochromate (2.0 equiv.), CH2Cl2, r.t. for 8 h; (c) (i) R-NH2 (1.0 equiv.), CH3OH, r.t. for 0.5–

10 mmol of 3-(1,3-benzimidazole-2-yl-sulfanyl)propanenitrile, 10 mmol sodium azide (40), 10 mL of DMF, and 10 mmol of zinc chloride were accepted in a flask, and the substances were warmed in an oil bath for 6 h at 125C. After the routine workup, it was recrystallized from

2.9. Single-leap synthesis of sterically hindered b1,5-disubstituted tetrazoles from bulky

A mixture of 1-(2-trifluoromethane phenyl)-5-phenyl-1H-tetrazole (42), sodium azide, and tetrachlorosilane in dry acetonitrile was refluxed under dry conditions to give the corresponding

1 h; and (ii) cyclohexyl isocyanide (1.2 equiv.), TMSN3 (1.5 equiv.), r.t. for 12–24 h.

2.8. Synthesis of 2-{[2-(1H-tetrazole-5-yl)ethyl]sulfanyl}-1,3-benzimidazole (3) as

equimolar DMF-ethanol blend to get compound (41) [42, 43].

secondary N-benzoyl amides: usage of triazidochlorosilane (TACS)

in acidic corrosive or formic corrosive [32–36].

their opposition of TMV movement

62 Molecular Docking

antioxidants

tetrazole 43 [44, 45].

#### 2.10. Synthesis of 1-substituted-1H-1,2,3,4-tetrazoles catalyzed by methanesulfonic acid under neat conditions

A blend of chosen amine (44), triethyl orthoformate (0.4 ml), and sodium azide (0.13 g) was added to methanesulfonic acid (20 mol%). The blend was mixed for adjusted time, and the advance of the response was checked by TLC. The mixture was stirred for the specified time to obtain 1-substituted 1H-1,2,3,4-tetrazole (45) [46–50].

The above experiments yield very good result in the presence of various catalysts especially with silica sulfuric acid.

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65

#### 2.11. Productive synthesis of 1,5-disubstituted-1H-tetrazoles through an Ugi-azide procedure

The readiness of 1,5-disubstituted-1H-tetrazoles (47) was achieved in no catalyst conditions, optimized Ugi-azide process. The addition of aryl-ethanamine derivatives (46), aldehydes, isocyanides, and TMSN3 in MeOH under mild conditions to give corresponding tetrazole (47) at room temperature [51–56].

#### 2.12. Straightforward and proficient strategy for the synthesis of novel tetrazole derivatives and its antibacterial exercises

A progression of novel 5-phenyl-1-acyl-1,2,3,4-tetrazoles (53) has been combined by buildup of 5-phenyl-1,2,3,4-tetrazoles (49, 51) with different acylating reagents. The union of tetrazoles by the response of amines (48, 50) with sodium azide and triethyl orthoformate in acidic medium [34, 36, 57–59].

#### 2.13. Synthesis and characterization of new 5-supplemented 1H-tetrazoles in water: a greener approach

A blend of carbonyl compound, malononitrile, and sodium azide in the presence of H2O was mixed at 50C for proper time to outfit the required tetrazole [34, 60–63].

#### 2.13.1. Synthesis of (1H-tetrazole-5-yl) acrylonitrile (NPTA)

3-Nitro benzaldehyde (54) reacts with malononitrile in the presence of sodium azide to give NPTA (55).

64 Molecular Docking A Click Chemistry Approach to Tetrazoles: Recent Advances http://dx.doi.org/10.5772/intechopen.75720 65

The above experiments yield very good result in the presence of various catalysts especially

2.11. Productive synthesis of 1,5-disubstituted-1H-tetrazoles through an Ugi-azide

The readiness of 1,5-disubstituted-1H-tetrazoles (47) was achieved in no catalyst conditions, optimized Ugi-azide process. The addition of aryl-ethanamine derivatives (46), aldehydes, isocyanides, and TMSN3 in MeOH under mild conditions to give corresponding tetrazole (47)

2.12. Straightforward and proficient strategy for the synthesis of novel tetrazole derivatives

A progression of novel 5-phenyl-1-acyl-1,2,3,4-tetrazoles (53) has been combined by buildup of 5-phenyl-1,2,3,4-tetrazoles (49, 51) with different acylating reagents. The union of tetrazoles by the response of amines (48, 50) with sodium azide and triethyl orthoformate in acidic medium

A blend of carbonyl compound, malononitrile, and sodium azide in the presence of H2O was

3-Nitro benzaldehyde (54) reacts with malononitrile in the presence of sodium azide to give

2.13. Synthesis and characterization of new 5-supplemented 1H-tetrazoles in water: a

mixed at 50C for proper time to outfit the required tetrazole [34, 60–63].

2.13.1. Synthesis of (1H-tetrazole-5-yl) acrylonitrile (NPTA)

with silica sulfuric acid.

procedure

at room temperature [51–56].

and its antibacterial exercises

[34, 36, 57–59].

greener approach

NPTA (55).

2.13.2. Synthesis of (E)-3,3<sup>0</sup> -(phenyl)-bis (1,4(2-(1H-tetrazole-5-yl)) acrylonitrile) (PBTA)

Aryl dicarbonyl compound (55) reacts with malononitrile in the presence of sodium azide to give PBTA (56).

2.15. Synthesis, characterization, and biological examination of novel thiazole outcomes

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A mixture of indole-3-carbaldehyde (62) and chloroethyl acetic acid was mixed in DMF. To this, anhydrous K2CO3 is included, and the response reaction mixture is mixed at room temperature (35C) for 8 hours, to manage the effective yield of 2-(3-formyl-1H-indol-1-yl)

carrying indole moiety bearing tetrazole

acetate (63).

2.13.3. Synthesis of (z)-3-(hexahydro-2,4,6-trioxopyrimidine-5-yl)2-(1H-tetrazole-5-yl)-2-butane nitrile (BTBN)

2,4,6-Trioxo derivative-5-yl compound (57) reacts with malononitrile in presence of sodium azide to give BTBN (58).

#### 2.14. Preparation of 5-phenyltetrazole and its N-methyl derivatives

Azidation of benzonitrile (59) with dimethylammonium azide passive 5-phenyltetrazole dimethylammonium salt (60) was executed under microreactor setting. The energy of azidation of benzonitrile in DMF was examined at the range 80–95�C. The thermodynamic parameters of azidation under the microreactor conditions relate to the component of the 1,3-dipolar cycloaddition of azides to nitriles [17, 64–67].

A Click Chemistry Approach to Tetrazoles: Recent Advances http://dx.doi.org/10.5772/intechopen.75720 67

2.13.2. Synthesis of (E)-3,3<sup>0</sup>

give PBTA (56).

66 Molecular Docking

nitrile (BTBN)

azide to give BTBN (58).

dition of azides to nitriles [17, 64–67].


Aryl dicarbonyl compound (55) reacts with malononitrile in the presence of sodium azide to

2.13.3. Synthesis of (z)-3-(hexahydro-2,4,6-trioxopyrimidine-5-yl)2-(1H-tetrazole-5-yl)-2-butane

2.14. Preparation of 5-phenyltetrazole and its N-methyl derivatives

2,4,6-Trioxo derivative-5-yl compound (57) reacts with malononitrile in presence of sodium

Azidation of benzonitrile (59) with dimethylammonium azide passive 5-phenyltetrazole dimethylammonium salt (60) was executed under microreactor setting. The energy of azidation of benzonitrile in DMF was examined at the range 80–95�C. The thermodynamic parameters of azidation under the microreactor conditions relate to the component of the 1,3-dipolar cycload-

#### 2.15. Synthesis, characterization, and biological examination of novel thiazole outcomes carrying indole moiety bearing tetrazole

A mixture of indole-3-carbaldehyde (62) and chloroethyl acetic acid was mixed in DMF. To this, anhydrous K2CO3 is included, and the response reaction mixture is mixed at room temperature (35C) for 8 hours, to manage the effective yield of 2-(3-formyl-1H-indol-1-yl) acetate (63).

To this mixture, aniline, EtOH, and three drops of acidic corrosive are included and after that a warmed steam shower for 5–6 h to obtain the compound (64) ethyl 2-(3-phenyl amino)methyl-1H-indole-1-yl-acetic acid. Compound (64) is changed over into ethyl2-(3- (1-phenyl-1H-tetrazol-5-yl)-1H-indol-1-yl)acetate (65) by utilizing of conditions. Schiff base combination of thiazole subsidiaries containing indole moiety bearing tetrazole ring (66) was incorporated by the buildup of 2-(3-(3-chloro-1-(4-substituted phenyl)-4-tetrazole-2 yl)-1H-indole-1-yl) acetohydrazide with potassium thiocyanide and substituted ketones. At that point 1-(2-(3-(3-chloro-1-(4-substituted phenyl)-4-tetrazole-2-yl)-1H-indol-1-yl)acetyl)-4-(2-(4-substituted phenyl)hydrazono)-3-(trifluoromethyl)-1H-pyrazol-5(4H)-one (67) is obtained [68–72].

### 2.16. A fast metal-free union of 5-substituted-1H-tetrazoles utilizing cuttlebone as a characteristic high compelling and minimal effort heterogeneous catalyst

Cuttlebone has a characteristic minimal effort heterogeneous impetus with high porosity. It carries high flexural firmness, high compressive quality, and high thermal solidness. Cuttlebone was taken out from cuttlefish (Sepia esculenta), which is ordinarily found in saltwater shorelines like Persian Gulf in Iran. This specimen can be found in a genuinely decent condition with negligible outer destruction. So as to evacuate contamination on the surface of cuttlebone, the catalyst has been powdered, washed with refined water, and dried at 100C for 2 h [52, 73, 74]. The SEM image of cuttlebone was shown in Figure 2.

Figure 3 describes the system for the synthesis of 5-substituted-1H-tetrazoles within the sight

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Figure 3. Synthesis of 5-substituted-1H-tetrazoles in the sight of cuttlebone.

of cuttlebone [30, 75].

An advantageous, fast, and metal-free synthesis of 5-substituted-1H-tetrazoles (70) is depicted by [3+2] cycloaddition response of nitriles (68) with sodium azide (69).

Figure 2. SEM image of cuttlebone.

A Click Chemistry Approach to Tetrazoles: Recent Advances http://dx.doi.org/10.5772/intechopen.75720 69

To this mixture, aniline, EtOH, and three drops of acidic corrosive are included and after that a warmed steam shower for 5–6 h to obtain the compound (64) ethyl 2-(3-phenyl amino)methyl-1H-indole-1-yl-acetic acid. Compound (64) is changed over into ethyl2-(3- (1-phenyl-1H-tetrazol-5-yl)-1H-indol-1-yl)acetate (65) by utilizing of conditions. Schiff base combination of thiazole subsidiaries containing indole moiety bearing tetrazole ring (66) was incorporated by the buildup of 2-(3-(3-chloro-1-(4-substituted phenyl)-4-tetrazole-2 yl)-1H-indole-1-yl) acetohydrazide with potassium thiocyanide and substituted ketones. At that point 1-(2-(3-(3-chloro-1-(4-substituted phenyl)-4-tetrazole-2-yl)-1H-indol-1-yl)acetyl)-4-(2-(4-substituted phenyl)hydrazono)-3-(trifluoromethyl)-1H-pyrazol-5(4H)-one (67)

2.16. A fast metal-free union of 5-substituted-1H-tetrazoles utilizing cuttlebone as a

Cuttlebone has a characteristic minimal effort heterogeneous impetus with high porosity. It carries high flexural firmness, high compressive quality, and high thermal solidness. Cuttlebone was taken out from cuttlefish (Sepia esculenta), which is ordinarily found in saltwater shorelines like Persian Gulf in Iran. This specimen can be found in a genuinely decent condition with negligible outer destruction. So as to evacuate contamination on the surface of cuttlebone, the catalyst has been powdered, washed with refined water, and dried at 100C

An advantageous, fast, and metal-free synthesis of 5-substituted-1H-tetrazoles (70) is depicted

characteristic high compelling and minimal effort heterogeneous catalyst

for 2 h [52, 73, 74]. The SEM image of cuttlebone was shown in Figure 2.

by [3+2] cycloaddition response of nitriles (68) with sodium azide (69).

is obtained [68–72].

68 Molecular Docking

Figure 2. SEM image of cuttlebone.

Figure 3 describes the system for the synthesis of 5-substituted-1H-tetrazoles within the sight of cuttlebone [30, 75].

Figure 3. Synthesis of 5-substituted-1H-tetrazoles in the sight of cuttlebone.

## 3. Molecular docking-tetrazole derivatives

There are several literature reports pertaining to molecular docking studies of divergent tetrazole derivatives. We are citing a few for basic understanding of the readers who can explore this field a lot.

References

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Chemistry. Vol. 4. Oxford, UK: Pergamon; 1996

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Chemistry II. Vol. 4, 17. New York: Elsevier; 1996. Chapter 4. p. 621

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medical Press; 1980. Chapter 4. p. 151

Very recently, Jonnalagadda et al. have synthesized some tetrazole-linked benzochromene derivatives and had their molecular docking study as well [76]. 5-Substituted 5-styryl terazolo [1,5-c]quinazoline derivatives were studied for their cytotoxicity and molecular docking by Parbhoo et al. [77]. In a similar fashion, several tetrazole derivatives were synthesized and subject to molecular docking in recent years [78–82].

## 4. Conclusion

The synthesis of tetrazole derivatives can be approached in various methods like ecofriendly, water solvent, moderate conditions, nontoxic, easy extractions, easy setup, low cost, etc. with good to excellent yields. The structural analysis was done by thermal and spectroscopic methods. Tetrazole and its derivatives play very important role in medicinal and pharmaceutical applications. Molecular docking studies play a vital role to decide the synthesis of pharmacologically relevant tetrazole derivatives in the near future. This facilitates, in fact, for new researchers to choose this topic as an apt and relevant research topic to explore.

## Acknowledgements

Dr. Ravi Varala thanks honorable Vice Chancellor, Sri Dr. A. Ashok, IAS, RGUKT Basar, and T. N. Venkata Swamy, administrative officer, for his kind support and encouragement.

## Author details

Ravi Varala<sup>1</sup> \* and Bollikolla Hari Babu<sup>2</sup>

\*Address all correspondence to: ravivarala@rgukt.ac.in

1 Department of Chemistry, Rajiv Gandhi University of Knowledge Technologies, Basar, Nirmal, Telangana, India

2 Department of Chemistry, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India

## References

3. Molecular docking-tetrazole derivatives

subject to molecular docking in recent years [78–82].

\* and Bollikolla Hari Babu<sup>2</sup>

\*Address all correspondence to: ravivarala@rgukt.ac.in

a lot.

70 Molecular Docking

4. Conclusion

Acknowledgements

Author details

Nirmal, Telangana, India

Ravi Varala<sup>1</sup>

There are several literature reports pertaining to molecular docking studies of divergent tetrazole derivatives. We are citing a few for basic understanding of the readers who can explore this field

Very recently, Jonnalagadda et al. have synthesized some tetrazole-linked benzochromene derivatives and had their molecular docking study as well [76]. 5-Substituted 5-styryl terazolo [1,5-c]quinazoline derivatives were studied for their cytotoxicity and molecular docking by Parbhoo et al. [77]. In a similar fashion, several tetrazole derivatives were synthesized and

The synthesis of tetrazole derivatives can be approached in various methods like ecofriendly, water solvent, moderate conditions, nontoxic, easy extractions, easy setup, low cost, etc. with good to excellent yields. The structural analysis was done by thermal and spectroscopic methods. Tetrazole and its derivatives play very important role in medicinal and pharmaceutical applications. Molecular docking studies play a vital role to decide the synthesis of pharmacologically relevant tetrazole derivatives in the near future. This facilitates, in fact, for new

Dr. Ravi Varala thanks honorable Vice Chancellor, Sri Dr. A. Ashok, IAS, RGUKT Basar, and

T. N. Venkata Swamy, administrative officer, for his kind support and encouragement.

1 Department of Chemistry, Rajiv Gandhi University of Knowledge Technologies, Basar,

2 Department of Chemistry, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India

researchers to choose this topic as an apt and relevant research topic to explore.


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74 Molecular Docking

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50:3525


**Chapter 5**

**Provisional chapter**

**Docking Studies on Novel Analogues of 8-Chloro-**

Molecular docking studies have been carried out for a better understanding of the drugreceptor interactions. All the synthesized compounds have been subjected to molecular docking against targets that have been chosen based on the specific mechanism of action of the quinolones used in the antibacterial activity screening. A study of the characteristics and molecular properties of the small molecule known as ligand has been realized. In the first stage of the study, the 2D and 3D structures have been generated. The most stable conformer for each structure was obtained by geometry optimization and energy minimization. A series of topological, conformational characteristics and QSAR properties, important to assess the flexibility and the ability of the studied conformer to bind to the protein receptor, were determined and analyzed. These properties were discussed in order to assess the flexibility and the binding ability of studied conformers to bind to the receptor protein. The docking studies have been carried out. The score and hydrogen bonds formed with the amino acids from group interaction atoms are used to predict the binding modes, the binding affinities and the

orientation of the docked quinolones in the active site of the protein receptor.

**Keywords:** molecular docking, antimicrobial activity, fluoroquinolones, quinolones

An important parameter in the development of a new drug is the drug's affinity to the identified target (protein/enzyme). Predicting the ligand binding to the target (protein/enzyme) by molecular simulation would allow the synthesis to be restricted to the most promising compounds [1–9]. Molecular docking can be accomplished by two interdependent steps [7–9]. The first step consists in sampling the ligand conformations in the active site of the protein receptor. The second step is to classify these conformations by a scoring function. The sampling algorithms

**Docking Studies on Novel Analogues of 8-Chloro-**

DOI: 10.5772/intechopen.72995

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

**Quinolones against** *Staphylococcus aureus*

**Quinolones against** *Staphylococcus aureus*

Lucia Pintilie and Amalia Stefaniu

Lucia Pintilie and Amalia Stefaniu

http://dx.doi.org/10.5772/intechopen.72995

**Abstract**

**1. Introduction**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

**Provisional chapter**

## **Docking Studies on Novel Analogues of 8-Chloro-Quinolones against** *Staphylococcus aureus* **Quinolones against** *Staphylococcus aureus*

**Docking Studies on Novel Analogues of 8-Chloro-**

DOI: 10.5772/intechopen.72995

Lucia Pintilie and Amalia Stefaniu Lucia Pintilie and Amalia Stefaniu Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72995

#### **Abstract**

Molecular docking studies have been carried out for a better understanding of the drugreceptor interactions. All the synthesized compounds have been subjected to molecular docking against targets that have been chosen based on the specific mechanism of action of the quinolones used in the antibacterial activity screening. A study of the characteristics and molecular properties of the small molecule known as ligand has been realized. In the first stage of the study, the 2D and 3D structures have been generated. The most stable conformer for each structure was obtained by geometry optimization and energy minimization. A series of topological, conformational characteristics and QSAR properties, important to assess the flexibility and the ability of the studied conformer to bind to the protein receptor, were determined and analyzed. These properties were discussed in order to assess the flexibility and the binding ability of studied conformers to bind to the receptor protein. The docking studies have been carried out. The score and hydrogen bonds formed with the amino acids from group interaction atoms are used to predict the binding modes, the binding affinities and the orientation of the docked quinolones in the active site of the protein receptor.

**Keywords:** molecular docking, antimicrobial activity, fluoroquinolones, quinolones

## **1. Introduction**

An important parameter in the development of a new drug is the drug's affinity to the identified target (protein/enzyme). Predicting the ligand binding to the target (protein/enzyme) by molecular simulation would allow the synthesis to be restricted to the most promising compounds [1–9]. Molecular docking can be accomplished by two interdependent steps [7–9]. The first step consists in sampling the ligand conformations in the active site of the protein receptor. The second step is to classify these conformations by a scoring function. The sampling algorithms

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

ligand) and their target receptor protein to form a stable complex. Molecular docking studies have been performed on topoisomerase II DNA gyrase with 32 quinolone compounds to understand the binding affinity of all quinolones with DNA gyrase. The crystal structure of topoisomerase II was downloaded from Protein Data Bank (PDB ID: 2XCT) [17]. The quinolone compounds have been synthesized in our laboratory [16], and their structures are shown in **Figure 1** and **Table 1**.

The ligands have been prepared using SPARTAN'14 software package [18]. In this study, the

vibrational frequencies and energies of optimized structures (**Figure 2**). In order to perform structure–activity relationship (SAR) studies, some electronic properties (**Table 2**) such as highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy values, HOMO and LUMO orbital coefficient distribution, molecular dipole moment, polar surface area (PSA), the ovality, polarizability, the octanol water partition coefficient (logP), the number of hydrogen-bond donors (HBDs) and ad acceptors (HBAs) and acceptor sites (HBAs) and positive and negative ionizable sites are derived from CFD assignments. HBA/HBD and ±Centers, Hydrophobe Centers including aromatic centers, can be viewed in **Figure 2**, for the quinolones FPQ 28 and 6ClPQ 28 (compounds that showed good activity against MRSA [19]). The polarizability is useful to predict the interactions between nonpolar atoms or groups and other electrically charged species, such as ions and polar molecules having a strong dipole moment.

Molecular polar surface area (PSA) [20] is a descriptor that has been shown to correlate well with passive molecular transport through membranes and therefore allows the prediction of transport properties of the drugs. Log P is estimated according to the method of Ghose, Pritchett and Crippen [21]. A number of important graphical quantities resulted from quantum chemical calculations were displayed, manipulated and interrogated. Another indicator of electrophilic addition local map is provided by the ionization potential, an overlapping of the energy of electron removal (ionization) on the electron density. In addition, the *electrostatic potential map*, an overlay of the electrostatic potential (the attraction or repulsion of a positive charge for a molecule) on the electron density, is valuable for describing the overall distribution of molecular charge, as well as to predict the sites of electrophilic addition. Another indicator of the electrophilic addition is supplied by the *local ionization potential map*, an overlapping of the energy of electron removal (ionization) on the electron density. In the end, an indicator of nucleophilic addition is offered by the *|LUMO| map*, an overlap of the absolute

The molecular orbital analysis of the Frontier molecular orbitals (FMOs) plays an essential role in the chemical stability of a molecule and in the interactions between atoms. They provide information that can be used to predict the characteristics of molecules such as optical properties and biological activities. Between them, the most important are the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). The

level of basis set has been used for the computation of molecular structure,

Docking Studies on Novel Analogues of 8-Chloro-Quinolones against *Staphylococcus aureus*

http://dx.doi.org/10.5772/intechopen.72995

79

**2.1. Ligand preparation**

*2.1.1. Molecular polar surface area (PSA)*

*2.1.2. Frontier molecular orbital analysis*

value of the lowest unoccupied molecular orbital (LUMO).

DFT/B3LYP/6–31G\*

**Figure 1.** The structure of the quinolone compounds.

should be able to reproduce experimental binding mode. Various algorithms used for docking analysis are molecular dynamics, Monte Carlo methods, genetic algorithms, fragment-based methods, point complementary methods and distance geometry methods, systematic searches. The scoring function should classify the highest among all the generated conformations. These mathematical models are used to predict the strength of binding affinity called noncovalent interaction between two molecules after they have been docked. They have also developed scoring function to predict the strength of other types of intermolecular interactions, for example, between two proteins or between proteins and DNA or protein and drug. These configurations are evaluated using the scoring functions to distinguish experimental binding modes of all other ways explored by the search algorithm. The goal of molecular docking is to predict the ligand-receptor complex structure by computation method to identify new active molecules that bind to a biological target [10–14]. The main methods used for docking are Lock and Key/ Rigid Docking and Induced Fit/Flexible Docking. In rigid docking, the internal geometry of the receptor and ligand is kept fixed and docking is performed. In flexible docking, enumeration on the rotations of one of the molecules (usually smaller one) is performed. Every rotation, the surface cell occupancy and energy are calculated; later, the most optimum pose is selected.

This chapter presents design and molecular docking studies about 8-chloro-quinolone compounds. The influence of the presence of chlorine atom in the eighth position of the quinolone ring (**Figure 1**, where R8 = Cl) on the antimicrobial activity against *Staphylococcus aureus* has been studied. The predicted activity has been correlated with the experimental activity who has been determined by agar dilution method [15, 16].

Drugs belonging to the quinolone compound are characterized by a quicker biological activity and a larger antibacterial spectrum. They are active on both gram-positive and gram-negative bacteria, as well as on recently discovered bacteria with intercellular development (Legionella, Mycoplasma, etc.), or even on acid-resistant bacteria (*M. tuberculosis* and *M. leprae*). The area of use of quinolones has expanded from urinary infections to systemic acute and chronic infections (lung and bronchus infections, osteitis, septicemia and endocarditis, chronic infections [chronic bronchitis, purulent osteoarthritis, chronic prostatite, cystitis and chronic sinusitis]) [15, 16].

### **2. Materials and methods**

Molecular docking studies have been performed with CLC Drug Discovery Workbench Software in order to achieve accurate predictions on optimized conformation for both the quinolone (as ligand) and their target receptor protein to form a stable complex. Molecular docking studies have been performed on topoisomerase II DNA gyrase with 32 quinolone compounds to understand the binding affinity of all quinolones with DNA gyrase. The crystal structure of topoisomerase II was downloaded from Protein Data Bank (PDB ID: 2XCT) [17]. The quinolone compounds have been synthesized in our laboratory [16], and their structures are shown in **Figure 1** and **Table 1**.

#### **2.1. Ligand preparation**

should be able to reproduce experimental binding mode. Various algorithms used for docking analysis are molecular dynamics, Monte Carlo methods, genetic algorithms, fragment-based methods, point complementary methods and distance geometry methods, systematic searches. The scoring function should classify the highest among all the generated conformations. These mathematical models are used to predict the strength of binding affinity called noncovalent interaction between two molecules after they have been docked. They have also developed scoring function to predict the strength of other types of intermolecular interactions, for example, between two proteins or between proteins and DNA or protein and drug. These configurations are evaluated using the scoring functions to distinguish experimental binding modes of all other ways explored by the search algorithm. The goal of molecular docking is to predict the ligand-receptor complex structure by computation method to identify new active molecules that bind to a biological target [10–14]. The main methods used for docking are Lock and Key/ Rigid Docking and Induced Fit/Flexible Docking. In rigid docking, the internal geometry of the receptor and ligand is kept fixed and docking is performed. In flexible docking, enumeration on the rotations of one of the molecules (usually smaller one) is performed. Every rotation, the surface cell occupancy and energy are calculated; later, the most optimum pose is selected.

This chapter presents design and molecular docking studies about 8-chloro-quinolone compounds. The influence of the presence of chlorine atom in the eighth position of the quinolone ring (**Figure 1**, where R8 = Cl) on the antimicrobial activity against *Staphylococcus aureus* has been studied. The predicted activity has been correlated with the experimental activity who

Drugs belonging to the quinolone compound are characterized by a quicker biological activity and a larger antibacterial spectrum. They are active on both gram-positive and gram-negative bacteria, as well as on recently discovered bacteria with intercellular development (Legionella, Mycoplasma, etc.), or even on acid-resistant bacteria (*M. tuberculosis* and *M. leprae*). The area of use of quinolones has expanded from urinary infections to systemic acute and chronic infections (lung and bronchus infections, osteitis, septicemia and endocarditis, chronic infections [chronic bronchitis, purulent osteoarthritis, chronic prostatite, cystitis and chronic sinusitis]) [15, 16].

Molecular docking studies have been performed with CLC Drug Discovery Workbench Software in order to achieve accurate predictions on optimized conformation for both the quinolone (as

has been determined by agar dilution method [15, 16].

**Figure 1.** The structure of the quinolone compounds.

78 Molecular Docking

**2. Materials and methods**

The ligands have been prepared using SPARTAN'14 software package [18]. In this study, the DFT/B3LYP/6–31G\* level of basis set has been used for the computation of molecular structure, vibrational frequencies and energies of optimized structures (**Figure 2**). In order to perform structure–activity relationship (SAR) studies, some electronic properties (**Table 2**) such as highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy values, HOMO and LUMO orbital coefficient distribution, molecular dipole moment, polar surface area (PSA), the ovality, polarizability, the octanol water partition coefficient (logP), the number of hydrogen-bond donors (HBDs) and ad acceptors (HBAs) and acceptor sites (HBAs) and positive and negative ionizable sites are derived from CFD assignments. HBA/HBD and ±Centers, Hydrophobe Centers including aromatic centers, can be viewed in **Figure 2**, for the quinolones FPQ 28 and 6ClPQ 28 (compounds that showed good activity against MRSA [19]). The polarizability is useful to predict the interactions between nonpolar atoms or groups and other electrically charged species, such as ions and polar molecules having a strong dipole moment.

#### *2.1.1. Molecular polar surface area (PSA)*

Molecular polar surface area (PSA) [20] is a descriptor that has been shown to correlate well with passive molecular transport through membranes and therefore allows the prediction of transport properties of the drugs. Log P is estimated according to the method of Ghose, Pritchett and Crippen [21]. A number of important graphical quantities resulted from quantum chemical calculations were displayed, manipulated and interrogated. Another indicator of electrophilic addition local map is provided by the ionization potential, an overlapping of the energy of electron removal (ionization) on the electron density. In addition, the *electrostatic potential map*, an overlay of the electrostatic potential (the attraction or repulsion of a positive charge for a molecule) on the electron density, is valuable for describing the overall distribution of molecular charge, as well as to predict the sites of electrophilic addition. Another indicator of the electrophilic addition is supplied by the *local ionization potential map*, an overlapping of the energy of electron removal (ionization) on the electron density. In the end, an indicator of nucleophilic addition is offered by the *|LUMO| map*, an overlap of the absolute value of the lowest unoccupied molecular orbital (LUMO).

#### *2.1.2. Frontier molecular orbital analysis*

The molecular orbital analysis of the Frontier molecular orbitals (FMOs) plays an essential role in the chemical stability of a molecule and in the interactions between atoms. They provide information that can be used to predict the characteristics of molecules such as optical properties and biological activities. Between them, the most important are the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). The


HOMO represents the ability of a molecule to donate an electron, while the LUMO represents the ability to accept an electron [22, 23]. The HOMO and LUMO, calculated at the B3LYP/6- 31G\* level, can be seen in **Figure 3** for the gas phase, for the quinolones FPQ 28 and 6ClPQ 28 (compounds that showed good activity against MRSA [19]). The graphic has 'blue and red'

**Compounds R6 R7 R8**

Docking Studies on Novel Analogues of 8-Chloro-Quinolones against *Staphylococcus aureus*

Cl 3-Methyl-piperazinyl Cl

81

http://dx.doi.org/10.5772/intechopen.72995

Cl Pyrrolidinyl H

Cl Pyrrolidinyl Cl

Cl Piperidinyl H

Cl Piperidinyl Cl

Cl 4-Methyl-piperidinyl H

Cl 4-Methyl-piperidinyl Cl

Cl 3-Methyl-piperidinyl H

Cl 3-Methyl-piperidinyl Cl

Cl Morfolinyl H

Cl Morfolinyl Cl

**6ClPQ29**:1-Ethyl-6,8-dichloro-7-(3-methyl-piperazin-1-yl)-1,4-dihydro-4-oxo-

**6ClPQ35**:1-Ethyl-6-chloro-7-(pyrrolidin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**6ClPQ32**:1-Ethyl-6-chloro-7-(piperidin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**6ClPQ33**:1-Ethyl-6,8-dichloro-7-(piperidin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**Q80**:1-Ethyl-6-chloro-7-(4-methyl-piperidin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**Q87**:1-Ethyl-6,8-dichloro-7-(4-methyl-piperidin-1-yl)-1,4-dihydro-4-oxo-quinoline-

**6ClPQ24**:1-Ethyl-6-chloro-7-(3-methyl-piperidin-1-yl)-1,4-dihydro-4-oxo-

**6ClPQ30**:1-Ethyl-6,8-dichloro-7-(3-methyl-piperidin-1-yl)-1,4-dihydro-4-oxo-

**6ClPQ25**:1-Ethyl-6-chloro-7-(morpholin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**Table 1.** The structure of the quinolone compounds.

**6ClPQ28**:1-Ethyl-6,8-dichloro-7-(morpholin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**6ClPQ36**:1-Ethyl-6,8-dichloro-7-(pyrrolidin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

quinoline-3-carboxylic acid

carboxylic acid

carboxylic acid

carboxylic acid

carboxylic acid

carboxylic acid

3-carboxylic acid

carboxylic acid

carboxylic acid

quinoline-3-carboxylic acid

quinoline-3-carboxylic acid

For the HOMO of 7-piperazinyl-8-unsubstituted-quinolones, electron density of NF, PF and FPQ27 is localized on piperazine heterocyclic, on aromatic ring and on 4-oxo group. For the HOMO of 7-piperazinyl-8-chloro-quinolones, electron density of FPQ 50 and FPQ 51 is localized on piperazine heterocyclic; for FPQ29 compound, electron density is localized on piperazine heterocyclic and C6, C8 and C10 atoms from aromatic ring. For the HOMO of 7-piperidinyl-8-unsubstituted-quinolones, electron density of Q 83, FPQ 24 and FPQ 32is localized on piperidine heterocyclic, and on C6, C7 and C8 atoms from aromatic ring. For the HOMO of 7-piperidinyl-8-chloro-quinolones, electron density of Q 85, FPQ 30 and FPQ 33is localized on piperidine heterocyclic, on C6, C7 and C8 atoms from aromatic ring and on chlorine atom. For the HOMO of 7-morpholinyl-8-unsubstituted-quinolone, FPQ 25 electron density is localized on morpholine heterocyclic, on aromatic ring and on 4-oxo group. For the HOMO of 7-morpholinyl-8-chloro-quinolone, FPQ 28 electron density is localized on

regions. These correspond to positive and negative values of the orbital.


**Table 1.** The structure of the quinolone compounds.

**Compounds R6 R7 R8**

F Piperazinyl H

F Piperazinyl Cl

F 4-Methyl-piperazinyl H

F 4-Methyl-piperazinyl Cl

F 3-Methyl-piperazinyl H

F 3-Methyl-piperazinyl Cl

F Pyrrolidinyl H

F Pyrrolidinyl Cl

F Piperidinyl H

F Piperidinyl Cl

F 4-Methyl-piperidinyl H

F 4-Methyl-piperidinyl Cl

F 3-Methyl-piperidinyl H

F 3-Methyl-piperidinyl Cl

F Morfolinyl H

F Morfolinyl Cl

Cl Piperazinyl H

Cl Piperazinyl Cl

Cl 4-Methyl-piperazinyl H

Cl 4-Methyl-piperazinyl Cl

Cl 3-Methyl-piperazinyl H

**NF:**1-Ethyl-6-fluoro-7-(piperazin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-carboxylic

**FPQ50:**1-Ethyl-6-fluoro-7-(piperazin-1-yl)-8-chloro-1,4-dihydro-4-oxo-quinoline-3-

**PF:**1-Ethyl-6-fluoro-7-(4-methyl-piperazin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**FPQ51:**1-Ethyl-6-fluoro-7-(4-methyl-piperazin-1-yl)-8-chloro-1,4-dihydro −4-oxo-

**FPQ27:**1-Ethyl-6-fluoro-7-(3-methyl-piperazin-1-yl)-1,4-dihydro-4-oxo-quinoline-

**FPQ29.HCl:**1-Ethyl-6-fluoro-7-(3-methyl-piperazin-1-yl)-8-chloro-1,4-dihydro-4-

**FPQ36:**1-Ethyl-6-fluoro-7-(pyrrolidin-1-yl)-8-chloro-1,4-dihydro-4-oxo-quinoline-

**FPQ33:**1-Ethyl-6-fluoro-7-(piperidin-1-yl)-8-chloro-1,4-dihydro-4-oxo-quinoline-

**Q83:**1-Ethyl-6-fluoro-7-(4-methyl-piperidin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**FPQ24:**1-Ethyl-6-fluoro-7-(3-methyl-piperidin-1-yl)-1,4-dihydro-4-oxo-quinoline-

**FPQ30:**1-Ethyl-6-fluoro-7-(3-methyl-piperidin-1-yl)-8-chloro-1,4-dihydro-4-oxo-

**FPQ28:**1-Ethyl-6-fluoro-7-(morpholin-1-yl)-8-chloro-1,4-dihydro-4-oxo-quinoline-

**6ClPQ50**:1-Ethyl-6,8-dichloro-7-(piperazin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**PClX**:1-Ethyl-6-chloro-7-(4-methyl-piperazin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**6ClPQ51**:1-Ethyl-6,8-dichloro-7-(4-methyl-piperazin-1-yl)-1,4-dihydro −4-oxo-

**6ClPQ27**:1-Ethyl-6-chloro7-(3-methyl-piperazin-1-yl)-1,4-dihydro-4-oxo-

**FPQ25:**1-Ethyl-6-fluoro-7-(morpholin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**NClX**:1-Ethyl-6-chloro-7-(piperazin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

**Q85:**1-Ethyl-6-fluoro-7-(4-methyl-piperidin-1-yl)-8-chloro-1,4-dihydro-4-oxo-

**FPQ35:**1-Ethyl-6-fluoro-7-(pyrrolidin-1-yl)-1,4-dihydro-4-oxo-quinoline

**FPQ32:**1-Ethyl-6-fluoro-7-(piperidin-1-yl)-1,4-dihydro-4-oxo-quinoline-3-

acid

carboxylic acid [20]

quinoline-3-carboxylic acid

oxo-quinoline-3-carboxylic acid . hydrochloride

carboxylic acid

80 Molecular Docking

3-carboxylic acid

−3-carboxylic acid

3-carboxylic acid

carboxylic acid

3-carboxylic acid

carboxylic acid

3-carboxylic acid

carboxylic acid

3-carboxylic acid

carboxylic acid

carboxylic acid

carboxylic acid

quinoline-3-carboxylic acid

quinoline-3-carboxylic acid

quinoline-3-carboxylic acid

quinoline-3-carboxylic acid

HOMO represents the ability of a molecule to donate an electron, while the LUMO represents the ability to accept an electron [22, 23]. The HOMO and LUMO, calculated at the B3LYP/6- 31G\* level, can be seen in **Figure 3** for the gas phase, for the quinolones FPQ 28 and 6ClPQ 28 (compounds that showed good activity against MRSA [19]). The graphic has 'blue and red' regions. These correspond to positive and negative values of the orbital.

For the HOMO of 7-piperazinyl-8-unsubstituted-quinolones, electron density of NF, PF and FPQ27 is localized on piperazine heterocyclic, on aromatic ring and on 4-oxo group. For the HOMO of 7-piperazinyl-8-chloro-quinolones, electron density of FPQ 50 and FPQ 51 is localized on piperazine heterocyclic; for FPQ29 compound, electron density is localized on piperazine heterocyclic and C6, C8 and C10 atoms from aromatic ring. For the HOMO of 7-piperidinyl-8-unsubstituted-quinolones, electron density of Q 83, FPQ 24 and FPQ 32is localized on piperidine heterocyclic, and on C6, C7 and C8 atoms from aromatic ring. For the HOMO of 7-piperidinyl-8-chloro-quinolones, electron density of Q 85, FPQ 30 and FPQ 33is localized on piperidine heterocyclic, on C6, C7 and C8 atoms from aromatic ring and on chlorine atom. For the HOMO of 7-morpholinyl-8-unsubstituted-quinolone, FPQ 25 electron density is localized on morpholine heterocyclic, on aromatic ring and on 4-oxo group. For the HOMO of 7-morpholinyl-8-chloro-quinolone, FPQ 28 electron density is localized on

**Figure 2.** Optimized geometry of quinolone compounds.

morpholine heterocyclic, on aromatic ring, on 4-oxo group and on chlorine atom. For the HOMO of 7-pyrrolidinyl-8-unsubstituted-quinolone, FPQ 35 electron density is localized on pyrrolidine heterocyclic, on aromatic ring and on 4-oxo group. For the HOMO of 7-pyrrolidinyl-8-chloro-quinolone, FPQ 36 electron density is localized on pyrrolidine heterocyclic, on aromatic ring, on 4-oxo group and on chlorine atom. For the LUMO of 7-substituted-8-unsubstituted-quinolones, NF, PF, FPQ27, O 83, FPQ 24, FPQ 32, electron density of FPQ 25 and FPQ 35 is localized on 4-piridinona ring and on aromatic ring. For the LUMO of 7-substituted-8-chloro-quinolones, electron density of FPQ 50, FPQ 51, FPQ29, O 85, FPQ 30, FPQ 33, FPQ 28 and FPQ 36 is localized on 4-piridinona ring, on aromatic ring B and on chlorine atom. For the 6-cloroqinolones, the electron density is located in the same manner

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83

**Figure 3.** HBA/HBD and ±Centers, Hydrophobe centers of 8-chloro-quinolone compounds: (a) FQ28 (b) 6ClPQ28.

The *frontier orbital gap* helps to characterize chemical reactivity of the molecule (**Table 2**). HOMO and LUMOs determine the way in which it interacts with other species. The introduction of the electron-withdrawing substituent (chlorine) at position C 8 in quinolone compounds decreases the HOMO-LUMO gap as compared to their corresponding 8-unsub-

Molecular electrostatic potential (MEP) has been evaluated using B3LYP method with the basis set 6-31G\* to investigate the chemical reactivity of a molecule. The MEP is especially important for the identification of the reactive sites of nucleophilic or electrophilic attack in hydrogen-bonding interactions and for the understanding of the process of biological recognition [21, 22]. An electrostatic potential map for quinolone compounds shows hydrophilic regions in red (negative potential) and blue (positive potential) and hydrophobic regions in

The **local ionization potential map** provides another indicator of electrophilic addition; the local ionization map is an overlay of the energy of electron removal (ionization) on the

green. In **Figure 5** can be viewed the MEP of the quinolones FPQ28 and 6ClPQ28.

as the corresponding fluoroquinolones.

stituted quinolone compounds (**Figure 4**).

*2.1.3. Molecular electrostatic potential (MEP)*

Docking Studies on Novel Analogues of 8-Chloro-Quinolones against *Staphylococcus aureus* http://dx.doi.org/10.5772/intechopen.72995 83

**Figure 3.** HBA/HBD and ±Centers, Hydrophobe centers of 8-chloro-quinolone compounds: (a) FQ28 (b) 6ClPQ28.

morpholine heterocyclic, on aromatic ring, on 4-oxo group and on chlorine atom. For the HOMO of 7-pyrrolidinyl-8-unsubstituted-quinolone, FPQ 35 electron density is localized on pyrrolidine heterocyclic, on aromatic ring and on 4-oxo group. For the HOMO of 7-pyrrolidinyl-8-chloro-quinolone, FPQ 36 electron density is localized on pyrrolidine heterocyclic, on aromatic ring, on 4-oxo group and on chlorine atom. For the LUMO of 7-substituted-8-unsubstituted-quinolones, NF, PF, FPQ27, O 83, FPQ 24, FPQ 32, electron density of FPQ 25 and FPQ 35 is localized on 4-piridinona ring and on aromatic ring. For the LUMO of 7-substituted-8-chloro-quinolones, electron density of FPQ 50, FPQ 51, FPQ29, O 85, FPQ 30, FPQ 33, FPQ 28 and FPQ 36 is localized on 4-piridinona ring, on aromatic ring B and on chlorine atom. For the 6-cloroqinolones, the electron density is located in the same manner as the corresponding fluoroquinolones.

The *frontier orbital gap* helps to characterize chemical reactivity of the molecule (**Table 2**). HOMO and LUMOs determine the way in which it interacts with other species. The introduction of the electron-withdrawing substituent (chlorine) at position C 8 in quinolone compounds decreases the HOMO-LUMO gap as compared to their corresponding 8-unsubstituted quinolone compounds (**Figure 4**).

#### *2.1.3. Molecular electrostatic potential (MEP)*

**Figure 2.** Optimized geometry of quinolone compounds.

82 Molecular Docking

Molecular electrostatic potential (MEP) has been evaluated using B3LYP method with the basis set 6-31G\* to investigate the chemical reactivity of a molecule. The MEP is especially important for the identification of the reactive sites of nucleophilic or electrophilic attack in hydrogen-bonding interactions and for the understanding of the process of biological recognition [21, 22]. An electrostatic potential map for quinolone compounds shows hydrophilic regions in red (negative potential) and blue (positive potential) and hydrophobic regions in green. In **Figure 5** can be viewed the MEP of the quinolones FPQ28 and 6ClPQ28.

The **local ionization potential map** provides another indicator of electrophilic addition; the local ionization map is an overlay of the energy of electron removal (ionization) on the


electron density (**Figure 6**). **|LUMO| map**, map that represents a superposition of the absolute value of the lowest unoccupied molecular orbital (the LUMO) on the electron density,

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**Figure 5.** The optimized geometry and electrostatic potential pattern of the surface of (a) FPQ 28 and (b) 6ClPQ 28 (red—

provides another indicator of the nucleophilic addition (**Figure 7**).

negative, high electron density, blue—positive area, low electron density).

**Figure 4.** HOMO, LUMO surfaces of 8-chloro-quinolone compounds: (a) FQ28 (b) 6ClPQ28.

**Table 2.** Molecular properties for CPK model computations for quinolone compounds using Spartan'14 V1.1.4 software.

Docking Studies on Novel Analogues of 8-Chloro-Quinolones against *Staphylococcus aureus* http://dx.doi.org/10.5772/intechopen.72995 85

**Figure 4.** HOMO, LUMO surfaces of 8-chloro-quinolone compounds: (a) FQ28 (b) 6ClPQ28.

**Compounds Molecular properties Dipole moment (debye)**

84 Molecular Docking

**E HOMO (eV)**

**E LUMO (eV)**

**HOMO-LUMO GAP**

NF 12.76 −5.76 −1.41 4.35 65.09 56.587 1.45 1.37 5 1 FPQ50 8.71 −6.00 −2.02 3.98 66.33 57.344 1.46 1.92 5 1 PF 12.36 −5.77 −1.43 4.34 66.65 46.369 1.48 1.74 5 1 FPQ51 8.91 −5.79 −1.97 3.82 67.92 46.808 1.49 2.30 5 1 FPQ27 12.86 −5.76 −1.40 4.36 66.57 56.053 1.48 1.68 5 1 FPQ29 9.10 −6.01 −1.96 4.05 67.80 56.717 1.49 2.24 5 1 FPQ35 12.50 −5.77 −1.39 4.38 64.18 44.034 1.43 2.30 4 1 FPQ36 8.83 −6.14 −1.97 4.17 65.44 44.405 1.45 2.86 4 1 FPQ32 9.49 −6.63 −1.82 −4.81 65.58 45.402 1.46 2.72 4 1 FPQ33 8.28 −6.33 −2.05 4.28 66.75 44.781 1.47 3.28 4 1 Q83 9.49 −6.36 −1.82 4.54 67.06 45.389 1.49 3.05 4 1 Q85 8.29 −6.33 −2.05 4.58 68.23 44.785 1.50 3.61 4 1 FPQ24 9.48 −6.34 −1.82 4.52 67.07 45.295 1.48 3.12 4 1 FPQ30 8.23 −6.33 −2.06 4.27 68.24 44.768 1.50 3.68 4 1 FPQ25 10.15 −6.02 −1.58 4.44 64.87 51.758 1.44 1.59 5 1 FPQ28 8.26 −6.24 −1.97 4.97 66.00 51.859 1.45 2.15 5 1 NClX 8.80 −6.08 −1.93 4.15 65.98 57.537 1.47 1.77 5 1 6ClPQ50 7.81 −6.06 −2.11 3.95 67.11 56.756 1.48 2.32 5 1 PClX 11.84 −5.84 −1.59 4.25 67.42 46.688 1.49 2.14 5 1 6ClPQ51 8.69 −5.77 −2.07 3.07 68.72 46.277 1.51 2.70 5 1 6ClPQ27 8.56 −6.13 −1.93 4.20 67.46 57.339 1.59 2.08 5 1 6ClPQ29 8.00 −6.04 −2.10 3.94 68.60 56.469 1.51 2.64 5 1 6ClPQ35 12.16 −5.92 −1.54 4.38 64.92 44.303 1.44 2.70 4 1 6ClPQ36 8.51 −6.05 −2.09 3.96 66.27 43.934 1.47 3.26 4 1 6ClPQ32 9.48 −6.25 −1.89 4.36 66.39 44.937 1.47 3.12 4 1 6ClPQ33 8.26 −6.19 −2.11 4.08 67.57 44.194 1.49 3.68 4 1 Q80 9.47 −6.26 −1.89 5.07 67.86 44.979 1.50 3.45 4 1 Q87 8.26 −6.19 −2.12 4.07 69.04 44.205 1.51 4.01 4 1 6ClPQ24 9.47 −6.24 −1.89 4.35 67.88 44.863 1.50 3.52 4 1 6ClPQ30 8.27 −6.19 −2.12 4.07 69.05 44.304 1.51 4.08 4 1 6ClPQ25 7.85 −6.26 −1.97 4.29 65.69 52.427 1.46 1.99 5 1 6ClPQ28 6.68 −6.20 −2.20 −4.00 66.86 51.596 1.48 2.55 5 1

**Table 2.** Molecular properties for CPK model computations for quinolone compounds using Spartan'14 V1.1.4 software.

**Polarizability (10−30 m3 )**

**PSA(Å2**

**) Ovality Log P HBA** 

**count**

**HBD count**

> electron density (**Figure 6**). **|LUMO| map**, map that represents a superposition of the absolute value of the lowest unoccupied molecular orbital (the LUMO) on the electron density, provides another indicator of the nucleophilic addition (**Figure 7**).

**Figure 5.** The optimized geometry and electrostatic potential pattern of the surface of (a) FPQ 28 and (b) 6ClPQ 28 (red negative, high electron density, blue—positive area, low electron density).

**Figure 6.** The optimized geometry and local ionization potential map of (a) FPQ 28 and (b) 6ClPQ 28.

**Figure 7.** The optimized geometry and ILUMOI map of (a) FPQ 28 and (b) 6ClPQ 28.

#### **2.2. Molecular docking**

The steps to go through to explore protein-ligand interaction using docking are as follows: set up the binding site in a Molecule Project, import the dock ligands to a Molecule Table and inspect the docking results. The docking studies have been carried out using CLC Drug Discovery Workbench Software. The score and hydrogen bonds formed with the amino acids from group interaction atoms are used to predict the binding modes, the binding affinities and the orientation of the docked quinolone compounds (**Figure 8a**–**c**, **e**, **f**, **h**) in the active site of the protein receptor (**Table 3**). The docking score used in the Drug Discovery Workbench is the PLANTSPLP score [24]. The protein-ligand complex has been realized based on the X-ray structure of *S. aureus* DNA GYRASE, who was downloaded from the Protein Data Bank (PDB ID: 2XCT) [17].

#### *2.2.1. Docking method validation*

It ensures that the ligand orientations and position obtained from the molecular docking studies are valid and reasonable potential binding modes of ligands; the docking methods and parameters used have been validated by redocking (**Figure 8d**, **f**).

*2.2.2. Determining molecular properties*

Using the "Calculate Molecular Properties" tool it have been calculated important molecular properties such as logP, number of hydrogen bond donors, number of hydrogen bond acceptors

**Figure 8.** Molecular docking studies with 2XCT receptor. (a) Docking pose of the co-crystallized ligand CP. (b) Docking pose of the co-crystallized ligand CP interacting with residues in the binding site. (c) Docking pose of FPQ 28. (d) Docking validation of FPQ 28. (e) Docking pose of the FPQ 28 interacting with residues in the binding site. (f) Docking pose of Q

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83. (g) Docking validation of Q 83. (h) Docking pose of the Q 83 interacting with residues in the binding site.

Docking Studies on Novel Analogues of 8-Chloro-Quinolones against *Staphylococcus aureus* http://dx.doi.org/10.5772/intechopen.72995 87

**2.2. Molecular docking**

86 Molecular Docking

ID: 2XCT) [17].

*2.2.1. Docking method validation*

The steps to go through to explore protein-ligand interaction using docking are as follows: set up the binding site in a Molecule Project, import the dock ligands to a Molecule Table and inspect the docking results. The docking studies have been carried out using CLC Drug Discovery Workbench Software. The score and hydrogen bonds formed with the amino acids from group interaction atoms are used to predict the binding modes, the binding affinities and the orientation of the docked quinolone compounds (**Figure 8a**–**c**, **e**, **f**, **h**) in the active site of the protein receptor (**Table 3**). The docking score used in the Drug Discovery Workbench is the PLANTSPLP score [24]. The protein-ligand complex has been realized based on the X-ray structure of *S. aureus* DNA GYRASE, who was downloaded from the Protein Data Bank (PDB

It ensures that the ligand orientations and position obtained from the molecular docking studies are valid and reasonable potential binding modes of ligands; the docking methods and

parameters used have been validated by redocking (**Figure 8d**, **f**).

**Figure 7.** The optimized geometry and ILUMOI map of (a) FPQ 28 and (b) 6ClPQ 28.

**Figure 6.** The optimized geometry and local ionization potential map of (a) FPQ 28 and (b) 6ClPQ 28.

**Figure 8.** Molecular docking studies with 2XCT receptor. (a) Docking pose of the co-crystallized ligand CP. (b) Docking pose of the co-crystallized ligand CP interacting with residues in the binding site. (c) Docking pose of FPQ 28. (d) Docking validation of FPQ 28. (e) Docking pose of the FPQ 28 interacting with residues in the binding site. (f) Docking pose of Q 83. (g) Docking validation of Q 83. (h) Docking pose of the Q 83 interacting with residues in the binding site.

#### *2.2.2. Determining molecular properties*

Using the "Calculate Molecular Properties" tool it have been calculated important molecular properties such as logP, number of hydrogen bond donors, number of hydrogen bond acceptors


**Ligand Score/**

6ClPQ27 −35.72/

FPQ 27 −37.06/

6ClPQ29 −32.01/

FPQ 29 −39.67/

6ClPQ25 −35.08/

FPQ25 −39.55/

6ClPQ28 −35.65/

FPQ28 −39.63/

6ClPQ35 −34.10/

FPQ35 −39.13/

**RMSD (Å)**

–O sp3

–O sp2

–O sp2

–O sp3

–O sp2

–O sp3

–O sp3

–O sp2

–O sp3

–O sp3

–O sp2

–O sp3

–O sp3

–O sp2

–O sp2

–O sp2

–O sp3

–O sp2

–O sp2

–O sp2

–O sp3

0.02

1.50

0.16

0.21

0.32

0.04

0.22

0.17

0.02

0.18

**Group interaction/hydrogen bond Bond** 

Docking Studies on Novel Analogues of 8-Chloro-Quinolones against *Staphylococcus aureus*

*SER438, ASP437, GLY436, GLU435, SP508, LEU457, ASP510, ILE516,* 

from SER 438

from ASP 437

from SER 438

from ASP510

from HIS 1081

from ASP 437

from ASP 437

from HIS 1081

from ASP510

*LYS460, ARG458, GLY459, ILE516, GLU435, ASP512,ASP510, PRO1080,* 

*ILE516, LYS460, GLY513, ASP512, GLY459, ARG458, GLU435, ASP510,* 

*LYS460, ARG458, GLY459, ILE516, GLU435, ASP508,ASP512, ASP510,* 

from SER 438

*ARG1033, PRO1080, HIS1081, GLY1082, SER1085*

from HIS 1081

from ASP 510

from ASP 437

from SER 438

from HIS 1081

from ASP 510

from SER 438

from SER 438

from ASP 437

*GLY582, ASP508, GLY584, LEU583, ALA439,SER438, ASP437,GLY436, GLU435, ASP510, ASP510, ASP512,LEU457, ARG458, GLY459, LYS460*

*SER438, ASP437, ALA439, GLY584, GLY436, GLU435, LEU457, ARG458,* 

from LYS 460 2.978

from ASP 508 2.642

*SER185, ARG1033, GLY1082, HIS1081, PRO1080, LYS460, GLY459, ASP512,* 

*ASP508, GLU435, ASP510, ASP512, ILE516, LYS460,ARG458, ARG1033,* 

*ASP512, LYS460, GLY459, ARG458*

from COOH(OH)–O sp2

from COOH(CO)–O sp3

from COOH(OH)–O sp2

from COOH(CO)–N sp2

from COOH(OH)–O sp2

from COOH(OH)–O sp2

from COOH(CO)–N sp2

from COOH(OH)–O sp2

from COOH(OH)–Nsp3

*HIS1081, GLY1082, SER1084, SER1085*

from COOH(CO)–N sp2

from COOH(OH)–O sp2

from COOH(OH)–O sp2

from COOH(CO)–O sp3

from COOH(CO)–N sp2

from COOH(OH)–O sp2

from COOH(CO)–O sp3

from COOH(CO)–N sp2

from COOH(CO)–N sp2

from COOH(OH)–O sp2

*GLY459, LYS460, ASP512, ILE516*

from CO–O sp3

*ASP508,GLY436, ASP437, SER438, ALA439*

*ASP437, ARG58, GLU477, LYS460, GLY459*

*ARG458, ILE516, ASP508, GLU435,ARG458*

*GLU477, ARG458, LYS460, GLY459, GLU435, ASP512*

*GLY459, PRO1080, HIS1081, GLY1082, SER1085*

from CO–O sp3

**length (Å)**

89

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2.854 2.746 3.073

3.081 2.726

2.714 3.389

2.768 2.804

2.905 2.632

2.968 2.641 2.915

2.863 2.671

3.174 3.017 2.995


**Ligand Score/**

88 Molecular Docking

CP −37.27/

ClCP −36.63/

NClX −34.82/

NF −39.79/

6ClPQ50 −33.63/

FPQ 50 −38.33/

PClX −36.00/

PF −39.89/

6ClPQ51 −34.98/

FPQ 51 −36.50/

**RMSD (Å)**

*GLY436, ASP437, SER438*

from CO–O sp3

from COOH(CO)–O sp2

from COOH(OH)–O sp2

from COOH(OH)–O sp2

from COOH(CO)–N sp2

from COOH(CO)–Nsp3

from COOH(CO)–N sp2

from COOH(OH)–O sp2

from piperazine–O sp2

from COOH(OH)–O sp2

from COOH(OH)–O sp2

from piperazine–N sp3

from COOH(OH)–O sp2

from COOH(CO)–N sp2

from COOH(CO)–Nsp3

from COOH(CO)–N sp3

from COOH(OH)–O sp2

from COOH(OH)–O sp2

from COOH(CO)–N sp2

from COOH(OH)–N sp3

from COOH(CO)–N sp3

*LYS466, VAL464, ALA467, ARG471*

from CO–Nsp3

from CO–N sp3

*ASP512, ILE 516, LYS459, ILE461, ARG458, GLU477*

*ARG1033, SER1085, GLY1082, HIS1081, PRO1080*

*ASP437, ARG458, GLY459, LYS460, ILE477, LEU462*

–O sp2

–O sp2

–O sp3

*ILE461* –O sp3

–O sp2

–O sp2

–O sp2

–O sp3

–N sp3

–O sp3

–O sp3

–N sp3

–O sp3

–O sp2

–O sp2

–O sp2

–O sp2

–O sp2

–O sp3

*LYS460* –O sp3

–O sp2

–O sp3

–O sp2

0.79

0.10

0.06

0.11

0.07

0.19

0.04

0.65

0.10

0.44

**Group interaction/hydrogen bond Bond** 

*ASP510, ASP508, ASP512, GLY513, LYS460, GLY459,ARG458, GLU435,* 

from SER 438

from ASP 437

from GLU 477

from ARG 458

from HIS 1081

from ASP 510

from ASP 437

from LYS 460

from ILE 461

from LYS 460

from ASP 508

from ALA 439

from LYS 460

from LYS 460

from ASP 512

from GLU 477

from ARG 458

from LYS 460

from LYS 460

*ASP437, ASP512,GLY459, ARG458,GLU477, ASN476,ASN475, ILE461,* 

*TYR1025, ASP512, HIS515, LYS460, ILE461, LEU519, LEU462, ASN463,* 

*GLY582, GLY584, LEU583, ASP508, ASP510 ASP512,LYS460, ILE516, GLY459, ARG458, LEU457, ASP437, GLY36, GLU435, SER438, ALA439*

*ASP437, ARG458, GLU477,ILE461, LYS460, GLY459,TYR1025*

from LYS 460

*ASP512, LYS460, ILE461,GLU477 GLY459, ARG458, ARG1033*

from LYS 460

from LYS 460 3.036

*GLU477, ASP512, ASP437, ARG458,LYS460, ASN475, GLY459, ASN476,* 

*LYS460, GLY459, ARG458, ILE516, GLU435, ASP512, ASP510, ASP508,* 

from SER 438

**length (Å)**

3.065 2.816 2.872

2.933 3.125

2.765 2.802

2.840 3.149 3.818

3.195 3.036 3.027

2.809 2.919

2.732 2.934 2.948

2.821 2.929

2.888 2.722


**Ligand Score/**

6ClPQ32 −33.86/

FPQ32 41.85/

6ClPQ33 −35.28/

FPQ33 −42.53/

**RMSD (Å)**

–O sp2

–O sp2

–O sp2

–O sp3

–O sp2

–O sp2

–O sp3

**(Daltons)**

0.03

0.07

0.57

0.11

discovery workbench software.

**Compounds Atoms Weight** 

**Group interaction/hydrogen bond Bond** 

Docking Studies on Novel Analogues of 8-Chloro-Quinolones against *Staphylococcus aureus*

*SER438, ASP437, ALA439, GLY436,GLU435, LEU457, ASP510, ASP512,* 

*SER1085, ARG458, GLY459, LYS460,ILE516, GLU435, SP508, ASP512,* 

*ARG458, LYS460, GLY459, ILE516, GLU435, ASP508,ASP512, ASP510,* 

**Table 3.** The list of intermolecular interactions between the ligand molecules docked with 2XCT using CLC drug

**Lipinski violations**

**NF** 41 319.33 3 0 2 6 0.68 **FPQ50** 41 353.78 3 0 2 6 1.31 **PF** 44 333.36 3 0 1 6 1.15 **FPQ51** 44 367.80 3 0 1 6 1.77 **FPQ27** 44 333.36 3 0 2 6 1.11 **FPQ29** 44 367.80 3 0 2 6 1.74 **FPQ35** 39 304.32 3 0 1 5 3.90 **FPQ36** 39 338.76 3 0 1 5 4.53 **FPQ32** 42 318.34 3 0 1 5 4.26 **FPQ33** 42 352.79 3 0 1 5 4.89 **Q83** 45 332.37 3 0 1 5 4.70 **Q85** 45 366.81 3 1 1 5 5.32 **FPQ24** 45 332.37 3 0 1 5 4.70 **FPQ30** 45 366.81 3 1 1 5 5.32 **FPQ25** 40 320.32 3 0 1 6 3.04

from SER 438

from SER 438

from HIS 1081

from ASP 510

from HIS 1081

from ASP 510

from LYS 460 3.073

**Hydrogen donors**

**Hydrogen acceptors**

*LYS460, ASP508, ARG458, ILE516, GLY459*

*ARG1033, LYS462, PRO1080, HIS1081, GLY1082*

from COOH(CO)–O sp3

from COOH(CO)–N sp2

from COOH(OH)–O sp2

from COOH(CO)–N sp3

from COOH(CO)–N sp2

from COOH(OH)–O sp2

**Flexible bonds**

*LYS460, ILE461, ARG458, GLU477, ASN476*

*ARG1033, PRO1080, HIS1081, GLY1082, SER1085*

from CO–O sp3

**length (Å)**

91

2.852 2.897

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2.775 2.817

2.759 2.830

**Log P**

Docking Studies on Novel Analogues of 8-Chloro-Quinolones against *Staphylococcus aureus* http://dx.doi.org/10.5772/intechopen.72995 91


**Ligand Score/**

90 Molecular Docking

6ClPQ36 −35.59/

FPQ36 −37.23/

Q80 −38.37/

Q83 −42.73/

Q87 −34.72/

Q85 −42.07/

6ClPQ24 −37.07/

FPQ24 −40.64/

6ClPQ30 −37.66/

FPQ30 −41.90/

**RMSD (Å)**

–O sp2

–O sp2

–O sp2

–O sp3

–O sp3

–O sp3

–O sp2

–O sp3

–O sp2

–O sp2

–O sp2

–O sp3

–O sp3

–O sp3

–O sp2

–O sp3

–O sp3

–O sp3

0.23

0.54

0.02

0.07

0.04

0.08

0.27

0.18

0.0063

0.32

**Group interaction/hydrogen bond Bond** 

from LYS 460

from HIS 1081

from ASP 510

from ASP 510

from HIS 1081

from ASP 437

from SER 438

*LYS460, GLY459, ARG458, ILE516, GLU435, ASP512, ASP510, SER1084,* 

*ASN475, ASN476, GLU477,ARG458,SER437, ILE461, LYS460, GLY459*

*ASP510, ASP512, GLY582, ASP508, LEU583 GLU435, ILE516, LYS460, GLY459, ARG458, GLY436, ALA439, SER438, SP437 LEU457*

*ARG458, GLY459, LYS460, ILE461,LEU462, ASN463, LEU519, LYS466,* 

from HIS 1081

from PRO1080

from GLU 477

from ARG 458

from LYS 460 2.935

from ASP 508 2.644

from LYS 460 3.060

from LYS 460 3.060

*LYS460, GLY459, ILE516, GLU435, ASP508, ASP512, ASP510, ARG1033,* 

*PRO1080, HIS1081, GLY1082, SER1085, ARG1033, ASP510, ASP508,* 

*ASP512, ASP510, GLY513, ASP508, ILE516, LYS460, GLY459, ARG458,* 

*ASP437, ARG458, GLU477,ILE461, LYS460, GLY459,TYR1025*

from LYS 460

from COOH(CO)–Nsp3

*PRO1080, HIS1081, GLY1082, SER1085*

from COOH(CO)–N sp2

from COOH(OH)–O sp2

from COOH(OH)–Nsp3

from COOH(OH)–Osp2

from COOH(CO)–N sp2

from COOH(OH)–Osp2

from COOH(CO)–Osp3

*SER1085, GLY1082, HIS1081, PRO1080*

from COOH(OH–N sp2

from COOH(OH)–O sp2

from COOH(OH)–O sp2

from COOH(CO)–N sp2

from COOH(OH)–O sp2

from COOH(OH)–N sp3

*MET622, HIS515, ASP512, TYR1025*

from CO–N sp3

*ARG458, GLY459, GLU477, LYS460, ILE461, ASN475*

from CO–Osp3

from CO–Nsp2

*ASP437, ARG458, GLU477, ILE461, LYS460, GLY459*

*GLU435, ASP12, ILE516, ARG458, LYS460 GLY459*

*LEU457,GLY436, GLU435, ASP437, SER438,ALA439*

from SER 438

from SER 438

from CO–Nsp3

**length (Å)**

3.070 3.040

2.896 2.614

2.855 2.761

2.645 2.778 2.792 3.239

2.981 2.411

2.962 2.821 **Table 3.** The list of intermolecular interactions between the ligand molecules docked with 2XCT using CLC drug discovery workbench software.



**3. Results and discussions**

Molecular docking study has been performed relating to some quinolone compounds known in medical therapeutics: ciprofloxacin, norfloxacin and pefloxacin. For a correct interpretation of the data has been used in the study the corresponding compound of ciprofloxacin, ClCp.

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**Figure 9.** Docking pose of quinolone compounds in the binding site. (a) The quinolones with the similar binding mode of the co-crystallized ligand Cp. (b) The quinolones with the similar binding mode of the ClCp. (c) The quinolones with the similar binding mode of the ligand NF. (d) The quinolones with the similar binding mode of the ligand PF. (e) The

quinolones with the similar binding mode of the ligand FPQ 35.

**Table 4.** Ligands with properties.

and molecular weight, parameters that can be used to evaluate if a molecule has properties that would make it a likely orally active drug, according to the Lipinski's rule of five [25].


The number of violations of the Lipinski rules gives an indication of how *drug-likeness* for a molecule is. In general, orally active drugs have fewer than two violations.

These properties can be useful for identifying potential drug-like molecules, or for removing nondrug-like molecules from a compound library before starting a large virtual screening experiment (**Table 4**).

## **3. Results and discussions**

and molecular weight, parameters that can be used to evaluate if a molecule has properties that

• Number of hydrogen bond donors less than 5 (the total number of nitrogen-hydrogen and

• Number of hydrogen bond acceptors less than 10 (the total number of nitrogen and oxygen

• Log P (octanol–water partition coefficient) less than 5. The calculation of the log P is based

The number of violations of the Lipinski rules gives an indication of how *drug-likeness* for a

These properties can be useful for identifying potential drug-like molecules, or for removing nondrug-like molecules from a compound library before starting a large virtual screening

molecule is. In general, orally active drugs have fewer than two violations.

would make it a likely orally active drug, according to the Lipinski's rule of five [25].

oxygen-hydrogen bonds);

**Table 4.** Ligands with properties.

**Compounds Atoms Weight** 

92 Molecular Docking

**(Daltons)**

**Flexible bonds**

**Lipinski violations**

**FPQ28** 40 354.76 3 0 1 6 3.67 **NClX** 41 335.79 3 0 2 6 0.66 **6ClPQ50** 41 370.23 3 0 2 6 1.28 **PClX** 44 349.81 3 0 1 6 1.12 **6ClPQ51** 44 384.26 3 0 1 6 1.75 **6ClPQ27** 44 349.81 3 0 2 6 1.09 **6ClPQ29** 44 384.26 3 0 2 6 1.72 **6ClPQ35** 39 320.77 3 0 1 5 3.88 **6ClPQ36** 39 355.22 3 0 1 5 4.51 **6ClPQ32** 42 334.80 3 0 1 5 4.24 **6ClPQ33** 42 369.24 3 0 1 5 4.86 **Q80** 45 348.82 3 0 1 5 4.67 **Q87** 45 383.27 3 1 1 5 5.30 **6ClPQ24** 45 348.82 3 0 1 5 4.67 **6ClPQ30** 45 383.27 3 1 1 5 5.30 **6ClPQ25** 40 336.77 3 0 1 6 3.02 **6ClPQ28** 40 371.22 3 0 1 6 3.64

**Hydrogen donors**

**Hydrogen acceptors**

**Log P**

• The molecular weight less than 500 Daltons;

on the XLOGP3-AA method [26].

atoms);

experiment (**Table 4**).

Molecular docking study has been performed relating to some quinolone compounds known in medical therapeutics: ciprofloxacin, norfloxacin and pefloxacin. For a correct interpretation of the data has been used in the study the corresponding compound of ciprofloxacin, ClCp.

**Figure 9.** Docking pose of quinolone compounds in the binding site. (a) The quinolones with the similar binding mode of the co-crystallized ligand Cp. (b) The quinolones with the similar binding mode of the ClCp. (c) The quinolones with the similar binding mode of the ligand NF. (d) The quinolones with the similar binding mode of the ligand PF. (e) The quinolones with the similar binding mode of the ligand FPQ 35.

ClCp is the compound having a chlorine atom in 6-position of quinolone ring in place of fluorine atom.

The result of molecular docking study for quinolone FPQ 28, compound with a good activity '*in vitro'* against *Staphylococcus aureus* ATCC 6538 (MIC = 0.32 μg/ml) and with a good activity against MRSA [19], reveals docking score −39.63 (RMSD 0.17) and shows the occurrence of two hydrogen bonds with HIS 1081 (2.863 Å) and ASP 510 (2.671 Å) (**Figure 8c**). The orientation of the FPQ 28 is the same of NF (norfloxacin). Same orientation shows also the compounds: FPQ 32, FPQ 33, Q 83, Q 85, FPQ 27, FPQ 29, FPQ24 and FPQ 25 (**Figure 9c**). Docking score of NF compound is −39.79 (RMSD 0.11). NF shows the occurrence of two hydrogen bonds with HIS 1081 (2.863 Å) and ASP 510 (2.671 Å). The better score docking has been obtained from quinolone Q83: −42.73 (RMSD 0.07). Q83 shows the occurrence of two hydrogen bonds with HIS 1081 (2.761 Å) and ASP 510 (2.855 Å), and its orientation is the same of NF. Compound Q83 shows also a good activity '*in vitro'* against *Staphylococcus aureus* ATCC 6538 (MIC <0.125 μg/ml).

Results of the docking showed that quinolones have adopted various orientations. The same orientation with the co-crystallized ligand Cp (ciprofloxacin) shows the compound 6 ClPQ 27, 6ClPQ 28, 6ClPQ35 and Q 87. Co-crystallized Cp shows the occurrence of three hydrogen bonds with SER 438 (3.065 Å), SER 438 (2.816 Å) and ASP 437 (2.872 Å) (**Figure 9a**). The quinolones with the similar binding mode of the ClCp are 6ClPQ 51 and 6ClPQ 24 (**Figure 9b**). The quinolones with the similar binding mode of the ligand PF (pefloxacin) are 6ClPQ50, NClX, 6ClPQ 25, Q 80, FPQ 30, 6 ClPQ 33, PClX, FPQ 51, 6 ClPQ 36 and 6ClPQ 30.Docking score of PF is −39.89 (RMSD 0.65).PF shows the occurrence of three hydrogen bonds with LYS 460 (2.732 Å), LYS 460 (2.934 Å) and ASP 512 (2.948 Å) (**Figure 9d**). Same orientation shows the compounds FPQ 35, FPQ 24 and FPQ 50 (**Figure 9e**).

**Figure 10.** (a) MIC histogram of 6-fluoro-quinolone compounds. *Minimum inhibitory concentration* (MIC) of quinolone compounds against *St. aur.* ATCC 6538 (8-H-fluoroquinolones-blue, 8-Cl-fluoroquinolones-red). (b) Score docking of

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**Figure 11.** (a) MIC histogram of 6-chloro-quinolone compounds. Minimum inhibitory concentration (MIC) of quinolone compounds against *St. aur.* ATCC 6538 (8-H-chloroquinolones-blue, 8-Cl-chloroquinolones-red). (b). Score docking of

6-chloro-quinolone compounds (8-H-chloroquinolones-blue, 8-Cl-chloroquinolones-red).

6-fluoro-quinolone compounds (8-H-fluoroquinolones-blue, 8-Cl-fluoroquinolones-red).

#### **3.1. Drug-likeness of the quinolone compounds**

According to the data presented in **Table 4**, four quinolones (Q 85, Q 87, FPQ 30 and 6ClPQ30) failed to respect one parameter (Log P > 5) of the Lipinski rules (Lipinski violation is 1). It was observed that 30 compounds of the study have zero violation of all the parameters involved in Lipinski's rule of five.

## **4. Conclusions**

In silico molecular docking, simulation was performed to position all quinolone compounds into the preferred binding site of the protein receptor *S. aureus* DNA GYRASE, to predict the binding modes, the binding affinities and the orientation. The docking studies revealed that the all compounds showed good docking score. The docking score is a measure of the antimicrobial activity of the studied compounds. A correlation of the predicted data was observed which is obtained by molecular docking study (score docking) with the experimental data obtained from the evaluation of the antimicrobial activity against *Staphylococcus aureus* ATCC 6538 [16] of the quinolone compounds (**Figure 10a**, **b**, and **11a**, **b**).

The studies presented in this chapter show the importance of the design and the molecular docking in the discovery of new compounds with biological activity. The prediction of the

ClCp is the compound having a chlorine atom in 6-position of quinolone ring in place of

The result of molecular docking study for quinolone FPQ 28, compound with a good activity '*in vitro'* against *Staphylococcus aureus* ATCC 6538 (MIC = 0.32 μg/ml) and with a good activity against MRSA [19], reveals docking score −39.63 (RMSD 0.17) and shows the occurrence of two hydrogen bonds with HIS 1081 (2.863 Å) and ASP 510 (2.671 Å) (**Figure 8c**). The orientation of the FPQ 28 is the same of NF (norfloxacin). Same orientation shows also the compounds: FPQ 32, FPQ 33, Q 83, Q 85, FPQ 27, FPQ 29, FPQ24 and FPQ 25 (**Figure 9c**). Docking score of NF compound is −39.79 (RMSD 0.11). NF shows the occurrence of two hydrogen bonds with HIS 1081 (2.863 Å) and ASP 510 (2.671 Å). The better score docking has been obtained from quinolone Q83: −42.73 (RMSD 0.07). Q83 shows the occurrence of two hydrogen bonds with HIS 1081 (2.761 Å) and ASP 510 (2.855 Å), and its orientation is the same of NF. Compound Q83 shows also a good activity '*in vitro'* against *Staphylococcus aureus* ATCC 6538 (MIC <0.125 μg/ml).

Results of the docking showed that quinolones have adopted various orientations. The same orientation with the co-crystallized ligand Cp (ciprofloxacin) shows the compound 6 ClPQ 27, 6ClPQ 28, 6ClPQ35 and Q 87. Co-crystallized Cp shows the occurrence of three hydrogen bonds with SER 438 (3.065 Å), SER 438 (2.816 Å) and ASP 437 (2.872 Å) (**Figure 9a**). The quinolones with the similar binding mode of the ClCp are 6ClPQ 51 and 6ClPQ 24 (**Figure 9b**). The quinolones with the similar binding mode of the ligand PF (pefloxacin) are 6ClPQ50, NClX, 6ClPQ 25, Q 80, FPQ 30, 6 ClPQ 33, PClX, FPQ 51, 6 ClPQ 36 and 6ClPQ 30.Docking score of PF is −39.89 (RMSD 0.65).PF shows the occurrence of three hydrogen bonds with LYS 460 (2.732 Å), LYS 460 (2.934 Å) and ASP 512 (2.948 Å) (**Figure 9d**). Same orientation shows the

According to the data presented in **Table 4**, four quinolones (Q 85, Q 87, FPQ 30 and 6ClPQ30) failed to respect one parameter (Log P > 5) of the Lipinski rules (Lipinski violation is 1). It was observed that 30 compounds of the study have zero violation of all the parameters involved

In silico molecular docking, simulation was performed to position all quinolone compounds into the preferred binding site of the protein receptor *S. aureus* DNA GYRASE, to predict the binding modes, the binding affinities and the orientation. The docking studies revealed that the all compounds showed good docking score. The docking score is a measure of the antimicrobial activity of the studied compounds. A correlation of the predicted data was observed which is obtained by molecular docking study (score docking) with the experimental data obtained from the evaluation of the antimicrobial activity against *Staphylococcus aureus* ATCC

The studies presented in this chapter show the importance of the design and the molecular docking in the discovery of new compounds with biological activity. The prediction of the

6538 [16] of the quinolone compounds (**Figure 10a**, **b**, and **11a**, **b**).

compounds FPQ 35, FPQ 24 and FPQ 50 (**Figure 9e**).

**3.1. Drug-likeness of the quinolone compounds**

in Lipinski's rule of five.

**4. Conclusions**

fluorine atom.

94 Molecular Docking

Docking Studies on Novel Analogues of 8-Chloro-Quinolones against *Staphylococcus aureus* http://dx.doi.org/10.5772/intechopen.72995 95

**Figure 10.** (a) MIC histogram of 6-fluoro-quinolone compounds. *Minimum inhibitory concentration* (MIC) of quinolone compounds against *St. aur.* ATCC 6538 (8-H-fluoroquinolones-blue, 8-Cl-fluoroquinolones-red). (b) Score docking of 6-fluoro-quinolone compounds (8-H-fluoroquinolones-blue, 8-Cl-fluoroquinolones-red).

**Figure 11.** (a) MIC histogram of 6-chloro-quinolone compounds. Minimum inhibitory concentration (MIC) of quinolone compounds against *St. aur.* ATCC 6538 (8-H-chloroquinolones-blue, 8-Cl-chloroquinolones-red). (b). Score docking of 6-chloro-quinolone compounds (8-H-chloroquinolones-blue, 8-Cl-chloroquinolones-red).

binding affinity of a new compound (ligand) to an identified target (protein/enzyme) is a significant parameter in the development of a new drug. The prediction of the binding mode of a ligand (a new compound) to the target (protein/enzyme) by molecular simulation would allow restricting the synthesis to the most promising compounds.

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## **Acknowledgements**

This chapter has been financed through the NUCLEU Program, which is implemented with the support of ANCSI (project no. PN 09-11 01 01 and PN 09-11 01 09).

## **Author details**

Lucia Pintilie\* and Amalia Stefaniu

\*Address all correspondence to: lucia.pintilie@gmail.com

National Institute of Chemical-Pharmaceutical Research and Development, Bucharest, Romania

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This chapter has been financed through the NUCLEU Program, which is implemented with

National Institute of Chemical-Pharmaceutical Research and Development, Bucharest,

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**Chapter 6**

**Provisional chapter**

**Molecular Docking in Halogen Bonding**

**Molecular Docking in Halogen Bonding**

DOI: 10.5772/intechopen.72994

Molecular modeling applies several computational chemistry tools as molecular docking; this latter has been useful in medicinal chemistry for prediction of interactions between small ligands and biological targets measuring angles, enthalpy and other physicalchemical properties involved in the supramolecular entities. In this chapter, we present molecular docking advances with a perspective to the improvement of parameterization including halogen bonding interactions (XB) and the modification of scoring functions based on halogen sigma-hole polarization. At the same time, we have included the current computational methods to study halogen bonding that increased the accuracy of predicted entities. Finally, we present examples of the main force fields including elec-

**Keywords:** molecular docking, scoring functions, force fields, halogen bonding,

Molecular docking is a powerful computational method to predict the pose and intermolecular interactions between a small ligand and a specific receptor (in most of the cases), using algorithms and scoring functions to obtain numerical scores or thermodynamic properties from the most favorable molecular interactions through predicted supramolecular entities. The molecular docking is a useful tool for the medicinal chemist who wants to know with certain accuracy the outcomes for each project; it involves a low computational cost in the quest of utility for predicted compounds in several ligands, i.e., virtual screening. The accuracy of the molecular docking predictions came up from the algorithm and the scoring function that needs to be adequate for each objective. In recent years, it has been a goal to improve the

Carlos Jesús Cortés-García and Luis Chacón-García

tronic distribution and modifications for halogen atoms.

Carlos Jesús Cortés-García and Luis Chacón-García

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

Abel Suárez-Castro, Mario Valle-Sánchez,

Abel Suárez-Castro, Mario Valle-Sánchez,

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72994

molecular modeling, σ-hole

**Abstract**

**1. Introduction**


**Provisional chapter**

## **Molecular Docking in Halogen Bonding**

**Molecular Docking in Halogen Bonding**

Abel Suárez-Castro, Mario Valle-Sánchez, Carlos Jesús Cortés-García and Luis Chacón-García Carlos Jesús Cortés-García and Luis Chacón-García Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

Abel Suárez-Castro, Mario Valle-Sánchez,

http://dx.doi.org/10.5772/intechopen.72994

#### **Abstract**

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98 Molecular Docking

DOI: 10.1021/ci800298z

S0169-409X(00)00129-0

Acta. 2015;**6**. DOI: 10.4172/2153-2435.1000418

2016;**1121**:35-45. DOI: 10.1016/j.molstruc.2016.05.044

Molecular modeling applies several computational chemistry tools as molecular docking; this latter has been useful in medicinal chemistry for prediction of interactions between small ligands and biological targets measuring angles, enthalpy and other physicalchemical properties involved in the supramolecular entities. In this chapter, we present molecular docking advances with a perspective to the improvement of parameterization including halogen bonding interactions (XB) and the modification of scoring functions based on halogen sigma-hole polarization. At the same time, we have included the current computational methods to study halogen bonding that increased the accuracy of predicted entities. Finally, we present examples of the main force fields including electronic distribution and modifications for halogen atoms.

DOI: 10.5772/intechopen.72994

**Keywords:** molecular docking, scoring functions, force fields, halogen bonding, molecular modeling, σ-hole

#### **1. Introduction**

Molecular docking is a powerful computational method to predict the pose and intermolecular interactions between a small ligand and a specific receptor (in most of the cases), using algorithms and scoring functions to obtain numerical scores or thermodynamic properties from the most favorable molecular interactions through predicted supramolecular entities. The molecular docking is a useful tool for the medicinal chemist who wants to know with certain accuracy the outcomes for each project; it involves a low computational cost in the quest of utility for predicted compounds in several ligands, i.e., virtual screening. The accuracy of the molecular docking predictions came up from the algorithm and the scoring function that needs to be adequate for each objective. In recent years, it has been a goal to improve the

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

analysis and prediction of the halogen bonding interactions (XB) that several halogenated small compounds can perform and have a huge relevance in drug discovery.

of medicinal chemistry and theoretical chemistry calculations using ab initio approaches [4]. Here, we describe the XB and their importance in biological systems, the theoretical and chemical bases, the computational methods that have been used to study this interaction to improve the drug design process and the recent applications in the computer drug design research.

Molecular Docking in Halogen Bonding http://dx.doi.org/10.5772/intechopen.72994 101

The importance of XB in drug design research has emerged from the past decade with the discovery of its importance in biological systems as potent stabilizing non-covalent interaction between ligand and receptor complexes. Although the first successful application of the XB concept was in 1996 by the optimization of an inhibitor of clotting factor Xa stressing, the importance of this kind of interaction started since the past decade with the discovery of a four-stranded DNA and aldose reductase complexes with halogens [5, 6]. As an example, one of the first applications of the XB interaction was the development of a compound that contains iodine atom in a pyridinone derivative identified as R221239 as inhibitor of reverse transcriptase in human immunodeficiency virus 1 [7] where the authors compare the reported angles between C─X… O and their findings in the complex interaction between this inhibitor and the reverse transcriptase receptor. For 2009, around 25% was reported that the brand name drugs possess halogen atoms in their chemical structure becoming this type of atoms in important molecular scaffold fragments in drug design [8]. The insights about the XB concept have led to its implementation into the principal approaches of drug design process being the computational methods of the most useful approaches to predict this kind of interactions to improve the predictions through the computer calculations to generate accurate results that can help for the best design of compounds as drug candidates for many diseases [4]. The importance of XB in drug design has been compared with the hydrogen bonding (XB) interactions but with the difference that the first ones have some chemical properties in the strength

Biological systems are composed of few elements from the periodic table, being based on carbon, oxygen, nitrogen, hydrogen, phosphorus and sulfur, but at the same time, few biological compounds contain halogens as iodine in the thyroid hormones functions [10], fluorine in bone-specific structures as teeth [11] and the chloride that has an anionic effect [12]. This type of elements is very important because they are not abundant in the cellular or subcellular structures, which means that they have specific interactions. In the human body, the presence of some biological compounds and ions that are halogenated starting with the thyroid hormones, the fluoride and chloride ions and its effect as anions playing an important role keeping the homeostasis of some important physiological mechanisms is well known. The beginning of the importance about the XB in biological systems started in 2003 with the discovery of a four-stranded DNA Holliday Junction that contains a bromine atom that played an important role in this type of macromolecular interaction [5] and the discovery of the complex aldolase reductase and a halogenated inhibitor at high resolution [6] where a bromine interaction was found as unusual showing short bromine-oxygen contact around 12% less

**1.2. Halogen bonding in drug design: an emerged non-covalent interaction**

and short distances between the atoms that form them [9].

**1.3. Importance of halogen bonding in biological systems**

The molecular interactions generated from a halogenated compound with a specific receptor could be addressed with molecular docking studies using quantum mechanics/molecular mechanics (QM/MM) approaches, combining a specific force field that could predict the chemical interactions of halogenated ligands based in their electronic distribution when they are close to an electronegative or electropositive atom. Here, we present some of current scoring functions (SF) used in molecular docking and some examples of works starting with the XB potential of mean force (XBPMF) that is a knowledge-based SF, following with the VinaXB, which is an implementation of the halogen bonding scoring function (XBSF) classified into the empirical-based SF. In order to improve molecular docking experiments regarding the XB interaction, in the lasts years, some force fields presented with high detail in here have been implemented and have been used by numerous researchers with fine performance and high accuracy; these are the optimized potentials for liquid simulations-all atoms (OPLS-AA), which is applied to biological macromolecules and the force field for biological halogen bonds (ffBXB) that implemented the anisotropic effect to investigate the XB between small compounds as ligands and specific receptors in molecular modeling.

#### **1.1. Halogen bonding (XB)**

The XB is defined as the interaction where a halogen is an electrophilic species and can be described as D … X-Y, where X is the electrophilic halogen atom (Lewis acid, XB donor), D is the donor of electron density (Lewis base, XB acceptor) and Y is a carbon, nitrogen or halogen atom, and in this context, the X electrophilic halogen atoms are iodine, bromine and chlorine (**Figure 1**), and the fluorine halogen atom is not considered under this description because this atom does not have the capacity to form the σ-hole effect [1]. The ability of halogens to form interactions with electron donor species was reported unequivocal the first time by Guthrie in 1896 [2] where he reported the formation of ammoniac-iodine complex and described the properties and the necessary conditions to obtain this unusual interaction now. In the subsequent years, there are reports about the interactions between amines and the bromine and chlorine halogens. In 1970, Odd Hassel explained the similarities in halogen and hydrogen bonding and remarked the importance of this kind of interactions and the opportunity to understand the atomic arrangements in donor-acceptor complexes [3]. The study of XB interaction has become interesting to be studied in many fields including rational drug design under the basis

**Figure 1.** Schematic representation of a XB interaction, X = Lewis acid donor, halogen (I, Br, Cl); D = Lewis base acceptor; Y = carbon, nitrogen.

of medicinal chemistry and theoretical chemistry calculations using ab initio approaches [4]. Here, we describe the XB and their importance in biological systems, the theoretical and chemical bases, the computational methods that have been used to study this interaction to improve the drug design process and the recent applications in the computer drug design research.

#### **1.2. Halogen bonding in drug design: an emerged non-covalent interaction**

analysis and prediction of the halogen bonding interactions (XB) that several halogenated

The molecular interactions generated from a halogenated compound with a specific receptor could be addressed with molecular docking studies using quantum mechanics/molecular mechanics (QM/MM) approaches, combining a specific force field that could predict the chemical interactions of halogenated ligands based in their electronic distribution when they are close to an electronegative or electropositive atom. Here, we present some of current scoring functions (SF) used in molecular docking and some examples of works starting with the XB potential of mean force (XBPMF) that is a knowledge-based SF, following with the VinaXB, which is an implementation of the halogen bonding scoring function (XBSF) classified into the empirical-based SF. In order to improve molecular docking experiments regarding the XB interaction, in the lasts years, some force fields presented with high detail in here have been implemented and have been used by numerous researchers with fine performance and high accuracy; these are the optimized potentials for liquid simulations-all atoms (OPLS-AA), which is applied to biological macromolecules and the force field for biological halogen bonds (ffBXB) that implemented the anisotropic effect to investigate the

small compounds can perform and have a huge relevance in drug discovery.

XB between small compounds as ligands and specific receptors in molecular modeling.

The XB is defined as the interaction where a halogen is an electrophilic species and can be described as D … X-Y, where X is the electrophilic halogen atom (Lewis acid, XB donor), D is the donor of electron density (Lewis base, XB acceptor) and Y is a carbon, nitrogen or halogen atom, and in this context, the X electrophilic halogen atoms are iodine, bromine and chlorine (**Figure 1**), and the fluorine halogen atom is not considered under this description because this atom does not have the capacity to form the σ-hole effect [1]. The ability of halogens to form interactions with electron donor species was reported unequivocal the first time by Guthrie in 1896 [2] where he reported the formation of ammoniac-iodine complex and described the properties and the necessary conditions to obtain this unusual interaction now. In the subsequent years, there are reports about the interactions between amines and the bromine and chlorine halogens. In 1970, Odd Hassel explained the similarities in halogen and hydrogen bonding and remarked the importance of this kind of interactions and the opportunity to understand the atomic arrangements in donor-acceptor complexes [3]. The study of XB interaction has become interesting to be studied in many fields including rational drug design under the basis

**Figure 1.** Schematic representation of a XB interaction, X = Lewis acid donor, halogen (I, Br, Cl); D = Lewis base acceptor;

**1.1. Halogen bonding (XB)**

100 Molecular Docking

Y = carbon, nitrogen.

The importance of XB in drug design research has emerged from the past decade with the discovery of its importance in biological systems as potent stabilizing non-covalent interaction between ligand and receptor complexes. Although the first successful application of the XB concept was in 1996 by the optimization of an inhibitor of clotting factor Xa stressing, the importance of this kind of interaction started since the past decade with the discovery of a four-stranded DNA and aldose reductase complexes with halogens [5, 6]. As an example, one of the first applications of the XB interaction was the development of a compound that contains iodine atom in a pyridinone derivative identified as R221239 as inhibitor of reverse transcriptase in human immunodeficiency virus 1 [7] where the authors compare the reported angles between C─X… O and their findings in the complex interaction between this inhibitor and the reverse transcriptase receptor. For 2009, around 25% was reported that the brand name drugs possess halogen atoms in their chemical structure becoming this type of atoms in important molecular scaffold fragments in drug design [8]. The insights about the XB concept have led to its implementation into the principal approaches of drug design process being the computational methods of the most useful approaches to predict this kind of interactions to improve the predictions through the computer calculations to generate accurate results that can help for the best design of compounds as drug candidates for many diseases [4]. The importance of XB in drug design has been compared with the hydrogen bonding (XB) interactions but with the difference that the first ones have some chemical properties in the strength and short distances between the atoms that form them [9].

#### **1.3. Importance of halogen bonding in biological systems**

Biological systems are composed of few elements from the periodic table, being based on carbon, oxygen, nitrogen, hydrogen, phosphorus and sulfur, but at the same time, few biological compounds contain halogens as iodine in the thyroid hormones functions [10], fluorine in bone-specific structures as teeth [11] and the chloride that has an anionic effect [12]. This type of elements is very important because they are not abundant in the cellular or subcellular structures, which means that they have specific interactions. In the human body, the presence of some biological compounds and ions that are halogenated starting with the thyroid hormones, the fluoride and chloride ions and its effect as anions playing an important role keeping the homeostasis of some important physiological mechanisms is well known. The beginning of the importance about the XB in biological systems started in 2003 with the discovery of a four-stranded DNA Holliday Junction that contains a bromine atom that played an important role in this type of macromolecular interaction [5] and the discovery of the complex aldolase reductase and a halogenated inhibitor at high resolution [6] where a bromine interaction was found as unusual showing short bromine-oxygen contact around 12% less than their van der Waals radii of both atoms. These findings attracted the attention of medicinal chemists and theoretical chemists to search deeply the characteristics of these interactions. The thyroid hormones are the most studied and understood halogen compounds in biological systems where the iodine atom forms a halogen bonding with the oxygen atom in the binding site for the thyroxine with short I—O interactions that play essential roles for the highly recognition of these types of hormones. Also, the thyroxine hormone binds to RNA sequences through halogen bonds [13]. Although the fluoride is not considered as a halogen bond, its molecular mechanisms are the more studied in the aspect of the toxicity of this halogen that explains the high negative effect of this halogen in the cellular respiration, generation of reactive oxygen species, necrosis and apoptosis between others [14]. The halogen bonding has the effect of stabilizing inter- and intramolecular interactions that can stabilize ligand interactions and can affect molecular folding [15]. In drug design, the pharmacological research has included many halogenated molecules that are inhibitors (some of them approved), but only few times, this interaction is considered as important for the rational drug design process. There are many X-ray crystal structures in the PDB that contain halogen bonding interactions.

## **2. Halogenated drugs in medicinal chemistry**

At present, the insertion of halogen atoms to improve the biological profile of a candidate compound has become an important strategy in drug development, and it is quite common in analogue-based drug discovery [16, 17]. Consequently, in medicinal chemistry the halogenation benefits include (a) increased membrane permeability, facilitating the blood-brain barrier crossing; (b) lower metabolic degradation, prolonging the lifetime of the drug; and (c) the addition of specific effects that enhance its binding to target macromolecules [18–20]. However, it was only recently that heavy halogen atoms are recognized to play an important role in the pharmacological activity through an interaction now defined as the halogen bond [21]. For this reason, it should not be surprising to find a greater presence of halogenated compounds at all stages of drug development.

In this context, the FDA has approved over 1582 new molecular entities (NME), of which approximately 20% are halogenated [22, 23]. On the other hand, 35% of the top 15 best-selling drugs between 2010 and 2016 were halogenated [24–26]. What is more interesting is that the pharmaceuticals called "blockbuster drugs" are mostly halogenated compounds (some examples are shown in **Figure 2**) [27, 28]. Additionally, a detailed analysis about the halogen atoms and statistical analysis of organohalogens and halogen bonds in medicinal chemistry were performed by Njardarson et al. [29], Hernandes et al. [18] and Zhu et al. [30], respectively.

The XB is an important approach in lead optimization of drug development and increases the

Molecular Docking in Halogen Bonding http://dx.doi.org/10.5772/intechopen.72994 103

Considered as the first event in a chemical process, molecular recognition is a fundamental but complex step in the building of supramolecular entities [32]. Molecular recognition involves the synergy of a vast number of weak interactions, such as hydrogen bonding, and electrostatic, hydrophobic and other nonconventional interactions [33]. In this context, we can mention anion-π stacking, hyper-coordination of carbon atoms and the σ-hole deformation

Halogen bonding (XB) is a non-covalent interaction classified into Lewis acid-base bonding, where particularly in this species, halogen acts as the Lewis acid in front of neutral or anionic

binding affinity and binding selectivity [31].

**3. Theory and concepts of halogen bonding**

**Figure 2.** Some halogenated drugs considered as "blockbuster" drugs.

that originates from the halogen bonding interactions [34–36].

#### **2.1. Optimization of the halogenated drugs**

The objectives to optimizing a drug are to increase their oral bioability and pharmacological pharmacodynamics and improve its metabolism. In the case of halogenated drugs, the influence of a halogen atom or substituents improves the thermodynamic parameters of the system (ligand-receptor pair), and the dissociation constant (*Kd*) is positively modified [18].

**Figure 2.** Some halogenated drugs considered as "blockbuster" drugs.

than their van der Waals radii of both atoms. These findings attracted the attention of medicinal chemists and theoretical chemists to search deeply the characteristics of these interactions. The thyroid hormones are the most studied and understood halogen compounds in biological systems where the iodine atom forms a halogen bonding with the oxygen atom in the binding site for the thyroxine with short I—O interactions that play essential roles for the highly recognition of these types of hormones. Also, the thyroxine hormone binds to RNA sequences through halogen bonds [13]. Although the fluoride is not considered as a halogen bond, its molecular mechanisms are the more studied in the aspect of the toxicity of this halogen that explains the high negative effect of this halogen in the cellular respiration, generation of reactive oxygen species, necrosis and apoptosis between others [14]. The halogen bonding has the effect of stabilizing inter- and intramolecular interactions that can stabilize ligand interactions and can affect molecular folding [15]. In drug design, the pharmacological research has included many halogenated molecules that are inhibitors (some of them approved), but only few times, this interaction is considered as important for the rational drug design process. There are many X-ray crystal structures in the PDB that contain halogen bonding interactions.

At present, the insertion of halogen atoms to improve the biological profile of a candidate compound has become an important strategy in drug development, and it is quite common in analogue-based drug discovery [16, 17]. Consequently, in medicinal chemistry the halogenation benefits include (a) increased membrane permeability, facilitating the blood-brain barrier crossing; (b) lower metabolic degradation, prolonging the lifetime of the drug; and (c) the addition of specific effects that enhance its binding to target macromolecules [18–20]. However, it was only recently that heavy halogen atoms are recognized to play an important role in the pharmacological activity through an interaction now defined as the halogen bond [21]. For this reason, it should not be surprising to find a greater presence of halogenated

In this context, the FDA has approved over 1582 new molecular entities (NME), of which approximately 20% are halogenated [22, 23]. On the other hand, 35% of the top 15 best-selling drugs between 2010 and 2016 were halogenated [24–26]. What is more interesting is that the pharmaceuticals called "blockbuster drugs" are mostly halogenated compounds (some examples are shown in **Figure 2**) [27, 28]. Additionally, a detailed analysis about the halogen atoms and statistical analysis of organohalogens and halogen bonds in medicinal chemistry were performed by Njardarson et al. [29], Hernandes et al. [18] and Zhu et al. [30], respectively.

The objectives to optimizing a drug are to increase their oral bioability and pharmacological pharmacodynamics and improve its metabolism. In the case of halogenated drugs, the influence of a halogen atom or substituents improves the thermodynamic parameters of the system (ligand-receptor pair), and the dissociation constant (*Kd*) is positively modified [18].

**2. Halogenated drugs in medicinal chemistry**

102 Molecular Docking

compounds at all stages of drug development.

**2.1. Optimization of the halogenated drugs**

The XB is an important approach in lead optimization of drug development and increases the binding affinity and binding selectivity [31].

## **3. Theory and concepts of halogen bonding**

Considered as the first event in a chemical process, molecular recognition is a fundamental but complex step in the building of supramolecular entities [32]. Molecular recognition involves the synergy of a vast number of weak interactions, such as hydrogen bonding, and electrostatic, hydrophobic and other nonconventional interactions [33]. In this context, we can mention anion-π stacking, hyper-coordination of carbon atoms and the σ-hole deformation that originates from the halogen bonding interactions [34–36].

Halogen bonding (XB) is a non-covalent interaction classified into Lewis acid-base bonding, where particularly in this species, halogen acts as the Lewis acid in front of neutral or anionic Lewis base entities. This interaction was first reported by Guthrie in the middle of the nineteenth century; nonetheless, it has attracted attention after its "rediscovery" in the 1990s as a strong interaction even compared with hydrogen bonding [37].

**4. Current computational methods to study the halogen bonding**

Moller-Plesset truncated at the second order (MP2) is valid for XB interactions [43].

**5. Molecular docking and halogen bonding**

It is important to consider that to apply computational methods in drug design, it is necessary to consider the use of those that are accessible and reliable for simulating. The docking experiments can help us to process a big amount of information through virtual screening where many compounds are halogenated, and in this sense, this calculation is more efficient to know how the halogenated ligand can bind in some specific target, but almost all the docking scoring functions are not capable to model the XB in a correct way, leading to some errors that we can interpret as false positives or vice versa. To address this point, there are some methods and approaches that allow us to model, search and know the best rank poses into a binding site, and this type of calculations is based on ab initio calculations and, in some cases, is modified as scoring functions into the docking algorithms with the software that are well known. The ab initio calculations can be performed with the evaluation of quantum mechanics/molecular mechanics (QM/MM) approaches. Therefore, some accurate methods play a key role in the prediction of binding free energy that rescores the best docking poses; the most useful method to do that is the molecular mechanics/generalized-born/surface area (MM/GBSA) [44]. Molecular docking is in some cases improved by this type of calculations, but now we described the molecular docking scoring functions as improved tools to get accurate predicted results in XB.

Molecular docking is classified into the structure-based drug design methods and is a good and extensively medicinal chemistry tool to predict the pose of a ligand in a specific region of the receptor structure. As is well known, molecular docking has two main components: scoring functions and search algorithms. The scoring functions can predict the affinity energy between the ligand and the receptor by calculations of the all possible interactions being the best ranked those that have the minimum energy (Δ*G*). One of the most used applications of molecular docking is virtual screening to find probable lead compounds against some specific receptor, because this method has the capability to do this and presents a less consuming time

As we have described so far in this chapter, the XB is relevant in drug design, and it requires to be studied and implemented by the current auxiliary computational tools and methods for drug design, and for this propose, the simulations of the σ-hole effect is a challenging task because not all the computational methods can achieve the accuracy to predict the distance, angle and strength of the interaction. There are references addressing algorithms to describe this phenomenon [41, 42], and the main parameters employed have been the chargetransfer (CT) complex, the electrostatic interactions (EI) and the polarization of the halogen atoms when they are in an environmental where their behavior is as a Lewis acid. The XB interactions arise from a combination of EI, CT and dispersion interactions. Other important considerations are the net attractive Coulomb interactions that play a key role in the σ-hole interactions. One of the deepest methods to simulate the XB came from the coupled clusters with single and double (CCSD) substitution method which came from the Hartree-Fock determinant, and the CCSD (T) provides better results in the type of interaction; the lighter

Molecular Docking in Halogen Bonding http://dx.doi.org/10.5772/intechopen.72994 105

The halogen bonding interaction is defined as pre-reactive complexes formed between species with a type Y-X----D, where X is a halogen atom that can behave as an electron acceptor, D is a neutral or anionic nucleophile and Y could be nitrogen, oxygen, carbon, halogen, etc. Also, n and π electron pairs can form interactions as XB acceptors. It is well known that alkenes and arenes can form complexes with dihalogen molecules prior to formation of addition or substitution products [15].

Theoretical and experimental data about this phenomenon prove that the four halogens can act as XB formers marking a tendency in strength from the strongest I > Br > Cl > F to the weakest interaction. Charge transfer, polarization, concentration, temperature, solvent properties and the nature of A play an important role in the ability of halogen.

The XB interaction energy spans from 5 to 180 KJ/mol, giving stability to formed complexes and a typical interaction angle of ~180°, leading to linear or slightly bended architectures in crystallographic data of available complexes, which correlates with the calculations that propose a deformation in the halogen σ\* molecular orbital. This phenomenon is called "the sigma hole" [15, 38].

Applications of XB properties are wide, covering crystal engineering design, improvement of conductor materials and the design of drugs.

The employment of halogen bonding in biomedical tasks is a new and interesting trend as the halogen can afford a short-range interaction (smaller to van der Waals interaction length) with electron-rich atoms involved in biological receptors and enzyme's active sites [38].

The electron acceptor stage of a halogen atom is a fashion research topic due to its outstanding properties. The preferred complexes that are subject of study are those where B is a tertiary amine. For example, García-Garibay's group recently reported the dynamics of a supramolecular rotor where the axle is based on this interaction between DABCO as an acceptor and 1,4-diiodotetrafluorobenzene as the halogen donor [39].

Applications of XB properties are wide, covering crystal engineering design, development of drugs and improvement of conductor materials.

Computational calculations help to explain, correlate and predict behavior of halogen donors and acceptors. The most accurate methods involve the use of quantum mechanics (QM) to calculate geometry and architecture of halogen bonding, but most of them are just available for small molecules. The development of different algorithms and methods is a useful tool to generate indirect experimental measurements of halogen bonding involving biological targets [38–40].

Interactions between proteins and drugs can be predicted by molecular docking; this method analyzes two crystallographic structures: one about biological target and the other about drug's molecule. This computational experiment uses classic mechanic's collisions, potential energy surfaces and some electrostatic and geometrical descriptors to correlate assemblies and enthalpy of the supramolecular complexes; the best methods will be treated further in this chapter.

## **4. Current computational methods to study the halogen bonding**

Lewis base entities. This interaction was first reported by Guthrie in the middle of the nineteenth century; nonetheless, it has attracted attention after its "rediscovery" in the 1990s as a

The halogen bonding interaction is defined as pre-reactive complexes formed between species with a type Y-X----D, where X is a halogen atom that can behave as an electron acceptor, D is a neutral or anionic nucleophile and Y could be nitrogen, oxygen, carbon, halogen, etc. Also, n and π electron pairs can form interactions as XB acceptors. It is well known that alkenes and arenes can form complexes with dihalogen molecules prior to formation of addition or

Theoretical and experimental data about this phenomenon prove that the four halogens can act as XB formers marking a tendency in strength from the strongest I > Br > Cl > F to the weakest interaction. Charge transfer, polarization, concentration, temperature, solvent properties

The XB interaction energy spans from 5 to 180 KJ/mol, giving stability to formed complexes and a typical interaction angle of ~180°, leading to linear or slightly bended architectures in crystallographic data of available complexes, which correlates with the calculations that propose a deformation in the halogen σ\* molecular orbital. This phenomenon is called "the

Applications of XB properties are wide, covering crystal engineering design, improvement of

The employment of halogen bonding in biomedical tasks is a new and interesting trend as the halogen can afford a short-range interaction (smaller to van der Waals interaction length) with

The electron acceptor stage of a halogen atom is a fashion research topic due to its outstanding properties. The preferred complexes that are subject of study are those where B is a tertiary amine. For example, García-Garibay's group recently reported the dynamics of a supramolecular rotor where the axle is based on this interaction between DABCO as an acceptor and

Applications of XB properties are wide, covering crystal engineering design, development of

Computational calculations help to explain, correlate and predict behavior of halogen donors and acceptors. The most accurate methods involve the use of quantum mechanics (QM) to calculate geometry and architecture of halogen bonding, but most of them are just available for small molecules. The development of different algorithms and methods is a useful tool to generate indirect experimental measurements of halogen bonding involving biological targets [38–40].

Interactions between proteins and drugs can be predicted by molecular docking; this method analyzes two crystallographic structures: one about biological target and the other about drug's molecule. This computational experiment uses classic mechanic's collisions, potential energy surfaces and some electrostatic and geometrical descriptors to correlate assemblies and enthalpy of the supramolecular complexes; the best methods will be treated

electron-rich atoms involved in biological receptors and enzyme's active sites [38].

strong interaction even compared with hydrogen bonding [37].

and the nature of A play an important role in the ability of halogen.

substitution products [15].

104 Molecular Docking

sigma hole" [15, 38].

further in this chapter.

conductor materials and the design of drugs.

1,4-diiodotetrafluorobenzene as the halogen donor [39].

drugs and improvement of conductor materials.

As we have described so far in this chapter, the XB is relevant in drug design, and it requires to be studied and implemented by the current auxiliary computational tools and methods for drug design, and for this propose, the simulations of the σ-hole effect is a challenging task because not all the computational methods can achieve the accuracy to predict the distance, angle and strength of the interaction. There are references addressing algorithms to describe this phenomenon [41, 42], and the main parameters employed have been the chargetransfer (CT) complex, the electrostatic interactions (EI) and the polarization of the halogen atoms when they are in an environmental where their behavior is as a Lewis acid. The XB interactions arise from a combination of EI, CT and dispersion interactions. Other important considerations are the net attractive Coulomb interactions that play a key role in the σ-hole interactions. One of the deepest methods to simulate the XB came from the coupled clusters with single and double (CCSD) substitution method which came from the Hartree-Fock determinant, and the CCSD (T) provides better results in the type of interaction; the lighter Moller-Plesset truncated at the second order (MP2) is valid for XB interactions [43].

It is important to consider that to apply computational methods in drug design, it is necessary to consider the use of those that are accessible and reliable for simulating. The docking experiments can help us to process a big amount of information through virtual screening where many compounds are halogenated, and in this sense, this calculation is more efficient to know how the halogenated ligand can bind in some specific target, but almost all the docking scoring functions are not capable to model the XB in a correct way, leading to some errors that we can interpret as false positives or vice versa. To address this point, there are some methods and approaches that allow us to model, search and know the best rank poses into a binding site, and this type of calculations is based on ab initio calculations and, in some cases, is modified as scoring functions into the docking algorithms with the software that are well known. The ab initio calculations can be performed with the evaluation of quantum mechanics/molecular mechanics (QM/MM) approaches. Therefore, some accurate methods play a key role in the prediction of binding free energy that rescores the best docking poses; the most useful method to do that is the molecular mechanics/generalized-born/surface area (MM/GBSA) [44]. Molecular docking is in some cases improved by this type of calculations, but now we described the molecular docking scoring functions as improved tools to get accurate predicted results in XB.

## **5. Molecular docking and halogen bonding**

Molecular docking is classified into the structure-based drug design methods and is a good and extensively medicinal chemistry tool to predict the pose of a ligand in a specific region of the receptor structure. As is well known, molecular docking has two main components: scoring functions and search algorithms. The scoring functions can predict the affinity energy between the ligand and the receptor by calculations of the all possible interactions being the best ranked those that have the minimum energy (Δ*G*). One of the most used applications of molecular docking is virtual screening to find probable lead compounds against some specific receptor, because this method has the capability to do this and presents a less consuming time of the calculations during the process, but we may say that not all of the scoring functions have the capacity to identify and predict the best XB interactions, and for this task, many scoring functions have emerged in molecular docking to improve and try to solve this problem.

*E* = *WD* (1)

Molecular Docking in Halogen Bonding http://dx.doi.org/10.5772/intechopen.72994 107

To validate the implementation of this scoring function, 106 halogenated ligand-protein complexes were evaluated with Vina and VinaXB finding that XB scoring function was closer to

Derived from the development and implementation of scoring functions in XB in the past early years, there are few researches that apply this new scoring function. More relevant, we describe the most useful empirical-based scoring function VinaXB so far. As is well known, AutoDock Vina is a free docking tool, and the addition of the XB can be added to it. One of the first researches reported that the empirical-based scoring functions were used in the work developed by Pal et al. [50] where they reported the application of VinaXB scoring function in molecular docking experiments with an aberrant expression of Notch-1 in aldehyde dehydrogenase (ALDH) in cancer stem cells in breast cancer. The aim of using molecular docking was to search for the binding ability of psoralidin with gamma secretase where the best pose ranked with a value of free energy of −8.5 kcal/mol was found suggesting that psoralidin binds to nicarstin in the micromolar concentrations. The docking studies let them know the main chain residues in the binding pocket with accuracy. Šeflová et al. [51] reported the effect of halogenated phenylquinolines specifically 5,6,7,8-tetrafluoro-3-hydroxy-2-phenylquinolin-4(1H)-one (**TFHPQ**) (**Figure 3a**) on Na+/K+-ATPase (NKA) where the experimental observations with the results from molecular docking using the VinaXB scoring function correlated. An important observation for these studies is that the compounds investigated firstly were optimized using density functional theory at the B3P86/631 + G (dp) level (289 K and 1 atm) and then were submitted to docking experiments to the open and closed NKA enzyme. The docking was performed in two steps: first, in a general screening with the whole protein, exhaustiveness was set to 400, and the number of modes was 9999; afterwards, they carried out redocking in the most favorable regions using the AutoDock VinaXB extension. The finding in this study that came from molecular docking was that the results provided a clue to the question why only **TFHPQ** inhibited in the in vitro studies to NKA, while other analogues can bind in the TFHPQ binding pose but were less active despite that all of molecules have similar chemical structure because the free energies were different by 1–3 kcal/mol, and in addition, they can bind in several sites of the NKA enzyme being dif-

Another well application of VinaXB soring function is in the work developed by Enkhtaivan et al. [52] where they researched the ability of berberine-based derivatives (**BDs**) as antiinfluenza agents against the neuraminidase using the VinaXB scoring function finding that **BD-5** (**Figure 3b**) has better affinity energies than oseltamivir that was used as a control in the

**6. Achievements and advances in the study of halogen bonding with** 

where *W* is the weight, *ϕ* is the angle factor and *D* is the distance factor.

the original poses below 2 Å deviation twice than Vina.

ferent for **TFHPQ**.

utilized neuraminidase receptor.

**modified and improved docking scoring functions**

#### **5.1. Scoring functions to study the halogen bonding**

There are some scoring functions to predict and model the XB in molecular docking and now are well known and designed knowledge- and empirical-based methods.

#### *5.1.1. Knowledge-based method*

This type of scoring functions is based in pairwise interactions that came from experimental properties of molecular interactions of high-resolution X-ray crystal structures and most of the times came from the Protein Data Bank (PDB). The particularity of this type of scoring functions is that it improves the computational efficiency but lacks enough accuracy. In the case of XB scoring functions of this type, we can cite to Zhu et al. [45] who developed a scoring function named XBPMF (halogen bonding potential of mean force) that was developed from two high-quality training datasets of protein-ligand complexes. The XB and the hydrogen binding (HB) were characterized by two-dimensional potentials for taking the energetic and geometric preferences for ligand-receptor interactions. The authors establish that this scoring function was evaluated to have moderate power of predicting ligand-receptor interactions in terms of docking power that shows the ability of the scoring function to identify the original ligand conformation from a set of decoys and is reflected in the root-mean-square deviation (RMSD) of the best conformation of the ligand with the minimum free energy. At the same time, ranking power that is the ability to rank a set of ligands against a receptor by affinity, was obtained and is described as scoring power being good scoring function for high-throughput virtual screening.

#### *5.1.2. Empirical-based method*

This type of scoring functions has been designed to estimate the free energy between ligand and its receptor when it is possible to know the structure information or it can be approximated [46]. This scoring function uses some parameterized functions based in physical or chemical properties, and the most important consideration is that this method is parameterized against training sets derived from experimental data [47]. One of the first empirical-based scoring functions was described by Watts et al., which considers local cooperative effects from the interaction between ligand and receptor [48] using a "small network" approach to describe how the environment affects to the non-covalent interactions as XB. The capability to predict with accuracy the binding affinities is when occurred small local changes in a ligand configuration, leading to obtaining the best affinity values.

More recently, Koebel et al. developed a new empirical-based scoring function that has been added to the most widely free used docking tool as AutoDock Vina (AutoDock VinaXB) that is an implementation of the halogen bonding score function (XBSF) [49]. This scoring function is derived on the X … A distance and C─X … A angle; other important parameters that are considered are the size and the anisotropic charge of the halogen atoms; and to define the halogen bonding term, an angle term was included to account for the varying positive charge on the atom (Eq. (1)):

$$E = \mathcal{W}\phi D\tag{1}$$

where *W* is the weight, *ϕ* is the angle factor and *D* is the distance factor.

of the calculations during the process, but we may say that not all of the scoring functions have the capacity to identify and predict the best XB interactions, and for this task, many scoring functions have emerged in molecular docking to improve and try to solve this problem.

There are some scoring functions to predict and model the XB in molecular docking and now

This type of scoring functions is based in pairwise interactions that came from experimental properties of molecular interactions of high-resolution X-ray crystal structures and most of the times came from the Protein Data Bank (PDB). The particularity of this type of scoring functions is that it improves the computational efficiency but lacks enough accuracy. In the case of XB scoring functions of this type, we can cite to Zhu et al. [45] who developed a scoring function named XBPMF (halogen bonding potential of mean force) that was developed from two high-quality training datasets of protein-ligand complexes. The XB and the hydrogen binding (HB) were characterized by two-dimensional potentials for taking the energetic and geometric preferences for ligand-receptor interactions. The authors establish that this scoring function was evaluated to have moderate power of predicting ligand-receptor interactions in terms of docking power that shows the ability of the scoring function to identify the original ligand conformation from a set of decoys and is reflected in the root-mean-square deviation (RMSD) of the best conformation of the ligand with the minimum free energy. At the same time, ranking power that is the ability to rank a set of ligands against a receptor by affinity, was obtained and is described as scoring power

This type of scoring functions has been designed to estimate the free energy between ligand and its receptor when it is possible to know the structure information or it can be approximated [46]. This scoring function uses some parameterized functions based in physical or chemical properties, and the most important consideration is that this method is parameterized against training sets derived from experimental data [47]. One of the first empirical-based scoring functions was described by Watts et al., which considers local cooperative effects from the interaction between ligand and receptor [48] using a "small network" approach to describe how the environment affects to the non-covalent interactions as XB. The capability to predict with accuracy the binding affinities is when occurred small local changes in a ligand configuration,

More recently, Koebel et al. developed a new empirical-based scoring function that has been added to the most widely free used docking tool as AutoDock Vina (AutoDock VinaXB) that is an implementation of the halogen bonding score function (XBSF) [49]. This scoring function is derived on the X … A distance and C─X … A angle; other important parameters that are considered are the size and the anisotropic charge of the halogen atoms; and to define the halogen bonding term, an angle term was included to account for the varying positive charge on the atom (Eq. (1)):

are well known and designed knowledge- and empirical-based methods.

being good scoring function for high-throughput virtual screening.

**5.1. Scoring functions to study the halogen bonding**

*5.1.1. Knowledge-based method*

106 Molecular Docking

*5.1.2. Empirical-based method*

leading to obtaining the best affinity values.

To validate the implementation of this scoring function, 106 halogenated ligand-protein complexes were evaluated with Vina and VinaXB finding that XB scoring function was closer to the original poses below 2 Å deviation twice than Vina.

## **6. Achievements and advances in the study of halogen bonding with modified and improved docking scoring functions**

Derived from the development and implementation of scoring functions in XB in the past early years, there are few researches that apply this new scoring function. More relevant, we describe the most useful empirical-based scoring function VinaXB so far. As is well known, AutoDock Vina is a free docking tool, and the addition of the XB can be added to it. One of the first researches reported that the empirical-based scoring functions were used in the work developed by Pal et al. [50] where they reported the application of VinaXB scoring function in molecular docking experiments with an aberrant expression of Notch-1 in aldehyde dehydrogenase (ALDH) in cancer stem cells in breast cancer. The aim of using molecular docking was to search for the binding ability of psoralidin with gamma secretase where the best pose ranked with a value of free energy of −8.5 kcal/mol was found suggesting that psoralidin binds to nicarstin in the micromolar concentrations. The docking studies let them know the main chain residues in the binding pocket with accuracy. Šeflová et al. [51] reported the effect of halogenated phenylquinolines specifically 5,6,7,8-tetrafluoro-3-hydroxy-2-phenylquinolin-4(1H)-one (**TFHPQ**) (**Figure 3a**) on Na+/K+-ATPase (NKA) where the experimental observations with the results from molecular docking using the VinaXB scoring function correlated. An important observation for these studies is that the compounds investigated firstly were optimized using density functional theory at the B3P86/631 + G (dp) level (289 K and 1 atm) and then were submitted to docking experiments to the open and closed NKA enzyme. The docking was performed in two steps: first, in a general screening with the whole protein, exhaustiveness was set to 400, and the number of modes was 9999; afterwards, they carried out redocking in the most favorable regions using the AutoDock VinaXB extension. The finding in this study that came from molecular docking was that the results provided a clue to the question why only **TFHPQ** inhibited in the in vitro studies to NKA, while other analogues can bind in the TFHPQ binding pose but were less active despite that all of molecules have similar chemical structure because the free energies were different by 1–3 kcal/mol, and in addition, they can bind in several sites of the NKA enzyme being different for **TFHPQ**.

Another well application of VinaXB soring function is in the work developed by Enkhtaivan et al. [52] where they researched the ability of berberine-based derivatives (**BDs**) as antiinfluenza agents against the neuraminidase using the VinaXB scoring function finding that **BD-5** (**Figure 3b**) has better affinity energies than oseltamivir that was used as a control in the utilized neuraminidase receptor.

To study the XB interactions with this useful force field at the quantum calculation level, one of the key modifications to the original force field was the inclusion of the X-site term to refer the XC, XB and XI for chlorine-, bromine- and iodine-halogenated compounds being a OPLS-AAx as the new term for the general force field where this X-sites have a stretching bond bringing constants for angle bending except for the fluorine atom. On the other hand, in 2015 as well, Rappé et al. reported the creation of force field named force field for biological halogen bonds (*ffBXB*) that implemented the anisotropic effect of the σ-hole in the bromine atom [55]. In this force field, the calculations are performed based on the anisotropic structure-energy relationships, calorimetric data and ab initio calculations specifically in bromine; in addition, the result was consistent with a charge-dipole electrostatic potential that could calculate and predict properly the XB interaction. Finally, Zimmerman et al. reported in 2015 a development of a scoring function named XB scoring function (XBScore) that includes the force fields described above and the next parameters based in the study of each XB property: σ-hole score that includes the angle, interaction geometry, tuning effects, the interaction partner and the type of halogen [56]. The spherical score comes from the MP2/TZVPP theory level. At least, Zimmerman et al. concluded that using a quantum mechanics calculation they could predict energies with high accuracy and that based in their scoring function quantum mechanics derived, it is possible to apply this term to improve the docking experiments.

Molecular Docking in Halogen Bonding http://dx.doi.org/10.5772/intechopen.72994 109

One of the main objectives in computational medicinal chemistry is to generate useful predictions employing different tools that could be achieved through molecular modeling using computational approaches. This fact is very important during the implementation of strategies in the projects or protocols for drug development due to the different tasks and challenges in the quest of hit compounds. Molecular docking is an important part of this area bringing consistent advantages. It is a nice tool that decreases consuming time by allowing calculations with several compounds simultaneously, with the use of an appropriated scoring function, and including a suitable force field, the researcher could obtain positive results in many cases. Nevertheless, it is important to recognize that the halogenated compounds have no chemical behavior that is studied in most of docking programs; thus, it is necessary to take in account the scoring functions or force fields showed here if it is a need to carry out molecular docking with halogenated ligands. The concepts and fundamental aspects of XB are well known, their importance in the drug design and discovery processes, thereby, the non-covalent interactions involving halogens as Lewis acid donors and the Lewis base acceptors have become in an important issue during pharmacophore design suiting halogenated ligand or drugs using computational approaches and methods. Here, we have described the main aspects about the computational considerations, specifically in molecular docking because it remains the tool to investigate the type of ligand-receptor interactions, and the XB represents a challenge due to its electronic anisotropic effects that we need to define and select for the best scoring function to achieve accurate results and to predict good results about the interactions in the supramacromolecular chemistry leading to the improvement of some techniques and methods in

**8. Conclusions**

the computer-aided drug discovery field.

**Figure 3.** Compounds analyzed in recent drug design projects using molecular docking with XB scoring functions.

The other most representative studies are the use of VinaXB scoring function by Fusi et al. [53] where they investigated the block of the vascular Ca2+ channel by the PKA inhibitor **H-89** (N-[2- [[3-(4-bromophenyl)-2-propen-1-yl] amino] ethyl]-5-isoquinolinesulfonamide) (**Figure 3c**) and the compound named **(S)-(−)-Bay K 8644** (S)-(−)-Bay ((S)-(−)-methyl-1,4-dihydro-2,6 dimethyl-3-nitro-4-(2-trifluoromethylphenyl) pyridine-5-carboxylate) (**Figure 3d**) in rat artery myocytes. These docking experiments were carried out with a flexible docking in AutoDock with the VinaXB. The findings in this research established the differences between the poses of the analyzed compounds where the compounds positioned at the same binding region but in different binding pockets.

### **7. Quantum mechanics-derived scoring functions**

In a normal docking experiment, the atoms are described by an atom type and a partial charge that fails when we want to describe the characteristic of anisotropic electron distribution in XB. In 2012, Jorgensen and Schyman described the additional positive charge in the σ-hole region using their optimized potentials for liquid simulations-all atom (OPLS-AA) that is a force field applied to biological macromolecules [54]. This force field has the ability to predict thermodynamic and physical-chemical properties of biomolecules in aqueous phase with high accuracy for organic liquid compounds and for 20 neutral peptide residues that were investigated first by Monte Carlo simulations where intramolecular terms for bond stretches, angle bending and torsions, as well as the intermolecular and intramolecular nonbonded interactions were taken for the final calculations similar to AMBER or CHARMM force fields, for example, that represent electrostatic interactions with a single partial charge on each atom. To study the XB interactions with this useful force field at the quantum calculation level, one of the key modifications to the original force field was the inclusion of the X-site term to refer the XC, XB and XI for chlorine-, bromine- and iodine-halogenated compounds being a OPLS-AAx as the new term for the general force field where this X-sites have a stretching bond bringing constants for angle bending except for the fluorine atom. On the other hand, in 2015 as well, Rappé et al. reported the creation of force field named force field for biological halogen bonds (*ffBXB*) that implemented the anisotropic effect of the σ-hole in the bromine atom [55]. In this force field, the calculations are performed based on the anisotropic structure-energy relationships, calorimetric data and ab initio calculations specifically in bromine; in addition, the result was consistent with a charge-dipole electrostatic potential that could calculate and predict properly the XB interaction. Finally, Zimmerman et al. reported in 2015 a development of a scoring function named XB scoring function (XBScore) that includes the force fields described above and the next parameters based in the study of each XB property: σ-hole score that includes the angle, interaction geometry, tuning effects, the interaction partner and the type of halogen [56]. The spherical score comes from the MP2/TZVPP theory level. At least, Zimmerman et al. concluded that using a quantum mechanics calculation they could predict energies with high accuracy and that based in their scoring function quantum mechanics derived, it is possible to apply this term to improve the docking experiments.

## **8. Conclusions**

The other most representative studies are the use of VinaXB scoring function by Fusi et al. [53] where they investigated the block of the vascular Ca2+ channel by the PKA inhibitor **H-89** (N-[2- [[3-(4-bromophenyl)-2-propen-1-yl] amino] ethyl]-5-isoquinolinesulfonamide) (**Figure 3c**) and the compound named **(S)-(−)-Bay K 8644** (S)-(−)-Bay ((S)-(−)-methyl-1,4-dihydro-2,6 dimethyl-3-nitro-4-(2-trifluoromethylphenyl) pyridine-5-carboxylate) (**Figure 3d**) in rat artery myocytes. These docking experiments were carried out with a flexible docking in AutoDock with the VinaXB. The findings in this research established the differences between the poses of the analyzed compounds where the compounds positioned at the same binding region but

**Figure 3.** Compounds analyzed in recent drug design projects using molecular docking with XB scoring functions.

In a normal docking experiment, the atoms are described by an atom type and a partial charge that fails when we want to describe the characteristic of anisotropic electron distribution in XB. In 2012, Jorgensen and Schyman described the additional positive charge in the σ-hole region using their optimized potentials for liquid simulations-all atom (OPLS-AA) that is a force field applied to biological macromolecules [54]. This force field has the ability to predict thermodynamic and physical-chemical properties of biomolecules in aqueous phase with high accuracy for organic liquid compounds and for 20 neutral peptide residues that were investigated first by Monte Carlo simulations where intramolecular terms for bond stretches, angle bending and torsions, as well as the intermolecular and intramolecular nonbonded interactions were taken for the final calculations similar to AMBER or CHARMM force fields, for example, that represent electrostatic interactions with a single partial charge on each atom.

in different binding pockets.

108 Molecular Docking

**7. Quantum mechanics-derived scoring functions**

One of the main objectives in computational medicinal chemistry is to generate useful predictions employing different tools that could be achieved through molecular modeling using computational approaches. This fact is very important during the implementation of strategies in the projects or protocols for drug development due to the different tasks and challenges in the quest of hit compounds. Molecular docking is an important part of this area bringing consistent advantages. It is a nice tool that decreases consuming time by allowing calculations with several compounds simultaneously, with the use of an appropriated scoring function, and including a suitable force field, the researcher could obtain positive results in many cases. Nevertheless, it is important to recognize that the halogenated compounds have no chemical behavior that is studied in most of docking programs; thus, it is necessary to take in account the scoring functions or force fields showed here if it is a need to carry out molecular docking with halogenated ligands. The concepts and fundamental aspects of XB are well known, their importance in the drug design and discovery processes, thereby, the non-covalent interactions involving halogens as Lewis acid donors and the Lewis base acceptors have become in an important issue during pharmacophore design suiting halogenated ligand or drugs using computational approaches and methods. Here, we have described the main aspects about the computational considerations, specifically in molecular docking because it remains the tool to investigate the type of ligand-receptor interactions, and the XB represents a challenge due to its electronic anisotropic effects that we need to define and select for the best scoring function to achieve accurate results and to predict good results about the interactions in the supramacromolecular chemistry leading to the improvement of some techniques and methods in the computer-aided drug discovery field.

## **Author details**

Abel Suárez-Castro<sup>1</sup> \*, Mario Valle-Sánchez<sup>1</sup> , Carlos Jesús Cortés-García<sup>2</sup> and Luis Chacón-García<sup>1</sup>

\*Address all correspondence to: belypat@hotmail.com

1 Instituto de Investigaciones Químico-Biológicas, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, México

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**Author details**

110 Molecular Docking

Abel Suárez-Castro<sup>1</sup>

Luis Chacón-García<sup>1</sup>

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C3CP00054K

jm0500323

\*, Mario Valle-Sánchez<sup>1</sup>

\*Address all correspondence to: belypat@hotmail.com

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2017-09-01]

112 Molecular Docking


[49] Koebel MR, Schmadeke G, Posner RG, Sirimulla S. AutoDock VinaXB: Implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina. Journal of Cheminformatics. 2016;**8**:2-8. DOI: 10.1186/s13321-016-0139-1

**Chapter 7**

Provisional chapter

**A Combined Molecular Docking and Electronic**

A Combined Molecular Docking and Electronic

Linda-Lucila Landeros-Martinez,

Linda-Lucila Landeros-Martinez,

Erasmo Orrantia-Borunda and

Erasmo Orrantia-Borunda and

http://dx.doi.org/10.5772/intechopen.72895

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

with the N-desmethyl-tamoxifen metabolite.

bond, hormone receptors

Daniel Glossman-Mitnik,

Daniel Glossman-Mitnik,

Norma Flores-Holguin

Norma Flores-Holguin

Abstract

**Structure Study for a Breast Cancer Drug Design**

DOI: 10.5772/intechopen.72895

The molecular docking of tamoxifen's metabolites, 4-hydroxy-tamoxifen, N-desmethyltamoxifen, and 4-hydroxy-N-desmethyl-tamoxifen, in estrogen and progesterone hormone receptors was studied in aqueous solution. The metabolites 4-hydroxy-tamoxifen, N-desmethyl-tamoxifen, and 4-hydroxy-N-desmethyl-tamoxifen exhibit a binding energy in the estrogen receptor cavity of 10.69 kcal/mol, 10.9 kcal/mol, and 11.35 kcal/mol, respectively, and 1.45 kcal/mol, 9.29 kcal/mol, and 0.38 kcal/mol in the progesterone receptor. This indicates a spontaneous interaction between the metabolites and the active sites in the hormone receptors. Docking has an adequate accuracy for both receptors, and from this calculation the active site residues were defined for the different metabolites and the estrogen and progesterone receptors. Also, the chemical reactivity of the amino acids of the active sites of each metabolite was determined. These reactivity properties were obtained within the framework of density functional theory, using the functional M06 with the basis set 6-31G (d). The results indicate that in the estrogen receptor, the highest charge transfer of the three analyzed metabolites is in the union of the metabolite and the Leu346-Thr347 residue. The progesterone receptor shows minor tendency to react with higher hardness values than the estrogen receptor. The hydrogen bonds are three for the estrogen receptor in two different metabolites, while in progesterone only one is formed

Keywords: molecular docking, tamoxifen, binding energy, charge transfer, hydrogen

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Structure Study for a Breast Cancer Drug Design


#### **A Combined Molecular Docking and Electronic Structure Study for a Breast Cancer Drug Design** A Combined Molecular Docking and Electronic Structure Study for a Breast Cancer Drug Design

DOI: 10.5772/intechopen.72895

Linda-Lucila Landeros-Martinez, Daniel Glossman-Mitnik, Erasmo Orrantia-Borunda and Norma Flores-Holguin Linda-Lucila Landeros-Martinez, Daniel Glossman-Mitnik, Erasmo Orrantia-Borunda and Norma Flores-Holguin

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72895

#### Abstract

[49] Koebel MR, Schmadeke G, Posner RG, Sirimulla S. AutoDock VinaXB: Implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina. Journal of

[50] Pal D, kolluru V, Chandrasekaran B, Baby BV, Aman M, Suman S, Sirimulla S, Sanders MA, Alatassi H, Ankem MK, Damodaran C. Targeting aberrant expression of Notch-1 in ALDH cancer stem cells in breast cancer. Molecular Carcinogenesis. 2017;**56**:1127-1136.

[51] Šeflová J, Cechov P, Biler M, Hradil P, Kubala M. Inhibition of Naþ/Kþ-ATPase by 5,6,7,8-tetrafluoro-3-hydroxy-2-phenylquinolin-4(1H)-one. Biochimie. 2017;**138**:56-61.

[52] Enkhtaivan G, Muthuraman P, Kim DH, Mistry B. Discovery of berberine based derivatives as anti-influenza agent through blocking of neuraminidase. Bioorganic and

[53] Fusi F, Trezza A, Spiga O, Sgaragli G, Bova S. Cav1.2 channel current block by the PKA inhibitor H-89 in rat tail artery myocytes via a PKA-independent mechanism: Electrophysiological, functional, and molecular docking studies. Biochemical Pharma-

[54] Jorgensen WL, Schyman P. Treatment of halogen bonding in the OPLS-AA force field: Application to potent anti-HIV agents. Journal of Chemical Theory and Computation.

[55] Schilfield MR, Ford MC, Vander Zanden CM, Billman MM, Ho PS, Rappé AK. Force field model of periodic trends in biomolecular halogen bonds. Journal of Physical Chemistry

[56] Zimmermann MO, Lange A, Boeckler FM. Evaluating the potential of halogen bonding in molecular design: Automated scaffold decoration using the new scoring function XBScore. Journal of Chemical Information and Modeling. 2015;**55**(3):687-699. DOI:

Medicinal Chemistry. 2017;**25**:5185-5193. DOI: 10.1016/j.bmc.2017.07.006

cology. 2017;**140**:53-63. DOI: 10.1016/j.bcp.2017.05.020

2012;**8**:3895-3901. DOI: 10.1021/ct300180w

B. 2015;**119**(29):9140-9149. DOI: 10.1021/ip509003r

Cheminformatics. 2016;**8**:2-8. DOI: 10.1186/s13321-016-0139-1

DOI: 10.1002/mc.22579

114 Molecular Docking

10.1021/ci5007118

DOI: 10.1016/j.biochi.2017.04.009

The molecular docking of tamoxifen's metabolites, 4-hydroxy-tamoxifen, N-desmethyltamoxifen, and 4-hydroxy-N-desmethyl-tamoxifen, in estrogen and progesterone hormone receptors was studied in aqueous solution. The metabolites 4-hydroxy-tamoxifen, N-desmethyl-tamoxifen, and 4-hydroxy-N-desmethyl-tamoxifen exhibit a binding energy in the estrogen receptor cavity of 10.69 kcal/mol, 10.9 kcal/mol, and 11.35 kcal/mol, respectively, and 1.45 kcal/mol, 9.29 kcal/mol, and 0.38 kcal/mol in the progesterone receptor. This indicates a spontaneous interaction between the metabolites and the active sites in the hormone receptors. Docking has an adequate accuracy for both receptors, and from this calculation the active site residues were defined for the different metabolites and the estrogen and progesterone receptors. Also, the chemical reactivity of the amino acids of the active sites of each metabolite was determined. These reactivity properties were obtained within the framework of density functional theory, using the functional M06 with the basis set 6-31G (d). The results indicate that in the estrogen receptor, the highest charge transfer of the three analyzed metabolites is in the union of the metabolite and the Leu346-Thr347 residue. The progesterone receptor shows minor tendency to react with higher hardness values than the estrogen receptor. The hydrogen bonds are three for the estrogen receptor in two different metabolites, while in progesterone only one is formed with the N-desmethyl-tamoxifen metabolite.

Keywords: molecular docking, tamoxifen, binding energy, charge transfer, hydrogen bond, hormone receptors

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## 1. Introduction

Breast cancer is the leading cause of cancer death in women. A prognosis of breast cancer can be issued because there are parameters that predict the evolution or aggressiveness of the cancer, such as lymph nodes, tumor size, and histological grade of cancer [1–4]. In mammary cells there are hormone receptors (estrogen receptors (ERs) and progesterone receptors (PRs)) that function as "switches," activating or deactivating a particular function in the mammary cell.

functional theory (DFT) with functional B3LYP and BLYP with a basis set 6-311++G(2d, 2p) [12]. In addition, there was a reported analysis of the amount of charge transfer and the direction of the flow of charge of alkylating drugs in the presence of DNA bases allowing prediction among its bases of which one is the main target of these antitumor drugs [19].

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117

Another technique is molecular docking, which is a computational procedure that attempts to predict noncovalent binding of macromolecules (receptor) and small molecules (ligands) efficiently [20]. In detail, docking consists of an operation in which one molecule is brought into the vicinity of another while calculating the interaction energies of the many mutual orientations and conformations of the two interacting species. A docking procedure is used as a guide to identify the preferred orientation of one molecule relative to the other [21]. This method plays a key role in promoting fundamental biomolecular events such as enzyme–substrate, drug–protein, and drug–nucleic acid interactions [22]; it is also widely used in drug design [23]. Some authors have used the molecular docking of macromolecules to define the energy and bonding affinity in ER-α and ER-β with estrogen [24]. It has also been used in the analysis of a maltogenic amylase of Bacillus lehensis G1, which provides a view of the substrate and specificity in the macromolecule [25], and in the DNA docking analysis of natural products such as methyltransferase inhibitors, which have become an alternative for cancer therapies [26].

The objective of this research is to develop molecular docking of the metabolites of TAM with the macromolecules ER and PR, to obtain an active site of the hormone receptors. To perform computational protein–ligand docking experiments, a 3-D structure of the target protein at atomic resolution must be available. The most reliable sources are crystal and solution structures provided by the Protein Data Bank (PDB) [26, 27]. The hormone receptors selected for this work are the 1A52 ER-α ligand-binding domain complexed to estradiol, and the 1A28 hormone-bound human progesterone receptor ligand-binding domain. Both belong to the organism Homo sapiens and are present in breast cancer cells. Molecular docking has the advantage of working on a large scale, as well as determining the important sites of the macromolecule (active sites) [27, 28]. Once the active site is defined, an accurate calculation of electronic structure can be developed with methods such as DFT, which is the most popular, efficient, and versatile tool for obtaining precise information of molecular systems. For both receptors, the amino acids (residues) forming the active site were analyzed in an attempt to obtain their electronic properties such as ionization potential, electron affinity, electrophilicity, chemical hardness, chemical potential, and electronegativity. A

Molecular docking is calculated with the specially tailored software AutoDock 4.2 with the Lamarckian Genetic Algorithm (LGA) [28, 29] to explore how ER and PR bond with the metabolites. AutoDock uses a semiempirical free energy force field to predict binding free energies of

transfer and charge flow direction analysis was also performed.

2. Computational details

2.1. Molecular docking

Over the last two years a number of drugs have been developed with specific properties for the treatment of breast cancer. Fulvestrant is a steroid-based selective estrogen receptor downregulator (SERD) that antagonizes and degrades ER-α and is active in patients who have progressed to antihormonal agents [5]. Also, the selective ER modulators (SERMs)/SERD hybrids (SSHs) have been used to facilitate the first-line treatment for ER 1 degradation in breast cancer cells [6].

Another important piece of research by Srinivasan et al. presents the discovery of a series of SERDs lacking a prototypical side chain. This absence improves the mechanism called "indirect antagonism" [7]. The latest developments have found the optimal design of antiestrogen cores and side chains with the middle structures of the original SERMs class, such as tamoxifen (TAM), raloxifene, lasofoxifene, and bazedoxifene. Also, current studies of SERDs have been made by GlaxoSmithKline(GSK), Genentech, and AstraZeneca. In these studies the side chain is modified to a simple adamantyl core [8].

In addition, for several years, have been used antibodies as cancer drugs, and some examples are trastuzumab and pertuzumab, which are used in breast cancer as the only component. In fact, efforts have been made to use the antibodies conjugated with a variety of substances with the aim of improving their effect. Research on cancer therapy is still in progress [9].

TAM is a SERM [10, 11] and is used for the treatment of hormone receptors expressing breast cancer [12]. This drug is metabolized in the liver, producing three different metabolites: 4-hydroxy-tamoxifen (4OHTAM), N-desmethyl-tamoxifen (NDTAM), and 4-hydroxy-N-desmethyltamoxifen, also known as endoxifen (END) [13, 14]. These metabolites show a range of agonist and partial antagonist activities of ER-mediated effects [15]. In vivo studies have shown that TAM competes against estrogens to dock to the receptors, resulting in an attenuation of the cellular response measured by estrogen [16]. Therefore, the clinical response to TAM therapy will depend on the total effect of the resulting metabolites on the patient, their affinity for receptors, and their agonist/antagonist profile [15].

Recently, a number of theoretical studies on TAM and some of its active metabolites have described its interaction with ERs. Calculations of molecular dynamics have been used to model dynamic fluctuations in structures of ERs (ER-α following the binding to estradiol and the metabolite 4OHTAM) [17]. Recently, in an article written by the authors, the molecular docking of TAM in ER and PR was presented in which the active site of the hormone receptors was determined, as well as the charge transfer of the drug to the amino acids of the active sites of the receptors [18]. Other theoretical studies analyzed the chirality of TAM using density functional theory (DFT) with functional B3LYP and BLYP with a basis set 6-311++G(2d, 2p) [12]. In addition, there was a reported analysis of the amount of charge transfer and the direction of the flow of charge of alkylating drugs in the presence of DNA bases allowing prediction among its bases of which one is the main target of these antitumor drugs [19].

Another technique is molecular docking, which is a computational procedure that attempts to predict noncovalent binding of macromolecules (receptor) and small molecules (ligands) efficiently [20]. In detail, docking consists of an operation in which one molecule is brought into the vicinity of another while calculating the interaction energies of the many mutual orientations and conformations of the two interacting species. A docking procedure is used as a guide to identify the preferred orientation of one molecule relative to the other [21]. This method plays a key role in promoting fundamental biomolecular events such as enzyme–substrate, drug–protein, and drug–nucleic acid interactions [22]; it is also widely used in drug design [23]. Some authors have used the molecular docking of macromolecules to define the energy and bonding affinity in ER-α and ER-β with estrogen [24]. It has also been used in the analysis of a maltogenic amylase of Bacillus lehensis G1, which provides a view of the substrate and specificity in the macromolecule [25], and in the DNA docking analysis of natural products such as methyltransferase inhibitors, which have become an alternative for cancer therapies [26].

The objective of this research is to develop molecular docking of the metabolites of TAM with the macromolecules ER and PR, to obtain an active site of the hormone receptors. To perform computational protein–ligand docking experiments, a 3-D structure of the target protein at atomic resolution must be available. The most reliable sources are crystal and solution structures provided by the Protein Data Bank (PDB) [26, 27]. The hormone receptors selected for this work are the 1A52 ER-α ligand-binding domain complexed to estradiol, and the 1A28 hormone-bound human progesterone receptor ligand-binding domain. Both belong to the organism Homo sapiens and are present in breast cancer cells. Molecular docking has the advantage of working on a large scale, as well as determining the important sites of the macromolecule (active sites) [27, 28]. Once the active site is defined, an accurate calculation of electronic structure can be developed with methods such as DFT, which is the most popular, efficient, and versatile tool for obtaining precise information of molecular systems. For both receptors, the amino acids (residues) forming the active site were analyzed in an attempt to obtain their electronic properties such as ionization potential, electron affinity, electrophilicity, chemical hardness, chemical potential, and electronegativity. A transfer and charge flow direction analysis was also performed.

## 2. Computational details

#### 2.1. Molecular docking

1. Introduction

116 Molecular Docking

mammary cell.

breast cancer cells [6].

is modified to a simple adamantyl core [8].

receptors, and their agonist/antagonist profile [15].

Breast cancer is the leading cause of cancer death in women. A prognosis of breast cancer can be issued because there are parameters that predict the evolution or aggressiveness of the cancer, such as lymph nodes, tumor size, and histological grade of cancer [1–4]. In mammary cells there are hormone receptors (estrogen receptors (ERs) and progesterone receptors (PRs)) that function as "switches," activating or deactivating a particular function in the

Over the last two years a number of drugs have been developed with specific properties for the treatment of breast cancer. Fulvestrant is a steroid-based selective estrogen receptor downregulator (SERD) that antagonizes and degrades ER-α and is active in patients who have progressed to antihormonal agents [5]. Also, the selective ER modulators (SERMs)/SERD hybrids (SSHs) have been used to facilitate the first-line treatment for ER 1 degradation in

Another important piece of research by Srinivasan et al. presents the discovery of a series of SERDs lacking a prototypical side chain. This absence improves the mechanism called "indirect antagonism" [7]. The latest developments have found the optimal design of antiestrogen cores and side chains with the middle structures of the original SERMs class, such as tamoxifen (TAM), raloxifene, lasofoxifene, and bazedoxifene. Also, current studies of SERDs have been made by GlaxoSmithKline(GSK), Genentech, and AstraZeneca. In these studies the side chain

In addition, for several years, have been used antibodies as cancer drugs, and some examples are trastuzumab and pertuzumab, which are used in breast cancer as the only component. In fact, efforts have been made to use the antibodies conjugated with a variety of substances with the

TAM is a SERM [10, 11] and is used for the treatment of hormone receptors expressing breast cancer [12]. This drug is metabolized in the liver, producing three different metabolites: 4-hydroxy-tamoxifen (4OHTAM), N-desmethyl-tamoxifen (NDTAM), and 4-hydroxy-N-desmethyltamoxifen, also known as endoxifen (END) [13, 14]. These metabolites show a range of agonist and partial antagonist activities of ER-mediated effects [15]. In vivo studies have shown that TAM competes against estrogens to dock to the receptors, resulting in an attenuation of the cellular response measured by estrogen [16]. Therefore, the clinical response to TAM therapy will depend on the total effect of the resulting metabolites on the patient, their affinity for

Recently, a number of theoretical studies on TAM and some of its active metabolites have described its interaction with ERs. Calculations of molecular dynamics have been used to model dynamic fluctuations in structures of ERs (ER-α following the binding to estradiol and the metabolite 4OHTAM) [17]. Recently, in an article written by the authors, the molecular docking of TAM in ER and PR was presented in which the active site of the hormone receptors was determined, as well as the charge transfer of the drug to the amino acids of the active sites of the receptors [18]. Other theoretical studies analyzed the chirality of TAM using density

aim of improving their effect. Research on cancer therapy is still in progress [9].

Molecular docking is calculated with the specially tailored software AutoDock 4.2 with the Lamarckian Genetic Algorithm (LGA) [28, 29] to explore how ER and PR bond with the metabolites. AutoDock uses a semiempirical free energy force field to predict binding free energies of small molecules to macromolecule targets [29]. The force field is based on a comprehensive thermodynamic model that allows incorporation of intramolecular energies into the predicted free energy of binding. It also incorporates a charge-based method for evaluation of desolvation designed to use a typical set of atom types [30]. The use of LGA allows individual conformations to search their local conformational space, find local minima, and then pass this information to later generations [29]; also LGA can handle ligands with more degrees of freedom and is efficient, reliable, and successful [31].

The chemical reactivity descriptors of the studied molecular systems were calculated using the DFT conceptual framework. These parameters include ionization potential (I), electron affinity (EA), chemical hardness (η) [41], electronegativity (χ) [41], electrophilicity (ω) [42], and chemical potential (μ) [42]. The overall interaction between metabolites and the amino acids that make up the active site on ER and PR can be identified by the charge transfer. This parameter determines the behavior of the different molecular systems as a donor or as an acceptor system. In this case, the electrons transferred from the metabolites to the amino acids of the active site of receptors or vice versa. The global interactions between two constituents can been

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The equations of the reactivity and charge transfer descriptors are shown in Table 1.

Validation docking was performed for each hormone receptor using the PyMOL program [44]. Figure 1 shows the structure of the native co-crystallized TAM bond and its metabolites. The root mean square deviation (RMSD) between TAM and the metabolites was calculated for each

determined using the charge transfer parameter (ΔN) [43].

3. Results and discussion

Figure 1. Chemical structures of tamoxifen and metabolites.

3.1. Validation docking

The water molecules in the receivers are eliminated and only the polar H atoms are added. The docking area is selected by constructing a grid box, size 52 � 36 � 34 points, centered at x, y, and z coordinates of 89.304, 14.745, and 70.512, respectively, for ER, and a grid box, size 20 � 18 � 26 points, centered at x, y, and z coordinates of 36.999, 31.767, and 42.694, respectively, for PR using in both receptors a grid spacing of 0.375 Å in AutoGrid [28, 29]. The docking parameters used for the LGA-based conformational searches are: docking trials—150; population size—150; maximum number of energy evaluations—25,000,000; maximum number of top individuals to survive to next generation—1; rate of gene mutation—0.02; rate of crossover —0.8; mean of Cauchy distribution for gene mutation—0.0; variance of Cauchy distribution for gene mutation—1.0; and number of generations for picking the worst individual—10.

#### 2.2. Electronic structure calculations

The energy calculations of the amino acids that make up the active site on ER, PR, and TAM metabolites are calculated with the functional hybrid meta-GGA M06 [32, 33] developed by the Truhlar Group from the University of Minnesota, combined with the basis set 6-31G (d) proposed by Pople [34] and the conductor-like polarizable continuum model (CPCM) [35] using water as a solvent. All calculations were made using DFT [35–38] with the Gaussian program 09 [39]. The charge distribution for amino acids and metabolites was obtained with the population analysis of Hirshfeld charges [40].


Table 1. Global reactivity and charge transfer parameters.

The chemical reactivity descriptors of the studied molecular systems were calculated using the DFT conceptual framework. These parameters include ionization potential (I), electron affinity (EA), chemical hardness (η) [41], electronegativity (χ) [41], electrophilicity (ω) [42], and chemical potential (μ) [42]. The overall interaction between metabolites and the amino acids that make up the active site on ER and PR can be identified by the charge transfer. This parameter determines the behavior of the different molecular systems as a donor or as an acceptor system. In this case, the electrons transferred from the metabolites to the amino acids of the active site of receptors or vice versa. The global interactions between two constituents can been determined using the charge transfer parameter (ΔN) [43].

The equations of the reactivity and charge transfer descriptors are shown in Table 1.

## 3. Results and discussion

#### 3.1. Validation docking

small molecules to macromolecule targets [29]. The force field is based on a comprehensive thermodynamic model that allows incorporation of intramolecular energies into the predicted free energy of binding. It also incorporates a charge-based method for evaluation of desolvation designed to use a typical set of atom types [30]. The use of LGA allows individual conformations to search their local conformational space, find local minima, and then pass this information to later generations [29]; also LGA can handle ligands with more degrees of freedom and is

The water molecules in the receivers are eliminated and only the polar H atoms are added. The docking area is selected by constructing a grid box, size 52 � 36 � 34 points, centered at x, y, and z coordinates of 89.304, 14.745, and 70.512, respectively, for ER, and a grid box, size 20 � 18 � 26 points, centered at x, y, and z coordinates of 36.999, 31.767, and 42.694, respectively, for PR using in both receptors a grid spacing of 0.375 Å in AutoGrid [28, 29]. The docking parameters used for the LGA-based conformational searches are: docking trials—150; population size—150; maximum number of energy evaluations—25,000,000; maximum number of top individuals to survive to next generation—1; rate of gene mutation—0.02; rate of crossover —0.8; mean of Cauchy distribution for gene mutation—0.0; variance of Cauchy distribution for

gene mutation—1.0; and number of generations for picking the worst individual—10.

The energy calculations of the amino acids that make up the active site on ER, PR, and TAM metabolites are calculated with the functional hybrid meta-GGA M06 [32, 33] developed by the Truhlar Group from the University of Minnesota, combined with the basis set 6-31G (d) proposed by Pople [34] and the conductor-like polarizable continuum model (CPCM) [35] using water as a solvent. All calculations were made using DFT [35–38] with the Gaussian program 09 [39]. The charge distribution for amino acids and metabolites was obtained with

Equations

<sup>η</sup> <sup>¼</sup> <sup>ð</sup><sup>I</sup> � AE<sup>Þ</sup>

<sup>χ</sup> <sup>¼</sup> <sup>ð</sup><sup>I</sup> <sup>þ</sup> AE<sup>Þ</sup>

<sup>ω</sup> <sup>¼</sup> <sup>μ</sup><sup>2</sup>

<sup>Δ</sup><sup>N</sup> <sup>¼</sup> <sup>μ</sup><sup>B</sup> � <sup>μ</sup><sup>A</sup>

<sup>2</sup> (1)

<sup>2</sup> (2)

<sup>2</sup><sup>η</sup> (3)

μ ¼ �χ (4)

<sup>2</sup>ðη<sup>A</sup> <sup>þ</sup> <sup>η</sup>B<sup>Þ</sup> (5)

efficient, reliable, and successful [31].

118 Molecular Docking

2.2. Electronic structure calculations

the population analysis of Hirshfeld charges [40].

Table 1. Global reactivity and charge transfer parameters.

Validation docking was performed for each hormone receptor using the PyMOL program [44]. Figure 1 shows the structure of the native co-crystallized TAM bond and its metabolites. The root mean square deviation (RMSD) between TAM and the metabolites was calculated for each

Figure 1. Chemical structures of tamoxifen and metabolites.

of the hormone receptor dockings. An RMSD value is considered a measurement of the accuracy of the docking results. The optimal position is recognized if the RMSD value is less than 2 Å [45]. In the case of metabolite dockings, TAM was used as the template for molecular overlap, as it is known that this drug is metabolized into the metabolites analyzed in this study. The metabolites were aligned by rotation and translation to obtain the RMSD using the "Align" option in PyMOL. Therefore, the RMSD in ER obtained between TAM with 4OHTAM, END, and NDTAM is 0.672, 1.106, and 1.461, respectively. For PR the RMSD obtained between TAM and 4OHTAM, END, and NDTAM is 1.387, 2.006, and 0.953, respectively. Figure 2 shows the alignment between TAM (black) and 4OHTAM, END, and NDTAM (gray).

3.2.1. Molecular docking

3.2.2. Active site

by Shiau et al. [48].

The binding energy of the metabolites with the ER active site was predicted with molecular docking calculations. The negative value of the binding energy (affinity) in the docking indicates that the system is stable and that there is an interaction between ER and the metabolites in the active site: 10.69 kcal/mol for 4OHTAM, 11.35 kcal/mol for END, and 10.90 kcal/ mol for NDTAM. It was observed that the binding affinity was lower in 4OHTAM; this is due to the effect of the orientation of the metabolite within the active site caused by the influence of

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Finally, the binding energy shows that END, which exhibits 11.35 kcal/mol, is the metabolite with the highest affinity with the active site. It even shows a better affinity than TAM at 10.38 kcal/mol [18]. This coincides with previous information reported by Clarke [46] who says that END has an affinity for ERs higher than NDTAM or TAM itself. As can be observed, all the metabolites have a high affinity to the receptor. According with Gareth [47], the greater the affinity of the ligand for the receptor, the more easily it binds to that receptor. This is important because the binding of a drug to a receptor stimulates the physiological response that characterizes the action of the drug, which means that release of a series of biochemical events results

The schematic structure of the active site and the binding energies are shown in Figure 3.

The conformational coupling of the active site with each metabolite is described below.

4OHTAM. There are 14 residues in contact with the metabolite 4OHTAM at the active site of the ER. Nine of them are linked forming an amino acids sequence: leucine346-threonine347 (Leu346- Thr347), tryptophan383-leucine384 (Trp383-Leu384), glutamic acid353-leucine354 (Glu353- Leu354), and leucine349-alanine350-aspartic acid351 (Leu349-Ala350-Asp351). The other five are: glycine residue (Gly521)—glycine is the smallest of the amino acids. It is ambivalent, which means that the amino acid can be inside or outside of the protein molecule; lysine529 (Lys529) this residue contains a protonated amino group that provides a positive charge to proteins as acetyltransferases; histidine524 (His524)—this residue has a positively charged imidazole functional group. This group participates in enzyme-catalyzed reactions; phenylalanine404 (Phe404) —an essential amino acid, it is a derivative of alanine with a phenyl substituent on the β carbon. Due to its hydrophobicity, phenylalanine is nearly always found buried within a protein. The π electrons of the phenyl ring can stack with other aromatic systems and often do so within folded proteins, adding stability to the structure; and finally, methionine388 (Met 388), which has a hydrophobic thiol ether in its lateral chain. According to the results obtained by theoretical calculations, in the metabolite 4OHTAM the active site of estrogen coincides with that reported

END. The active site of END is formed by the following residues: leucine346-threonine347 (Leu346- Thr347), leucine387-methionine388 (Leu387-Met388), tryptophan383-leucine384 (Trp383-Leu384), glycine521 (Gly521), and histidine524 (His524). The last two residues are highly hydrophilic.

the tertiary amine functional group containing the 4OHTAM.

in a biological or pharmacological effect [47].

#### 3.2. Analysis of the estrogen receptor with the metabolites

An analysis of molecular docking of the metabolites in ER was carried out, revealing the active site of the ER, followed by its description, the analysis of the chemical reactivity parameters of the residues and the metabolites, as well as the description of the hydrogen bonds between the metabolites and the ER active site.

Figure 2. Conformation of tamoxifen and metabolites after docking in hormone receptors.

#### 3.2.1. Molecular docking

of the hormone receptor dockings. An RMSD value is considered a measurement of the accuracy of the docking results. The optimal position is recognized if the RMSD value is less than 2 Å [45]. In the case of metabolite dockings, TAM was used as the template for molecular overlap, as it is known that this drug is metabolized into the metabolites analyzed in this study. The metabolites were aligned by rotation and translation to obtain the RMSD using the "Align" option in PyMOL. Therefore, the RMSD in ER obtained between TAM with 4OHTAM, END, and NDTAM is 0.672, 1.106, and 1.461, respectively. For PR the RMSD obtained between TAM and 4OHTAM, END, and NDTAM is 1.387, 2.006, and 0.953, respectively. Figure 2 shows

An analysis of molecular docking of the metabolites in ER was carried out, revealing the active site of the ER, followed by its description, the analysis of the chemical reactivity parameters of the residues and the metabolites, as well as the description of the hydrogen bonds between the

the alignment between TAM (black) and 4OHTAM, END, and NDTAM (gray).

3.2. Analysis of the estrogen receptor with the metabolites

Figure 2. Conformation of tamoxifen and metabolites after docking in hormone receptors.

metabolites and the ER active site.

120 Molecular Docking

The binding energy of the metabolites with the ER active site was predicted with molecular docking calculations. The negative value of the binding energy (affinity) in the docking indicates that the system is stable and that there is an interaction between ER and the metabolites in the active site: 10.69 kcal/mol for 4OHTAM, 11.35 kcal/mol for END, and 10.90 kcal/ mol for NDTAM. It was observed that the binding affinity was lower in 4OHTAM; this is due to the effect of the orientation of the metabolite within the active site caused by the influence of the tertiary amine functional group containing the 4OHTAM.

Finally, the binding energy shows that END, which exhibits 11.35 kcal/mol, is the metabolite with the highest affinity with the active site. It even shows a better affinity than TAM at 10.38 kcal/mol [18]. This coincides with previous information reported by Clarke [46] who says that END has an affinity for ERs higher than NDTAM or TAM itself. As can be observed, all the metabolites have a high affinity to the receptor. According with Gareth [47], the greater the affinity of the ligand for the receptor, the more easily it binds to that receptor. This is important because the binding of a drug to a receptor stimulates the physiological response that characterizes the action of the drug, which means that release of a series of biochemical events results in a biological or pharmacological effect [47].

The schematic structure of the active site and the binding energies are shown in Figure 3.

### 3.2.2. Active site

The conformational coupling of the active site with each metabolite is described below.

4OHTAM. There are 14 residues in contact with the metabolite 4OHTAM at the active site of the ER. Nine of them are linked forming an amino acids sequence: leucine346-threonine347 (Leu346- Thr347), tryptophan383-leucine384 (Trp383-Leu384), glutamic acid353-leucine354 (Glu353- Leu354), and leucine349-alanine350-aspartic acid351 (Leu349-Ala350-Asp351). The other five are: glycine residue (Gly521)—glycine is the smallest of the amino acids. It is ambivalent, which means that the amino acid can be inside or outside of the protein molecule; lysine529 (Lys529) this residue contains a protonated amino group that provides a positive charge to proteins as acetyltransferases; histidine524 (His524)—this residue has a positively charged imidazole functional group. This group participates in enzyme-catalyzed reactions; phenylalanine404 (Phe404) —an essential amino acid, it is a derivative of alanine with a phenyl substituent on the β carbon. Due to its hydrophobicity, phenylalanine is nearly always found buried within a protein. The π electrons of the phenyl ring can stack with other aromatic systems and often do so within folded proteins, adding stability to the structure; and finally, methionine388 (Met 388), which has a hydrophobic thiol ether in its lateral chain. According to the results obtained by theoretical calculations, in the metabolite 4OHTAM the active site of estrogen coincides with that reported by Shiau et al. [48].

END. The active site of END is formed by the following residues: leucine346-threonine347 (Leu346- Thr347), leucine387-methionine388 (Leu387-Met388), tryptophan383-leucine384 (Trp383-Leu384), glycine521 (Gly521), and histidine524 (His524). The last two residues are highly hydrophilic.

3.2.3. Chemical reactivity

Table 2.

Once the most stable structure of the active site of TAM's metabolites were defined, an analysis of the reactivity of ER residues was performed using descriptors such as ionization potential (I), electron affinity (EA), chemical potential (μ), chemical hardness (η), and electrophilicity (ω). Calculated results for the reactivity parameters of the drug and residues of the ER are shown in

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The electron affinities of the residues fluctuate from 0.21 eV to 0.91 eV. The highest value of electron affinity is for the Trp383-Leu384 residue, which is present in the active site of the three different metabolites analyzed in this work. According to the ionization potential results, the residue with the greatest possibility of losing electrons is Leu346-Thr347 with 7.74 eV. This

Metabolite Active site EA (eV) I (eV) hη(eV) μ = –χ (eV) ω (eV) 4OHTAM Gly521 0.21 7.03 3.41 3.62 1.92

END Gly521 0.21 7.03 3.41 3.62 1.92

NDTAM Gly521 0.21 7.03 3.41 3.62 1.92

Table 2. Parameters of chemical reactivity of the active site residues of the estrogen receptor.

Met388 0.46 6.11 2.82 2.39 1.91 His524 0.43 6.2 2.89 3.31 1.9 Lys529 0.83 7.22 3.19 4.02 2.54 Phe404 0.51 6.4 2.95 3.46 2.03 Trp383-Leu384 0.91 6.04 2.56 3.47 2.35 Glu353-Leu354 0.66 5.59 2.47 3.13 1.98 Leu346-Thr347 0.88 7.74 3.43 4.31 2.71 Leu349-Ala350-Asp351 0.73 5.79 2.53 3.26 2.1

His524 0.43 6.2 2.89 3.31 1.9 Leu387-Met388 0.51 6.25 2.87 3.38 1.99 Leu346-Thr347 0.88 7.74 3.43 4.31 2.71 Trp383-Leu384 0.91 5.79 2.53 3.26 2.1

Phe404 0.51 6.4 2.95 3.46 2.03 Glu353 0.2 5.62 2.71 2.91 1.57 Leu428 0.47 7 3.23 3.73 2.14 His524-Leu525 0.81 6.1 2.65 3.46 2.25 Trp383-Leu384 0.91 6.04 2.56 3.47 2.35 Leu346-Thr347 0.88 7.74 3.43 4.31 2.71 Leu349-Ala350-Asp351 0.73 5.79 2.53 3.26 2.1

residue is present in the active site of the three metabolites.

Figure 3. Amino acids of the active site of the estrogen receptor with (A) 4OHTAM, (B) END, and (C) NDTAM.

NDTAM. The active site in NDTAM consists of the following residues: leucine346-threonine347 (Leu346-Thr347), histidine524-leucine525 (His524-Leu525), tryptophan383-leucine384 (Trp383-Leu384), and (Leu349-Ala350-Asp351),and hydrophilic residue glycine521 (Gly521) and hydrophobic residues phenylalanine404 (Phe404), glutamic acid (Glu353), and leucine428 (Leu428).

Most of the residues are situated over the planar core of the ligand. The others surround the functional groups amine and hydroxyl. These interactions contribute to binding energies of up to 10 kcal/mol.

The metabolites act by blocking the activation domain AF-2 of ER found in the ligand bond domain or LBD of the active site. Therefore, the metabolites act as estrogen antagonists over the genes that require only the activation domain AF-2 [49, 50].

The residues for the metabolites in ER are shown in Figure 3.

#### 3.2.3. Chemical reactivity

NDTAM. The active site in NDTAM consists of the following residues: leucine346-threonine347 (Leu346-Thr347), histidine524-leucine525 (His524-Leu525), tryptophan383-leucine384 (Trp383-Leu384), and (Leu349-Ala350-Asp351),and hydrophilic residue glycine521 (Gly521) and hydrophobic residues phenylalanine404 (Phe404), glutamic acid (Glu353), and leucine428

Figure 3. Amino acids of the active site of the estrogen receptor with (A) 4OHTAM, (B) END, and (C) NDTAM.

Most of the residues are situated over the planar core of the ligand. The others surround the functional groups amine and hydroxyl. These interactions contribute to binding energies of up

The metabolites act by blocking the activation domain AF-2 of ER found in the ligand bond domain or LBD of the active site. Therefore, the metabolites act as estrogen antagonists over

the genes that require only the activation domain AF-2 [49, 50]. The residues for the metabolites in ER are shown in Figure 3.

(Leu428).

122 Molecular Docking

to 10 kcal/mol.

Once the most stable structure of the active site of TAM's metabolites were defined, an analysis of the reactivity of ER residues was performed using descriptors such as ionization potential (I), electron affinity (EA), chemical potential (μ), chemical hardness (η), and electrophilicity (ω). Calculated results for the reactivity parameters of the drug and residues of the ER are shown in Table 2.

The electron affinities of the residues fluctuate from 0.21 eV to 0.91 eV. The highest value of electron affinity is for the Trp383-Leu384 residue, which is present in the active site of the three different metabolites analyzed in this work. According to the ionization potential results, the residue with the greatest possibility of losing electrons is Leu346-Thr347 with 7.74 eV. This residue is present in the active site of the three metabolites.


Table 2. Parameters of chemical reactivity of the active site residues of the estrogen receptor.

Chemical hardness ranges from 2.53 eV to 3.43 eV; this parameter measures the resistance to change in the electronic configuration. The Glu353-Leu354 residue with 2.47 eV will react more easily in the presence of 4OHTAM, the Leu349-Ala350-Asp351 residue with 2.53 eV will react more easily in the presence of NDTAM, and the Trp383-Leu384 residue with 2.56 eV will react more easily in the presence of END. The chemical potential (μ = χ) represents the average effect between the tendency among molecules to attract and transfer electrons. This parameter is an important part in the description of the charge transfer descriptor. The electronegativity shows that the Leu346-Thr347 residue has the greatest tendency to attract electrons with 4.31 eV. This trend is repeated with the three different metabolites. Electrophilicity ω represents the stabilization energy of the systems when it becomes saturated with electrons coming from the surroundings. In this case, in the active site of 4OHTAM, value decreases in the following order: Leu346-Thr347 > Lys529 > Trp383-Leu384 > Leu349-Ala350-Asp351 > Phe404 > Glu353-Leu354 > Gly521 > Met388 > His524. In END the decreasing order is Leu346-Thr347 > Trp383-Leu384 > Leu387-Met388 > Gly521 > His524 and in NDTAM the decreasing order is Leu346-Thr347 > Trp383-Leu384 > His524-Leu525 > Leu428 > Leu349-Ala350-Asp351 > Phe404 > Gly521 > Glu353.

#### 3.2.4. Charge transfer descriptor

The chemical reactivity descriptors mentioned above are intramolecular parameters, whose values are calculated from the electronic properties of the molecule. To understand a chemical reaction in depth an intermolecular parameter that represents the fractional number of electrons transferred from one system to another should also be considered. This parameter is called charge transfer and is described as Eq. 5 in Table 1. In this formula, μA is TAM's metabolites and μB is the chemical potential for the residues of the active site. ηA, ηB represent the chemical hardness of TAM's metabolites and its residues of the active site, respectively [43]. The significance of these kinds of interactions lies in the fact that they are the primary directors of specificity, rate control, and reversibility in many biochemical reactions. Furthermore, it represents a first step in understanding oxidative damage in the active site produced by the TAM's metabolites and leads to identify their functioning and biological activity. Some authors use charge transfer to describe the oxidative damage of DNA bases [51, 52].

The interpretation of the value ΔN is as follows: for ΔN < 0 the charge flows from A to B (A acts as an electron donor). For ΔN > 0 the charge flows from B to A (A acts as an electron acceptor). Therefore, in the presence of Glu353-Leu354, Leu349-Ala350-Asp351, and His524 residues, ΔN of 4OHTAM accepts electrons, while for the rest of the residues it acts as an electron donor. END is an electron acceptor in the presence of the Trp383-Leu384 residue and with the remainder of the residues it acts as an electron donor. Finally, NDTAM acts as an electron acceptor in the presence of Glu353 and Leu349-Ala 350-Asp351 residues, and as an electron donor with the remainder of the residues. The values are shown in Table 3.

3.2.5. Electrostatics interactions

metabolites was done. The results are as follows.

Table 3. Charge transfer descriptor in the estrogen receptor.

residue Trp383 and the planar core of the ligand.

Other noncovalent interactions between the ligand and hormone receptor are the hydrogen bond and π–π interactions. An analysis of these bonds between the ER and each of TAM's

Metabolite Residue ΔN 4OHTAM Gly521 0.022

NDTAM Gly521 0.016

END Gly521 0.029

Met388 0.089 His524 0.004 Lys529 0.058 Phe404 0.010 Trp383-Leu384 0.012 Glu353-Leu354 0.022 Leu346-Thr347 0.080 Leu349-Ala350-Asp351 0.009

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A Combined Molecular Docking and Electronic Structure Study for a Breast Cancer Drug Design

Phe404 0.004 Glu353 0.047 Leu428 0.026 His524-Lue525 0.004 Trp383-Leu384 0.005 Leu346-Thr347 0.073 Leu349-Ala350-Asp351 0.015

His524 0.004 Leu387-Met388 0.010 Leu346-Thr347 0.086 Trp383-Leu384 0.001

4OHTAM. This residue has one hydrogen bond (C=O----O-H) between the donor group (O-H) and the acceptor group (C=O) of the Gly521 residue. Also, there is a π–π interaction between

END. There are two hydrogen bonds: first (C=O----OH) between the acceptor group (C=O) of Gly521 and the donor group (O-H) belonging to one of the rings and second (C=O----HN) between the accepting group (C=O) of Asp351 and the secondary amine (NH). The π–π

interaction was found among residue Trp383 and the planar core of the ligand.

The charge transfer descriptor is one of the noncovalent interactions that are present in biological systems in a macromolecule–ligand complex. In this case, the highest charge transfer value is in the same residue, Leu346-Thr347, for all the metabolites, which acts as a donor with amounts of 0.080, 0.086, and –0.073 for 4OHTAM, END, and NDTAM, respectively. Therefore, oxidative damage in the active site decreases in the order 4OHTAM > NDTAM > END.

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Table 3. Charge transfer descriptor in the estrogen receptor.

#### 3.2.5. Electrostatics interactions

Chemical hardness ranges from 2.53 eV to 3.43 eV; this parameter measures the resistance to change in the electronic configuration. The Glu353-Leu354 residue with 2.47 eV will react more easily in the presence of 4OHTAM, the Leu349-Ala350-Asp351 residue with 2.53 eV will react more easily in the presence of NDTAM, and the Trp383-Leu384 residue with 2.56 eV will react more easily in the presence of END. The chemical potential (μ = χ) represents the average effect between the tendency among molecules to attract and transfer electrons. This parameter is an important part in the description of the charge transfer descriptor. The electronegativity shows that the Leu346-Thr347 residue has the greatest tendency to attract electrons with 4.31 eV. This trend is repeated with the three different metabolites. Electrophilicity ω represents the stabilization energy of the systems when it becomes saturated with electrons coming from the surroundings. In this case, in the active site of 4OHTAM, value decreases in the following order: Leu346-Thr347 > Lys529 > Trp383-Leu384 > Leu349-Ala350-Asp351 > Phe404 > Glu353-Leu354 > Gly521 > Met388 > His524. In END the decreasing order is Leu346-Thr347 > Trp383-Leu384 > Leu387-Met388 > Gly521 > His524 and in NDTAM the decreasing order is Leu346-Thr347 > Trp383-Leu384 > His524-Leu525 > Leu428 > Leu349-Ala350-Asp351 > Phe404 > Gly521 > Glu353.

The chemical reactivity descriptors mentioned above are intramolecular parameters, whose values are calculated from the electronic properties of the molecule. To understand a chemical reaction in depth an intermolecular parameter that represents the fractional number of electrons transferred from one system to another should also be considered. This parameter is called charge transfer and is described as Eq. 5 in Table 1. In this formula, μA is TAM's metabolites and μB is the chemical potential for the residues of the active site. ηA, ηB represent the chemical hardness of TAM's metabolites and its residues of the active site, respectively [43]. The significance of these kinds of interactions lies in the fact that they are the primary directors of specificity, rate control, and reversibility in many biochemical reactions. Furthermore, it represents a first step in understanding oxidative damage in the active site produced by the TAM's metabolites and leads to identify their functioning and biological activity. Some authors

The interpretation of the value ΔN is as follows: for ΔN < 0 the charge flows from A to B (A acts as an electron donor). For ΔN > 0 the charge flows from B to A (A acts as an electron acceptor). Therefore, in the presence of Glu353-Leu354, Leu349-Ala350-Asp351, and His524 residues, ΔN of 4OHTAM accepts electrons, while for the rest of the residues it acts as an electron donor. END is an electron acceptor in the presence of the Trp383-Leu384 residue and with the remainder of the residues it acts as an electron donor. Finally, NDTAM acts as an electron acceptor in the presence of Glu353 and Leu349-Ala 350-Asp351 residues, and as an electron

The charge transfer descriptor is one of the noncovalent interactions that are present in biological systems in a macromolecule–ligand complex. In this case, the highest charge transfer value is in the same residue, Leu346-Thr347, for all the metabolites, which acts as a donor with amounts of 0.080, 0.086, and –0.073 for 4OHTAM, END, and NDTAM, respectively. Therefore, oxidative damage in the active site decreases in the order 4OHTAM > NDTAM > END.

use charge transfer to describe the oxidative damage of DNA bases [51, 52].

donor with the remainder of the residues. The values are shown in Table 3.

3.2.4. Charge transfer descriptor

124 Molecular Docking

Other noncovalent interactions between the ligand and hormone receptor are the hydrogen bond and π–π interactions. An analysis of these bonds between the ER and each of TAM's metabolites was done. The results are as follows.

4OHTAM. This residue has one hydrogen bond (C=O----O-H) between the donor group (O-H) and the acceptor group (C=O) of the Gly521 residue. Also, there is a π–π interaction between residue Trp383 and the planar core of the ligand.

END. There are two hydrogen bonds: first (C=O----OH) between the acceptor group (C=O) of Gly521 and the donor group (O-H) belonging to one of the rings and second (C=O----HN) between the accepting group (C=O) of Asp351 and the secondary amine (NH). The π–π interaction was found among residue Trp383 and the planar core of the ligand.

NDTAM. In this metabolite was found one hydrogen bond (C=O----HN) between the accepting group (C=O) of Asp351 and the amine group of the ligand. No π–π interactions were found in this ligand–receptor complex.

parameters of the residues and metabolites were carried out, as well as the description of the

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The binding energy of TAM's metabolites at the active site of PR has been predicted by carrying out molecular docking calculations. The schematic structure of the active site and the binding energies are shown in Figure 5. The negative value of the binding energy in the

Figure 5. Amino acids of the active site of the progesterone receptor with (A) 4OHTAM, (B) END, and (C) NDTAM.

hydrogen bond formed between the metabolites and the PR active site.

3.3.1. Molecular docking

In all cases the metabolites analyzed followed the Lipinski et al. rule of five, which states: when there are five or fewer hydrogen bonds the drug will not present poor absorption or permeation and will be more active [53]. Figure 4 shows the metabolites as a ball and stick and the residues of the active site as a tube. The hydrogen bonds are shown as green dots and π–π interactions are the areas marked in yellow.

#### 3.3. Analysis of the progesterone receptor with the metabolites

Analysis of molecular docking between PR and the metabolites is characterized by the active site of PR: the active site was described and the calculation and analysis of chemical reactivity

Figure 4. Hydrogen bond (green) and π–π interactions (yellow) at the active site of the estrogen receptor with (A) 4OHTAM, (B) END, and (C) NDTAM.

parameters of the residues and metabolites were carried out, as well as the description of the hydrogen bond formed between the metabolites and the PR active site.

#### 3.3.1. Molecular docking

NDTAM. In this metabolite was found one hydrogen bond (C=O----HN) between the accepting group (C=O) of Asp351 and the amine group of the ligand. No π–π interactions were found in

In all cases the metabolites analyzed followed the Lipinski et al. rule of five, which states: when there are five or fewer hydrogen bonds the drug will not present poor absorption or permeation and will be more active [53]. Figure 4 shows the metabolites as a ball and stick and the residues of the active site as a tube. The hydrogen bonds are shown as green dots and π–π

Analysis of molecular docking between PR and the metabolites is characterized by the active site of PR: the active site was described and the calculation and analysis of chemical reactivity

Figure 4. Hydrogen bond (green) and π–π interactions (yellow) at the active site of the estrogen receptor with (A)

this ligand–receptor complex.

126 Molecular Docking

4OHTAM, (B) END, and (C) NDTAM.

interactions are the areas marked in yellow.

3.3. Analysis of the progesterone receptor with the metabolites

The binding energy of TAM's metabolites at the active site of PR has been predicted by carrying out molecular docking calculations. The schematic structure of the active site and the binding energies are shown in Figure 5. The negative value of the binding energy in the

Figure 5. Amino acids of the active site of the progesterone receptor with (A) 4OHTAM, (B) END, and (C) NDTAM.

docking indicates that the system is stable and that there is an interaction between PR and metabolites at the site: –1.45 kcal/mol for 4OHTAM, –0.38 kcal/mol for END, and –9.29 kcal/ mol for NDTAM.

3.3.3. Chemical reactivity

As soon as the most stable structure of the active site of TAM's metabolites was obtained, an analysis of the chemical reactivity of progesterone residues was performed by means of the

Metabolite Active site EA (eV) I (eV) η (eV) μ = – χ (eV) ω (eV) 4OHTAM Phe905 0.93 6.58 2.82 3.76 2.50

END Arg766 0.21 7.03 3.41 3.62 1.92

NDTAM Leu763 0.35 7.17 3.41 3.76 2.07

Leu797 0.67 6.86 3.09 3.74 2.36 Leu887 0.43 7.01 3.29 3.72 2.10

Leu763 0.43 6.2 2.89 3.31 1.90 Gly722 0.51 6.25 2.87 3.38 1.99 Met759 0.88 7.74 3.43 4.31 2.71 Gln725 0.70 7.10 3.20 3.90 2.38 Trp755 0.86 5.85 2.50 3.35 2.25 Asn719 0.76 7.16 3.20 3.96 2.45 Leu797 0.67 6.86 3.09 3.74 2.36 Met756 0.75 6.30 2.77 3.52 2.54 Phe778 0.86 6.60 2.87 3.73 2.42 Leu715 0.60 7.02 3.21 3.81 2.26 Cys891 0.55 6.89 3.17 3.72 2.18 Met801 0.64 6.27 2.82 3.45 2.12 Met909 0.73 5.79 2.85 3.26 2.10

Leu797 0.67 6.86 3.09 3.74 2.36 Leu887 0.43 7.01 3.29 3.72 2.10 Thr894 0.49 6.57 3.04 3.53 2.05 Val760 0.76 6.92 3.08 3.84 2.40 Met756 0.75 6.3 2.77 3.52 2.54 Leu715 0.60 7.02 3.21 3.81 2.26 Gln725 0.70 7.10 3.20 3.90 2.38 Cys891 0.55 6.89 3.17 3.72 2.18 Met801 0.64 6.27 2.82 3.45 2.12 Met909 0.50 6.21 2.85 3.36 1.97 Leu721-Gly722 0.33 7.13 3.73 3.40 1.55 Leu718-Asn719 1.06 7.06 3.00 4.06 2.74

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reactivity descriptors. Results for these calculations are shown in Table 4.

Although the metabolites END and 4OHTAM have a negative bond energy, their values remain very low compared to TAM, which has –9.38 kcal/mol [13]. Therefore, these two metabolites will have very low biological activity in PRs.

#### 3.3.2. Active site

The active site of PR obtained by theoretical analysis is as follows.

4OHTAM. There are 15 residues in contact with the metabolite 4OHTAM at the active site of PR. Four of them are linked, forming an amino acids sequence, leucine718-aspartic acid719 (Leu718-Asn719) and leucine721-glycine722 (Leu721-Gly722). The other 11 residues are highly hydrophilic: glutamine725 residue (Gln725), cysteine891 (Cys891), threonine894 (Thr894), and phenylalanine905 (Phe905); and seven are hydrophobic residues: methionine756, methionine801, and methionine909 (Met756, Met801, and Met909), valine760 (Val760), and leucine715, leucine797, and leucine887 (Leu715, Leu797, and Leu887).

END. The active site in END is formed by the following residues: glutamine725 (Gln725), cysteine891 (Cys891), glycine722 (Gly722), asparagine719 (Asn19), and arginine766 (Arg766), which are hydrophilic. The hydrophobic residues are phenylalanine778 (Phe778), tryptophan755 (Trp755), methionine756, methionine759, methionine801, and methionine909 (Met756, Met759, Met801, and Met909), and leucine715, leucine763, and leucine797 (Leu715, Leu763, and Leu797).

NDTAM. The active site for NDTAM consists of the following residues: leucine718-aspartic acid719 (Leu718-Asn 719) and methionine759-valine760 (Met759-Val760). Hydrophilic residues are glutamine725 (Gln725), arginine766 (Arg766), and cysteine891 (Cys891). Hydrophobic residues are phenylalanine778 (Phe778), methionine756, methionine801, and methionine909 (Met756, Met801, and Met909), and leucine715, lucine763, leucine797, and leucine887 (Leu715, Leu763, Leu797, and Leu887).

Most of the residues of the active site of 4OHTAM and END surround the planar core of the ligand and over the functional group amine. The steric hindrance of this amine group produces minor binding energy.

When the metabolites bind, there is a conformational change and they are recognized by the amino acids of the active site. This has to do with the coupling energies. In PR, NDTAM has a higher amount of binding energy exceeding –9 kcal/mol.

Even when PR is more labile than ER, the binding energies indicate that the receptor is not sufficiently labile to recognize the metabolites 4OHTAM and END, which present binding energies lower than –1.5 kcal/mol.

The residues for the metabolites in PR are shown in Figure 5.

#### 3.3.3. Chemical reactivity

docking indicates that the system is stable and that there is an interaction between PR and metabolites at the site: –1.45 kcal/mol for 4OHTAM, –0.38 kcal/mol for END, and –9.29 kcal/

Although the metabolites END and 4OHTAM have a negative bond energy, their values remain very low compared to TAM, which has –9.38 kcal/mol [13]. Therefore, these two metabolites

4OHTAM. There are 15 residues in contact with the metabolite 4OHTAM at the active site of PR. Four of them are linked, forming an amino acids sequence, leucine718-aspartic acid719 (Leu718-Asn719) and leucine721-glycine722 (Leu721-Gly722). The other 11 residues are highly hydrophilic: glutamine725 residue (Gln725), cysteine891 (Cys891), threonine894 (Thr894), and phenylalanine905 (Phe905); and seven are hydrophobic residues: methionine756, methionine801, and methionine909 (Met756, Met801, and Met909), valine760 (Val760), and leucine715,

END. The active site in END is formed by the following residues: glutamine725 (Gln725), cysteine891 (Cys891), glycine722 (Gly722), asparagine719 (Asn19), and arginine766 (Arg766), which are hydrophilic. The hydrophobic residues are phenylalanine778 (Phe778), tryptophan755 (Trp755), methionine756, methionine759, methionine801, and methionine909 (Met756, Met759, Met801, and Met909), and leucine715, leucine763, and leucine797 (Leu715, Leu763, and Leu797). NDTAM. The active site for NDTAM consists of the following residues: leucine718-aspartic acid719 (Leu718-Asn 719) and methionine759-valine760 (Met759-Val760). Hydrophilic residues are glutamine725 (Gln725), arginine766 (Arg766), and cysteine891 (Cys891). Hydrophobic residues are phenylalanine778 (Phe778), methionine756, methionine801, and methionine909 (Met756, Met801, and Met909), and leucine715, lucine763, leucine797, and leucine887 (Leu715,

Most of the residues of the active site of 4OHTAM and END surround the planar core of the ligand and over the functional group amine. The steric hindrance of this amine group pro-

When the metabolites bind, there is a conformational change and they are recognized by the amino acids of the active site. This has to do with the coupling energies. In PR, NDTAM has a

Even when PR is more labile than ER, the binding energies indicate that the receptor is not sufficiently labile to recognize the metabolites 4OHTAM and END, which present binding

mol for NDTAM.

128 Molecular Docking

3.3.2. Active site

Leu763, Leu797, and Leu887).

duces minor binding energy.

energies lower than –1.5 kcal/mol.

will have very low biological activity in PRs.

The active site of PR obtained by theoretical analysis is as follows.

leucine797, and leucine887 (Leu715, Leu797, and Leu887).

higher amount of binding energy exceeding –9 kcal/mol.

The residues for the metabolites in PR are shown in Figure 5.

As soon as the most stable structure of the active site of TAM's metabolites was obtained, an analysis of the chemical reactivity of progesterone residues was performed by means of the reactivity descriptors. Results for these calculations are shown in Table 4.



in NDTAM act as donor acceptors, namely, these residues are oxidized in the presence of the metabolites. The remainder of the residues act as electron acceptors. The values are shown in

For 4OHTAM and NDTAM the maxima values are in Leu718-Asp719 with 0.064 and 0.057, respectively. For END the maxima value is in Met759 with 0.088. Thus, the calculations

> Leu721-Gly722 0.002 Phe905 0.037 Leu797 0.033 Leu887 0.03 Thr894 0.014 Val760 0.043 Met756 0.014 Leu715 0.039 Gln725 0.047 Cys891 0.031 Met801 0.007 Met909 0.002

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Leu763 0.001 Gly722 0.006 Met759 0.088 Trp755 0.003 Asn719 0.06 Leu797 0.04 Met756 0.020 Gln725 0.058 Phe778 0.041 Leu715 0.046 Cys891 0.037 Met801 0.013 Met909 0.006

Leu797 0.027 Leu887 0.024 Phe778 0.027

Metabolite Residue ΔN 4OHTAM Leu718-Asn719 0.064

END Arg766 0.027

NDTAM Leu763 0.615

Table 5.

Table 4. Parameters of chemical reactivity of the active site residues of the progesterone receptor.

The electron affinities of the residues fluctuate from 0.33 eV to 1.06 eV. The highest value of electron affinity is for the Leu718-Asn719 residue, which is present in the active site of 4OHTAM, NDTAM, and the Met759 residue of END. The ionization potential results show that the greatest possibility of losing electrons is: Leu721-Gly722 with 7.13 eV in 4OHTAM, Leu763 with 7.17 eV in NDTAM, and Met759 with 7.74 eV in END.

Chemical hardness, the parameter that measures the resistance to change in the electronic configuration, exhibited amounts from 2.50 eV to 3.73 eV. In 4OHTAM, the lowest value and therefore the one that will react more easily in the presence of the metabolites is 2.77 eV for Met756. For END it is 2.50 eV in Trp755 and 2.61 eV in Met759-Val760. Met801 had a value of 2.82 eV in the NDTAM metabolite.

According to chemical potential, Met759 residue at –4.31 eV presents the highest value in END. The electronegativity (μ = χ) shows that the Met759 residue has the greatest tendency to attract electrons at 4.31 eV in END. Electrophilicity ω is the measure of the stabilization energy when systems become saturated by electrons from the surroundings. In this case in the active site of 4OHTAM value decreases in the following order: Leu718-Asn719 > Met756 > Phe905 > Val760 > Gln725 > Leu797 > Leu715 > Cys891 > Met801 > Leu887 > Thr894 > Met909 > Leu721-Gly722. In END the decreasing order is Met759 > Met756 > Asn719 > Phe778 > Gln725 > Leu797 > Leu715 > Trp755 > Cys891 > Met801 > Met909 > Gly722 > Arg766 > Leu763 and in NDTAM the decreasing order is Leu718-Asn719 > Met759-Val760 > Phe778 > Gln725 > Leu797 > Arg766 > Leu715 > Met756 > Cys891 > Met801 > Leu887 > Leu763 > Met909.

#### 3.3.4. Charge transfer descriptor

Considering the high importance of this parameter in the formation of complexes in biological systems, the highest values in the different metabolites were defined. The charge transfer between metabolites and PR residues was calculated using Eq. 5. The results show that Met909 in 4OHTAM, Met909 and Leu763 in END, and Met909, Met756, Arg766, and Leu763 in NDTAM act as donor acceptors, namely, these residues are oxidized in the presence of the metabolites. The remainder of the residues act as electron acceptors. The values are shown in Table 5.

For 4OHTAM and NDTAM the maxima values are in Leu718-Asp719 with 0.064 and 0.057, respectively. For END the maxima value is in Met759 with 0.088. Thus, the calculations


The electron affinities of the residues fluctuate from 0.33 eV to 1.06 eV. The highest value of electron affinity is for the Leu718-Asn719 residue, which is present in the active site of 4OHTAM, NDTAM, and the Met759 residue of END. The ionization potential results show that the greatest possibility of losing electrons is: Leu721-Gly722 with 7.13 eV in 4OHTAM,

Metabolite Active site EA (eV) I (eV) η (eV) μ = – χ (eV) ω (eV)

Phe778 0.86 6.6 2.87 3.73 2.42

Leu715 0.60 7.02 3.21 3.81 2.26 Arg766 0.78 6.70 2.96 3.74 2.36 Gln725 0.70 7.10 3.20 3.90 2.38 Cys891 0.55 6.89 3.17 3.72 2.18 Met756 0.75 6.30 2.77 3.53 2.24 Met801 0.64 6.27 2.82 3.45 2.12 Met909 0.50 6.21 2.85 3.36 1.97 Met759-Val760 1.05 6.26 2.61 3.65 2.56 Leu718-Asn719 1.06 7.06 3 4.06 2.74

Chemical hardness, the parameter that measures the resistance to change in the electronic configuration, exhibited amounts from 2.50 eV to 3.73 eV. In 4OHTAM, the lowest value and therefore the one that will react more easily in the presence of the metabolites is 2.77 eV for Met756. For END it is 2.50 eV in Trp755 and 2.61 eV in Met759-Val760. Met801 had a value of

According to chemical potential, Met759 residue at –4.31 eV presents the highest value in END. The electronegativity (μ = χ) shows that the Met759 residue has the greatest tendency to attract electrons at 4.31 eV in END. Electrophilicity ω is the measure of the stabilization energy when systems become saturated by electrons from the surroundings. In this case in the active site of 4OHTAM value decreases in the following order: Leu718-Asn719 > Met756 > Phe905 > Val760 > Gln725 > Leu797 > Leu715 > Cys891 > Met801 > Leu887 > Thr894 > Met909 > Leu721-Gly722. In END the decreasing order is Met759 > Met756 > Asn719 > Phe778 > Gln725 > Leu797 > Leu715 > Trp755 > Cys891 > Met801 > Met909 > Gly722 > Arg766 > Leu763 and in NDTAM the decreasing order is Leu718-Asn719 > Met759-Val760 > Phe778 > Gln725 > Leu797

Considering the high importance of this parameter in the formation of complexes in biological systems, the highest values in the different metabolites were defined. The charge transfer between metabolites and PR residues was calculated using Eq. 5. The results show that Met909 in 4OHTAM, Met909 and Leu763 in END, and Met909, Met756, Arg766, and Leu763

> Arg766 > Leu715 > Met756 > Cys891 > Met801 > Leu887 > Leu763 > Met909.

Leu763 with 7.17 eV in NDTAM, and Met759 with 7.74 eV in END.

Table 4. Parameters of chemical reactivity of the active site residues of the progesterone receptor.

2.82 eV in the NDTAM metabolite.

130 Molecular Docking

3.3.4. Charge transfer descriptor


hydrogen bond (C=O----H-N) was formed between the Asn719 residue and the amino group. The unique electrostatic interaction is shown with green dots in Figure 6. Also, it was found

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In this chapter the molecular docking of ER and PR with TAM's metabolites, 4OHTAM, NDTAM, and NDTAM, was analyzed. The amino acids sequence of the active site for each ligand–macromolecule complex was examined. The residues that constituted each active site were analyzed separately to find the charge transfer parameter, the hydrogen bond, and the

According to the binding energy obtained from docking, ER has greater stability than PR with the metabolites analyzed. However, in both cases there is a coupling between the receptor and the ligand, even when two of the binding energies in PR–ligand coupling are very small.

This coupling plays an important part in avoiding the transcription factor cascade reported by

This information agrees with the results of the chemical reactivity parameters, where it was found that the average of the chemical hardness values are lowest in active site residues of ER

The charge transfer descriptor shows that TAM's metabolites mostly act as electron acceptors in their interaction with the hormone receptors. The hydrogen bonds in ER with END agree with the highest binding energy of this ligand. There are two hydrogen bonds, one π–π interaction, and a ΔN of –0.086. While in PR, there is only one hydrogen bond with NDTAM

This work described the successful combination of the methods of molecular mechanics and electronic structure. It also explored the different conformational spaces and binding modes

In addition to the above a significant conclusion is that the molecular modeling and simulations are an important improvement tool for any laboratory in many industries. Currently, many sectors are moving toward using more modeling and simulations in their laboratories. As Bernard Charlès, Dassault Systèmes CEO, states: "digitalization will mean big changes for everyday lab activities down the road." Two key solutions for all industries (such as pharmaceutical, chemical, life sciences, energy, and consumer goods) are collaboration and the ability

Patrick Bultinck et al. in their preface to the book Computational Medicinal Chemistry for Drug Discovery [21] wrote: "Nowadays, one can safely state that the computational chemist has become a respectable member of a drug design team." And we can add that the docking tool

that allow smaller systems to work with them at the electronic level.

is essential for most techniques for structure-based drug design.

that the rule of five by Lipinski et al. [52, 53] was fulfilled.

π–π interaction between the ligand and the receptor.

4. Conclusions

Leehy et al. [54].

and the value of ΔN is –0.057.

to predict using simulation and modeling [55].

than in PR.

Table 5. Transfer of charge between metabolites and progesterone receptor residues.

indicate that oxidative damage in the active site decreases in the following order: NDTAM > END > 4OHTAM.

#### 3.3.5. Electrostatics interactions

An analysis of the hydrogen bond and π–π interactions between the active site on PR and TAM's metabolites was performed. In the case of 4OHTAM and END metabolites no hydrogen bonds were generated, nor were there any π–π interactions, whereas with NDTAM only a

Figure 6. Hydrogen bond (green) at the active site of the progesterone receptor with the NDTAM metabolite.

hydrogen bond (C=O----H-N) was formed between the Asn719 residue and the amino group. The unique electrostatic interaction is shown with green dots in Figure 6. Also, it was found that the rule of five by Lipinski et al. [52, 53] was fulfilled.

## 4. Conclusions

indicate that oxidative damage in the active site decreases in the following order: NDTAM >

Leu715 0.033 Arg766 0.665 Gln725 0.041 Cys891 0.025 Met756 0.669 Met801 0.001 Met909 0.008 Met759-Val760 0.021 Leu718-Asn719 0.057

Metabolite Residue ΔN

Table 5. Transfer of charge between metabolites and progesterone receptor residues.

An analysis of the hydrogen bond and π–π interactions between the active site on PR and TAM's metabolites was performed. In the case of 4OHTAM and END metabolites no hydrogen bonds were generated, nor were there any π–π interactions, whereas with NDTAM only a

Figure 6. Hydrogen bond (green) at the active site of the progesterone receptor with the NDTAM metabolite.

END > 4OHTAM.

132 Molecular Docking

3.3.5. Electrostatics interactions

In this chapter the molecular docking of ER and PR with TAM's metabolites, 4OHTAM, NDTAM, and NDTAM, was analyzed. The amino acids sequence of the active site for each ligand–macromolecule complex was examined. The residues that constituted each active site were analyzed separately to find the charge transfer parameter, the hydrogen bond, and the π–π interaction between the ligand and the receptor.

According to the binding energy obtained from docking, ER has greater stability than PR with the metabolites analyzed. However, in both cases there is a coupling between the receptor and the ligand, even when two of the binding energies in PR–ligand coupling are very small.

This coupling plays an important part in avoiding the transcription factor cascade reported by Leehy et al. [54].

This information agrees with the results of the chemical reactivity parameters, where it was found that the average of the chemical hardness values are lowest in active site residues of ER than in PR.

The charge transfer descriptor shows that TAM's metabolites mostly act as electron acceptors in their interaction with the hormone receptors. The hydrogen bonds in ER with END agree with the highest binding energy of this ligand. There are two hydrogen bonds, one π–π interaction, and a ΔN of –0.086. While in PR, there is only one hydrogen bond with NDTAM and the value of ΔN is –0.057.

This work described the successful combination of the methods of molecular mechanics and electronic structure. It also explored the different conformational spaces and binding modes that allow smaller systems to work with them at the electronic level.

In addition to the above a significant conclusion is that the molecular modeling and simulations are an important improvement tool for any laboratory in many industries. Currently, many sectors are moving toward using more modeling and simulations in their laboratories. As Bernard Charlès, Dassault Systèmes CEO, states: "digitalization will mean big changes for everyday lab activities down the road." Two key solutions for all industries (such as pharmaceutical, chemical, life sciences, energy, and consumer goods) are collaboration and the ability to predict using simulation and modeling [55].

Patrick Bultinck et al. in their preface to the book Computational Medicinal Chemistry for Drug Discovery [21] wrote: "Nowadays, one can safely state that the computational chemist has become a respectable member of a drug design team." And we can add that the docking tool is essential for most techniques for structure-based drug design.

## Author details

Linda-Lucila Landeros-Martinez, Daniel Glossman-Mitnik, Erasmo Orrantia-Borunda and Norma Flores-Holguin\*

[9] Papageorgiou L, Cuong NT, Vlachakis D. Antibodies as stratagems against cancer. Molec-

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135

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NANOCOSMOS Virtual Lab, Department of Environment and Energy, Advanced Materials Research Center (CIMAV), Miguel de Cervantes 120, Complejo Industrial Chihuahua, Chihuahua, México

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**Chapter 8**

Provisional chapter

**Has Molecular Docking Ever Brought us a Medicine?**

DOI: 10.5772/intechopen.72898

Molecular docking has been developed and improving for many years, but its ability to bring a medicine to the drug market effectively is still generally questioned. In this chapter, we introduce several successful cases including drugs for treatment of HIV, cancers, and other prevalent diseases. The technical details such as docking software, protein data bank (PDB) structures, and other computational methods employed are also collected and displayed. In most of the cases, the structures of drugs or drug candidates and the interacting residues on the target proteins are also presented. In addition, a few successful examples of drug repurposing using molecular docking are mentioned in this chapter. It should provide us with confidence that the docking will be extensively employed in the industry and basic research. Moreover, we should actively apply molec-

ular docking and related technology to create new therapies for diseases.

Keywords: computational drug design, molecular docking, drug repurposing

Molecular docking is one of many computational tools that can be used in drug discovery [1–4]. It is a form of structure-based drug discovery that quantifies the binding affinities between small molecules and macromolecular targets (proteins). The first step in molecular docking is choosing a drug target. Any macromolecule can be used as a target; some very common targets include enzymes and regulatory elements. Next, the three-dimensional structure must be determined or predicted; high resolution structures can be determined using X-rays, NMR, or electron microscopy (EM). Thousands of popular targets have solved structures available on the protein data bank (PDB) [5]. Many drug targets have known binding sites; if not, software that can predict potential binding sites for different ligands have been developed. Docking

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Has Molecular Docking Ever Brought us a Medicine?

Mark Andrew Phillips, Marisa A. Stewart, Darby L. Woodling and Zhong-Ru Xie

Mark Andrew Phillips, Marisa A. Stewart, Darby L. Woodling and Zhong-Ru Xie

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72898

Abstract

1. Introduction

#### **Has Molecular Docking Ever Brought us a Medicine?** Has Molecular Docking Ever Brought us a Medicine?

DOI: 10.5772/intechopen.72898

Mark Andrew Phillips, Marisa A. Stewart, Darby L. Woodling and Zhong-Ru Xie Mark Andrew Phillips, Marisa A. Stewart, Darby L. Woodling and Zhong-Ru Xie

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72898

#### Abstract

Molecular docking has been developed and improving for many years, but its ability to bring a medicine to the drug market effectively is still generally questioned. In this chapter, we introduce several successful cases including drugs for treatment of HIV, cancers, and other prevalent diseases. The technical details such as docking software, protein data bank (PDB) structures, and other computational methods employed are also collected and displayed. In most of the cases, the structures of drugs or drug candidates and the interacting residues on the target proteins are also presented. In addition, a few successful examples of drug repurposing using molecular docking are mentioned in this chapter. It should provide us with confidence that the docking will be extensively employed in the industry and basic research. Moreover, we should actively apply molecular docking and related technology to create new therapies for diseases.

Keywords: computational drug design, molecular docking, drug repurposing

#### 1. Introduction

Molecular docking is one of many computational tools that can be used in drug discovery [1–4]. It is a form of structure-based drug discovery that quantifies the binding affinities between small molecules and macromolecular targets (proteins). The first step in molecular docking is choosing a drug target. Any macromolecule can be used as a target; some very common targets include enzymes and regulatory elements. Next, the three-dimensional structure must be determined or predicted; high resolution structures can be determined using X-rays, NMR, or electron microscopy (EM). Thousands of popular targets have solved structures available on the protein data bank (PDB) [5]. Many drug targets have known binding sites; if not, software that can predict potential binding sites for different ligands have been developed. Docking

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

studies can be performed using known ligands (naturally occurring molecules or known drugs) or novel ligands. Virtual screening (i.e. identifying novel ligands with molecular docking) provides an extremely useful (but time consuming) method of drug discovery because molecules can be designed to have high binding affinity to a very specific site. Docking studies are often validated using further computational methods, such as molecular dynamic simulation. The most successful candidates from computational trials can be tested in vitro or in vivo, and eventually progress to clinical trials (Figure 1).

It is believed that a searching algorithm, which assists in thoroughly and efficiently exploring possible positions, orientations and conformations of potential drugs and the target proteins, and a scoring function, which assists in precisely and correctly identifying the most energetically favorable binding poses, are two most important components of a molecular docking programs. However, some other factors will affect the effectiveness and accuracy of molecular docking, such as the availability and quality of a determined or predicted structure of the target protein, the conformational changes of the target proteins after the drug binding, and the identification of potential binding sites. As those mentioned in previous chapters, many commercial and academic docking search algorithms, scoring functions, and software packages have been developed and improved in the past decades. However, it is still questioned if there are any successful stories in which molecular docking have helped to bring a drug to the market.

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 143

Although many molecular docking algorithms have been developed and improved for many decades, biomedical laboratories or pharmaceutical companies used to be hesitant to apply

1. The "force fields" which describe the intra- and inter-molecular interaction energies were not accurate and precise enough to estimate or calculate the binding affinities between

2. The computer was not "fast" enough to calculate the interacting energy of many possible binding "conformations" of one or many possible binding compound(s) using a sophisticated model taking account into all the factors, components, and conditions of molecular

3. The number of binding complex structures was not large enough and the resolution of

4. The searching/sampling algorithms to explore the possible binding orientations and conformations were not efficient to identify possible binding poses with reasonable time.

These reasons and concerns are all tightly cross-linked together, and, fortunately, have been dramatically improved in the past years. For example, the number of structures on PDB has increased from 47,605 to 133,759 since 2007 [6]. The resolution of determined structures has significantly improved. Therefore, the accuracy of both physics-based and knowledge-based scoring functions which assist researchers in identifying the most energy favorable binding poses and estimating binding affinities have been improved. The substantial improvement in both computer hardware and software also make it possible to screen a large number of

When we attempt to dock a compound to a target protein, often we need to use other computational methods before docking or in parallel. For instance, we may need to do structure prediction if the structure of the target protein has not yet been determined. The accumulated PDB structures with good resolution and the accurate structure prediction algorithms make it possible for researchers to obtain reliable structural models to perform molecular docking experiments. The enhanced quantity, quality, and diversity of proteincompound complex structures provide solid basis for creation of accurate binding site

natural and artificial compounds and search the best binding poses efficiently.

this technology to drug screening. Here are some possible reasons:

proteins and potential binding drugs.

available structures was not good.

interactions.

Figure 1. A brief flowchart of novel drug discovery procedure.

It is believed that a searching algorithm, which assists in thoroughly and efficiently exploring possible positions, orientations and conformations of potential drugs and the target proteins, and a scoring function, which assists in precisely and correctly identifying the most energetically favorable binding poses, are two most important components of a molecular docking programs. However, some other factors will affect the effectiveness and accuracy of molecular docking, such as the availability and quality of a determined or predicted structure of the target protein, the conformational changes of the target proteins after the drug binding, and the identification of potential binding sites. As those mentioned in previous chapters, many commercial and academic docking search algorithms, scoring functions, and software packages have been developed and improved in the past decades. However, it is still questioned if there are any successful stories in which molecular docking have helped to bring a drug to the market.

studies can be performed using known ligands (naturally occurring molecules or known drugs) or novel ligands. Virtual screening (i.e. identifying novel ligands with molecular docking) provides an extremely useful (but time consuming) method of drug discovery because molecules can be designed to have high binding affinity to a very specific site. Docking studies are often validated using further computational methods, such as molecular dynamic simulation. The most successful candidates from computational trials can be tested

in vitro or in vivo, and eventually progress to clinical trials (Figure 1).

142 Molecular Docking

Figure 1. A brief flowchart of novel drug discovery procedure.

Although many molecular docking algorithms have been developed and improved for many decades, biomedical laboratories or pharmaceutical companies used to be hesitant to apply this technology to drug screening. Here are some possible reasons:


These reasons and concerns are all tightly cross-linked together, and, fortunately, have been dramatically improved in the past years. For example, the number of structures on PDB has increased from 47,605 to 133,759 since 2007 [6]. The resolution of determined structures has significantly improved. Therefore, the accuracy of both physics-based and knowledge-based scoring functions which assist researchers in identifying the most energy favorable binding poses and estimating binding affinities have been improved. The substantial improvement in both computer hardware and software also make it possible to screen a large number of natural and artificial compounds and search the best binding poses efficiently.

When we attempt to dock a compound to a target protein, often we need to use other computational methods before docking or in parallel. For instance, we may need to do structure prediction if the structure of the target protein has not yet been determined. The accumulated PDB structures with good resolution and the accurate structure prediction algorithms make it possible for researchers to obtain reliable structural models to perform molecular docking experiments. The enhanced quantity, quality, and diversity of proteincompound complex structures provide solid basis for creation of accurate binding site

prediction methods, and they help reduce the searching surface area on the target proteins for docking algorithms [7–9]. Other computational methods such as pharmacophore and quantitative structure-activity relationship (QSAR) models can be used prior to the molecular docking to reduce computational load and time [10–12]. In summary, the technology of molecular docking has matured and been applied in different stages of the drug discovery process. The successful stories have not been mentioned often and are not widely known. They will be introduced in this chapter.

viral DNA that is transported to the nucleus. The integration of the viral DNA into the host's genome is carried out by the integrase enzyme [16]. The HIV virus then may remain dormant or continue to assemble new HIV-1 virions. The plasma membrane of the host cell is the site for the production of new HIV-1 virions. The virion buds that are produced at the plasma membrane are cleaved by HIV-1 protease enzyme. Once the bud has been cleaved by HIV-1 protease, the internal components can assemble, and in turn create a virion capable of infecting other cells. The two targets that computational drug researchers have focused on

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 145

Integrase (IN) is a retrovirus enzyme not exclusive to only HIV. This protein allows the genetic material of the virus to be integrated into the DNA of the host cell. Integration occurs after the double-stranded viral DNA is produced by reverse transcriptase. Once integration has commenced for a cell, there is no turning back. The cell is now considered a pro-virus, and it is now a permanent carrier of the virus. In general, retroviral integrases catalyze two reactions. Both reactions are catalyzed by the same active site on the enzyme and occur via transesterification.

The most common inhibitors for integrase are referred to as integrase strand transfer inhibitors (INSTIs). Mg2+ and Mn2+ are critical cofactors in the integration phase [17], and inactivating these cofactors causes functional impairment of integrase. Most HIV-1 INSTIs contain a structural motif that coordinates the two divalent magnesium ions in the enzyme's active site [17]. Researchers screen over 250,000 compounds to yield potent inhibitors [18]. The most active inhibitors seemed to contain a distinct beta-diketo acid (DKA) moiety [19]. This moiety had the ability to coordinate metal ions within the IN active site. There was similar antiviral activity when the DKA pharmacophore was transferred to a naphthyridine carboxamide core [20]. A class of N-alkyl hydroxypyridinone carboxylic acids was the result of the success with the diketo acid structural analogs. These new analogs had a good pharmacokinetic profile in rats [21]. The drug, MK-0518, also known as Raltegravir, became the most promising pyrimidinone carboxamide derivative. Raltegravir was the first integrase inhibitor to progress to Phase III clinical trials. While there have been multiple resistant mutations for both treatmentexperienced and treatment-naïve patients, Raltegravir still proved to be an effective IN inhibitor [22]. In October 2007, Raltegravir became the first FDA-approved IN inhibitor (Table 1). To bring a single drug to the market, it can cost upwards of \$2 billion [23]. Even with this, only one in three drugs will generate enough revenue to cover the cost of the research and development of the drug [24]. Pharmaceutical researchers and executives can see the allure of modifying current leads on drugs, rather than trying to design a new drug. "Me-too" drugs [18] can create an optimized drug and create vital marketplace competition, but many argue that slight modifications are producing negligible improvements [25]. "Me-too" drug emergence has seen a surge in the HIV-1 integrase inhibitor market. While Raltegravir has become the known and widely used anti-HIV drug, amino acid mutations have already conferred robust viral resistance of the drug [26]. This viral drug resistance normally occurs when one

significantly are HIV-1 integrase and HIV-1 protease.

2.2.1. HIV-1 integrase inhibitor—Raltegravir and its ensuing analogs

2.2. HIV-1 integrase

## 2. Identification of medicine for HIV

The human immunodeficiency virus (HIV) epidemic around the world has pushed massive amounts of money into research that looks for ways to help treat and prevent this virus. Because bringing a drug into the market can take many years and cost astronomical amounts of money, it is of the utmost importance of researchers to use a cost effective ways to find these new therapeutics. Computational methods have been gradually becoming commonplace in drug design research. These methods have been either confirming established research, discovering new compounds, binding sites or conformations, and even allowing for the repurposing of the drug to treat other illnesses. HIV research has seen an influx of multiple computational methods being used to confirm discoveries of previous studies and establish new ones. Methods such as docking and molecular dynamics are saving researchers valuable time. These methods are also allowing research to make accurate and precise predictions of what is going on at the molecular level. While computational drug design methods are nowhere near replacing in vitro and in vivo testing, in silico testing is becoming increasingly popular for researchers to validate their research or act as a starting point for in vitro testing. This section will introduce how researchers used computational methods to help identify drugs for HIV-1 Protease and HIV-1 Integrase. It will also discuss how these methods are being utilized for future developments in this area of research, and how researchers were able to use the drugs Saquinavir and Nelfinavir toward treating a disease unrelated to HIV—Chagas.

#### 2.1. Human immunodeficiency virus

Acquired immunodeficiency syndrome (AIDS) is acquired in humans by the retrovirus HIV [13]. HIV infects important helper T cells in the human immune system—specifically CD4+ T cells [14]. HIV is transmitted as positive-sense, single-stranded, enveloped RNA virus. There are currently two types of HIV that have been characterized as HIV-1 and HIV-2. HIV-1 was the first HIV virus discovered and it is more virulent and more infective than HIV-2 [15]. After the viral capsid has entered the cell, an enzyme called reverse transcriptase liberates the positive-sense RNA from the viral proteins and copies it into a complimentary DNA molecule [16]. The reverse transcriptase process is very prone to errors. This characteristic results in many mutations that make this component of HIV likely to encounter drug resistance. For this reason, HIV reverse transcriptase is an unlikely target for HIV therapeutics. The newly formed circular DNA strand and its complement form a double-stranded

viral DNA that is transported to the nucleus. The integration of the viral DNA into the host's genome is carried out by the integrase enzyme [16]. The HIV virus then may remain dormant or continue to assemble new HIV-1 virions. The plasma membrane of the host cell is the site for the production of new HIV-1 virions. The virion buds that are produced at the plasma membrane are cleaved by HIV-1 protease enzyme. Once the bud has been cleaved by HIV-1 protease, the internal components can assemble, and in turn create a virion capable of infecting other cells. The two targets that computational drug researchers have focused on significantly are HIV-1 integrase and HIV-1 protease.

#### 2.2. HIV-1 integrase

prediction methods, and they help reduce the searching surface area on the target proteins for docking algorithms [7–9]. Other computational methods such as pharmacophore and quantitative structure-activity relationship (QSAR) models can be used prior to the molecular docking to reduce computational load and time [10–12]. In summary, the technology of molecular docking has matured and been applied in different stages of the drug discovery process. The successful stories have not been mentioned often and are not widely known.

The human immunodeficiency virus (HIV) epidemic around the world has pushed massive amounts of money into research that looks for ways to help treat and prevent this virus. Because bringing a drug into the market can take many years and cost astronomical amounts of money, it is of the utmost importance of researchers to use a cost effective ways to find these new therapeutics. Computational methods have been gradually becoming commonplace in drug design research. These methods have been either confirming established research, discovering new compounds, binding sites or conformations, and even allowing for the repurposing of the drug to treat other illnesses. HIV research has seen an influx of multiple computational methods being used to confirm discoveries of previous studies and establish new ones. Methods such as docking and molecular dynamics are saving researchers valuable time. These methods are also allowing research to make accurate and precise predictions of what is going on at the molecular level. While computational drug design methods are nowhere near replacing in vitro and in vivo testing, in silico testing is becoming increasingly popular for researchers to validate their research or act as a starting point for in vitro testing. This section will introduce how researchers used computational methods to help identify drugs for HIV-1 Protease and HIV-1 Integrase. It will also discuss how these methods are being utilized for future developments in this area of research, and how researchers were able to use the drugs Saquinavir and Nelfinavir toward treating a disease unrelated to HIV—Chagas.

Acquired immunodeficiency syndrome (AIDS) is acquired in humans by the retrovirus HIV [13]. HIV infects important helper T cells in the human immune system—specifically CD4+ T cells [14]. HIV is transmitted as positive-sense, single-stranded, enveloped RNA virus. There are currently two types of HIV that have been characterized as HIV-1 and HIV-2. HIV-1 was the first HIV virus discovered and it is more virulent and more infective than HIV-2 [15]. After the viral capsid has entered the cell, an enzyme called reverse transcriptase liberates the positive-sense RNA from the viral proteins and copies it into a complimentary DNA molecule [16]. The reverse transcriptase process is very prone to errors. This characteristic results in many mutations that make this component of HIV likely to encounter drug resistance. For this reason, HIV reverse transcriptase is an unlikely target for HIV therapeutics. The newly formed circular DNA strand and its complement form a double-stranded

They will be introduced in this chapter.

144 Molecular Docking

2.1. Human immunodeficiency virus

2. Identification of medicine for HIV

Integrase (IN) is a retrovirus enzyme not exclusive to only HIV. This protein allows the genetic material of the virus to be integrated into the DNA of the host cell. Integration occurs after the double-stranded viral DNA is produced by reverse transcriptase. Once integration has commenced for a cell, there is no turning back. The cell is now considered a pro-virus, and it is now a permanent carrier of the virus. In general, retroviral integrases catalyze two reactions. Both reactions are catalyzed by the same active site on the enzyme and occur via transesterification.

#### 2.2.1. HIV-1 integrase inhibitor—Raltegravir and its ensuing analogs

The most common inhibitors for integrase are referred to as integrase strand transfer inhibitors (INSTIs). Mg2+ and Mn2+ are critical cofactors in the integration phase [17], and inactivating these cofactors causes functional impairment of integrase. Most HIV-1 INSTIs contain a structural motif that coordinates the two divalent magnesium ions in the enzyme's active site [17]. Researchers screen over 250,000 compounds to yield potent inhibitors [18]. The most active inhibitors seemed to contain a distinct beta-diketo acid (DKA) moiety [19]. This moiety had the ability to coordinate metal ions within the IN active site. There was similar antiviral activity when the DKA pharmacophore was transferred to a naphthyridine carboxamide core [20]. A class of N-alkyl hydroxypyridinone carboxylic acids was the result of the success with the diketo acid structural analogs. These new analogs had a good pharmacokinetic profile in rats [21]. The drug, MK-0518, also known as Raltegravir, became the most promising pyrimidinone carboxamide derivative. Raltegravir was the first integrase inhibitor to progress to Phase III clinical trials. While there have been multiple resistant mutations for both treatmentexperienced and treatment-naïve patients, Raltegravir still proved to be an effective IN inhibitor [22]. In October 2007, Raltegravir became the first FDA-approved IN inhibitor (Table 1).

To bring a single drug to the market, it can cost upwards of \$2 billion [23]. Even with this, only one in three drugs will generate enough revenue to cover the cost of the research and development of the drug [24]. Pharmaceutical researchers and executives can see the allure of modifying current leads on drugs, rather than trying to design a new drug. "Me-too" drugs [18] can create an optimized drug and create vital marketplace competition, but many argue that slight modifications are producing negligible improvements [25]. "Me-too" drug emergence has seen a surge in the HIV-1 integrase inhibitor market. While Raltegravir has become the known and widely used anti-HIV drug, amino acid mutations have already conferred robust viral resistance of the drug [26]. This viral drug resistance normally occurs when one


of three amino acids—Y143, Q148, or N155—mutate in conjunction with at least one other mutation [27]. The strongest antiviral resistant mutation seems to be the Q148H integrase mutant (IC50 > 700 nM), and G140S has been shown to restore the poor replication ability of Q148H to wild-type levels [28]. Even though Raltegravir has seen this resistance profile, pharmaceutical companies still spend lots of money on "me-too" research and the

Table 1. The various HIV-1 protease and HIV-1 integrase inhibitors and their structures. The affected residues in HIV-1

Table of various HIV-1 integrase and HIV-1 protease inhibitors

Nelfinavir HIV-1 protease D30

Amprenavir HIV-1 protease I84

Drug Type of inhibitor Affected residues Structure

I84 L90 Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 147

L90 I54 V82

protease and HIV-1 integrase binding pocket are shown as well.

Table of various HIV-1 integrase and HIV-1 protease inhibitors

Raltegravir HIV-integrase D64

146 Molecular Docking

S-1360 HIV-1 integrase D64

Saquinavir HIV-1 protease G84

Drug Type of inhibitor Affected residues Structure

T66 E92 D116 Y143 Q148 E152 N155

T66 D116 Y143 Q148 E152 N155

I84 L90

Table 1. The various HIV-1 protease and HIV-1 integrase inhibitors and their structures. The affected residues in HIV-1 protease and HIV-1 integrase binding pocket are shown as well.

of three amino acids—Y143, Q148, or N155—mutate in conjunction with at least one other mutation [27]. The strongest antiviral resistant mutation seems to be the Q148H integrase mutant (IC50 > 700 nM), and G140S has been shown to restore the poor replication ability of Q148H to wild-type levels [28]. Even though Raltegravir has seen this resistance profile, pharmaceutical companies still spend lots of money on "me-too" research and the development on this drug. There should be a distinction made between me-too drugs and second-generation drugs [18]. A second-generation inhibitor needs to exhibit a new mode of action. Secondly, a second-generation drug needs to show significantly improved potency or decreased toxicity. A major problem with second-generation drugs is cross resistance, so these drugs should maintain potency, but avoid this cross-resistance.

atoms from the furan ring and keto group for coordinate bonds with the Mg2+ ions. This conformation appears to be stable because it occurs 102 out of 200 times. The binding site conformation of S-1360 inside the C active site is also very stable because of its 172 appearances out of 200. While these conformations of A and C are stable, this conformation is not in line with experimentally observed results. S-1360 selectively inhibits strand transfer reactions of HIV-1 integrase, but S-1360 in A and C did not interact with amino acids in the strand transfer (ST) cavity. However, S-1360 did form strong interactions with various amino acids and Mg2+

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 149


Docking in this study was performed using version 1.2 of the Genetic Optimization for Ligand Docking (GOLD) software. This uses a genetic algorithm to explore the ligand conformational flexibility with partial flexibility of the active site [30]. GOLD was tested on a dataset of over 300 complexes. GOLD succeeded in more than 70% of cases in reproducing the experimental bound conformations of the ligand [31]. GOLD requires that users define the specific binding site. For this study, Dayam and Neamati defined a 20 Å radius active site. D64 was selected as the center of the active site. The GOLD program then searches for a cavity within the defined area. The program also considers all the solvent accessible atoms in the defined area as active site atoms. "All docking runs were carried out using standard default settings with a population size of 100, a maximum number of 100,000 operations, a mutation, and crossover rate of 95" [29]. At the end of each run, GOLD reported all the predicted bound conformations based on their fitness score. The fitness score consists of H-bonding, complex energy, and ligand

2.2.3. Validating the resistance profiles of "me-too" Raltegravir analogs using docking studies

Serrao et al. sought to validate the resistance profiles of me-too Raltegravir analogs [18]. There are minor variations in the in vitro activity of the numerous me-too integrase inhibitors. The researchers believed that the development of me-too compounds could possibly yield a relatively low amount of clinical success due to their similarities [18]. It is still possible for a Raltegravir me-too analog to become a second-generation integrase inhibitor. To elucidate this viewpoint, the researchers utilized the molecular docking program GOLD version 3.2 to conduct a docking study. Serrao et al. used the structure of 1BL3 complexed with an Mg2+

Serrao et al. proposed that residues essential to the compounds' interaction with HIV-1 integrase would be prime candidates for resistance mutation. "Raltegravir makes direct interactions with three residues encompassing the [IN] catalytic motif (D64, D116, E152)" [18]. The researchers wanted to predict the interaction residues of Raltegravir's analogs in a similar way. They wanted to show that the compounds would have little success in viral eradication. Because S-1360 was the one of the first clinical IN inhibitor candidates, the researchers thought it would be interesting to look at the interactions between S-1360 and 1BL3 and compare with that of Raltegravir. The researchers found that there are identical interactions between the two drugs (D64, T66, D116, Y143, Q148, E152, and N155). Raltegravir showed an additional interaction with E92. While this observation has been confirmed by clinical experiments, the E92Q mutation has conferred upwards of a sevenfold viral resistance to Raltegravir [32–34].

ion in the cavity where 3<sup>0</sup>

internal energy.

ion, and various me-too compounds.

#### 2.2.2. Using docking studies to predict the binding mode of S-1360

It is very important to predict a bioactive conformation of a ligand, but the task becomes difficult when the receptor site has a region with unusual conformational flexibility. With the numerous crystal structures available for HIV-1 integrase, there are numerous differences in the active site regions in the core domains of IN. S-1360 was one of the first beta-diketoacid IN inhibitors to enter clinical studies. Dayam and Neamati sought to predict the bioactive (active site bound) conformations of S-1360 [29]. To achieve this, the researchers performed extensive docking studies with three different crystal structures. The study was extended to include 5CITEP and a bis-diketoacid (BDKA).

To predict the binding mode of S-1360, 104 unique conformations within a 20 kcal/mol energy range were generated using catConf module of the Catalyst. All 104 conformations of S-1360 were docked into the active sites A, B, and C (PDB: 1QS4, 1BIS, and 1BL3, respectively). Based on GOLD fitness scores, 10 conformers with highest scores were selected for further analysis. The researchers noted that S-1360 adopted very different binding orientations inside the active sites for A, B, and C. In the A active site, the bound conformation with the highest GOLD fitness score was found 102 times of 200 conformations. S-1360 occupies a space near D64, D116, N120, and Mg2+ ion. In active site B, the highly favorable conformation of S-1360 is found 62 times. The triazole and the diketoacid moiety of S-1360 occupy a deep cavity surrounded by I151, N155, V75, and Q62. The groups show favorable van der Waals and electrostatic interactions with D64, I151, E152, and N155 (Figure 2). The highly favorable binding conformations of the C active site are found 172 times. The researchers also compared the best binding orientations of S-1360 in the active sites from the three different crystal structures of HIV-1 integrase. Dayam and Neamati observed that S-1360 in the A active site achieves a planar conformation and interacts with various residues throughout the active site. In this orientation, S-1360 forms H-bonding interactions with K159 and N120. The two oxygen

Figure 2. 1BL3 active site with the residues that contribute to antiviral resistance highlighted.

atoms from the furan ring and keto group for coordinate bonds with the Mg2+ ions. This conformation appears to be stable because it occurs 102 out of 200 times. The binding site conformation of S-1360 inside the C active site is also very stable because of its 172 appearances out of 200. While these conformations of A and C are stable, this conformation is not in line with experimentally observed results. S-1360 selectively inhibits strand transfer reactions of HIV-1 integrase, but S-1360 in A and C did not interact with amino acids in the strand transfer (ST) cavity. However, S-1360 did form strong interactions with various amino acids and Mg2+ ion in the cavity where 3<sup>0</sup> -processing of IN is believed to be carried out.

development on this drug. There should be a distinction made between me-too drugs and second-generation drugs [18]. A second-generation inhibitor needs to exhibit a new mode of action. Secondly, a second-generation drug needs to show significantly improved potency or decreased toxicity. A major problem with second-generation drugs is cross resistance, so these

It is very important to predict a bioactive conformation of a ligand, but the task becomes difficult when the receptor site has a region with unusual conformational flexibility. With the numerous crystal structures available for HIV-1 integrase, there are numerous differences in the active site regions in the core domains of IN. S-1360 was one of the first beta-diketoacid IN inhibitors to enter clinical studies. Dayam and Neamati sought to predict the bioactive (active site bound) conformations of S-1360 [29]. To achieve this, the researchers performed extensive docking studies with three different crystal structures. The study was extended to include

To predict the binding mode of S-1360, 104 unique conformations within a 20 kcal/mol energy range were generated using catConf module of the Catalyst. All 104 conformations of S-1360 were docked into the active sites A, B, and C (PDB: 1QS4, 1BIS, and 1BL3, respectively). Based on GOLD fitness scores, 10 conformers with highest scores were selected for further analysis. The researchers noted that S-1360 adopted very different binding orientations inside the active sites for A, B, and C. In the A active site, the bound conformation with the highest GOLD fitness score was found 102 times of 200 conformations. S-1360 occupies a space near D64, D116, N120, and Mg2+ ion. In active site B, the highly favorable conformation of S-1360 is found 62 times. The triazole and the diketoacid moiety of S-1360 occupy a deep cavity surrounded by I151, N155, V75, and Q62. The groups show favorable van der Waals and electrostatic interactions with D64, I151, E152, and N155 (Figure 2). The highly favorable binding conformations of the C active site are found 172 times. The researchers also compared the best binding orientations of S-1360 in the active sites from the three different crystal structures of HIV-1 integrase. Dayam and Neamati observed that S-1360 in the A active site achieves a planar conformation and interacts with various residues throughout the active site. In this orientation, S-1360 forms H-bonding interactions with K159 and N120. The two oxygen

Figure 2. 1BL3 active site with the residues that contribute to antiviral resistance highlighted.

drugs should maintain potency, but avoid this cross-resistance.

2.2.2. Using docking studies to predict the binding mode of S-1360

5CITEP and a bis-diketoacid (BDKA).

148 Molecular Docking

Docking in this study was performed using version 1.2 of the Genetic Optimization for Ligand Docking (GOLD) software. This uses a genetic algorithm to explore the ligand conformational flexibility with partial flexibility of the active site [30]. GOLD was tested on a dataset of over 300 complexes. GOLD succeeded in more than 70% of cases in reproducing the experimental bound conformations of the ligand [31]. GOLD requires that users define the specific binding site. For this study, Dayam and Neamati defined a 20 Å radius active site. D64 was selected as the center of the active site. The GOLD program then searches for a cavity within the defined area. The program also considers all the solvent accessible atoms in the defined area as active site atoms. "All docking runs were carried out using standard default settings with a population size of 100, a maximum number of 100,000 operations, a mutation, and crossover rate of 95" [29]. At the end of each run, GOLD reported all the predicted bound conformations based on their fitness score. The fitness score consists of H-bonding, complex energy, and ligand internal energy.

#### 2.2.3. Validating the resistance profiles of "me-too" Raltegravir analogs using docking studies

Serrao et al. sought to validate the resistance profiles of me-too Raltegravir analogs [18]. There are minor variations in the in vitro activity of the numerous me-too integrase inhibitors. The researchers believed that the development of me-too compounds could possibly yield a relatively low amount of clinical success due to their similarities [18]. It is still possible for a Raltegravir me-too analog to become a second-generation integrase inhibitor. To elucidate this viewpoint, the researchers utilized the molecular docking program GOLD version 3.2 to conduct a docking study. Serrao et al. used the structure of 1BL3 complexed with an Mg2+ ion, and various me-too compounds.

Serrao et al. proposed that residues essential to the compounds' interaction with HIV-1 integrase would be prime candidates for resistance mutation. "Raltegravir makes direct interactions with three residues encompassing the [IN] catalytic motif (D64, D116, E152)" [18]. The researchers wanted to predict the interaction residues of Raltegravir's analogs in a similar way. They wanted to show that the compounds would have little success in viral eradication. Because S-1360 was the one of the first clinical IN inhibitor candidates, the researchers thought it would be interesting to look at the interactions between S-1360 and 1BL3 and compare with that of Raltegravir. The researchers found that there are identical interactions between the two drugs (D64, T66, D116, Y143, Q148, E152, and N155). Raltegravir showed an additional interaction with E92. While this observation has been confirmed by clinical experiments, the E92Q mutation has conferred upwards of a sevenfold viral resistance to Raltegravir [32–34]. The researchers' data could significantly validate the reliability of their docking technique. The researchers then moved on to describing the interactions between HIV-1 integrase and each most potent analog of Raltegravir. On the several compounds that were used in this follow up study, most all of them interacted in the same binding pocket that Raltegravir is active in. If the researchers' predictions are correct, these candidate drugs will fail to replace Raltegravir. The researcher's note, while there is always the possibility for me-too drugs to evolve into blockbuster drugs, the studied HIV-1 integrase "drugs appear to have a small chance of improving the clinical outlook of HIV patients with Raltegravir viral strains" [18].

Pharmaceuticals and Lilly Research Laboratories collaborated to produce Nelfinavir [42]. The structure of Nelfinavir is very similar to the structure of Saquinavir, but Nelfinavir contains a couple of changes. Labile components in Saquinavir were replaced with a hydroxytoluene amide group, however, this modification resulted in reduced potency. Drug developers replaced the phenyl group with a phenylthio group. This phenylthio group was better able to fill the hydrophobic pocket of the HIV-1 protease active site [43]. With an IC50 = 2 nM,

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 151

As shown with HIV-1 integrase inhibitors, resistance persists to be a pressing problem in the treatment plans for HIV-1. Because Saquinavir and Nelfinavir have similar structures, there are different, yet highly overlapping sets of amino acids substitution mutations that confer to drug resistance. The mutations that affect the binding site for Saquinavir are G84, I84, or L90. For Nelfinavir, the only difference from the Saquinavir mutation is D30 instead of G48 [41]. While these amino acids affect the binding pocket, there are other overlapping sets of amino acids that when mutated elsewhere in the HIV-1 protease enzyme confers antiviral resistance. These sites include L10, M46, L63, A71, and N88. Because many of the HIV-1 protease inhibitors on the market right now are very similar in structure, it is not surprising that there is a high

There are several different methods to interpret the resistant behavior of HIV-1 from genotypic data. A physics-based approach of docking has seen an influx of use by researchers in evaluating the energy interactions of the protein-inhibitor complexes. This technique has been widely used to look at the interactions between HIV-1 protease and its inhibitors. In 2005, Jenwitheesuk and Samudrala completed a study that used a protein-inhibitor docking approach to determine the correlation between experimentally and computer calculated protease inhibitor binding affinities [44]. The researchers also supplemented their findings with a molecular dynamics protocol [45]. This was used in part because most docking programs utilize a rigid protein protocol. HIV-1 protease has special flaps that are in motion upon binding. Since the structure of target protein is rigid, the opening and closing of the flaps is not performed [46]. This protocol was used to simulate the flexible nature between the ligand and the enzyme. The researchers used the X-ray crystal structures of various wild-type HIV-1 protease-inhibitor complexes. For Saquinavir and Nelfinavir, the researchers selected 1HXB and 1OHR, respectively (Figures 3 and 4). The researchers then substituted the wild-type side

When preparing the inhibitor structure, the researchers treated them as an all atom entry. By doing so this filled the empty valences with hydrogen. All the rotatable bonds in the inhibitors were also allowed to rotate freely. The researchers used AutoDock version 3.0.5 with a Lamarckian genetic algorithm to carry out docking calculations. Genetic algorithms use the idea of natural genetics and biological evolution. There are specific values describing the ligand with respect to the protein (translation, orientation, and conformation). These are described at state variables and in the genetic algorithm (GA), each state variable corresponds to a gene. In genetic algorithms, the genotype is from the ligand's state, and the phenotype

Nelfinavir is also a very potent HIV-1 protease inhibitor.

degree of cross-resistance between the drugs.

chains with a mutant side chain.

2.3.2. Predicting HIV-1 protease resistance with docking studies

#### 2.3. HIV-1 protease

An essential element in the HIV life cycle is HIV-1 protease. It is a retroviral aspartyl protease. HIV-1 protease is a homodimer, with each subunit made up of 99 amino acids [35]. Gag and Pol polyproteins are cleaved by this protease [36]. When these are cleaved at the appropriate places, a mature and infectious HIV virion is produced. When an effective HIV protease is blocked, the HIV virus is not infectious [37]. HIV's ability to replicate and infect additional cells can be disrupted by mutation of the HIV protease active site or inhibition [38]. For this reason, HIV protease has seen a massive amount of research money in developing HIV-1 protease inhibitors.

HIV-1 protease is a homodimeric enzyme. Two aspartic acid residues that are essential for catalysis [39], D25 and D25, are located on each monomer. Asp-Thr-Gly sequence is present in HIV-1 protease, but this is conserved among other mammalian aspartic protease enzymes. There are extended beta-sheet regions on each monomer, and these are known as "the flap". This makes up the hydrophobic substrate binding cavity with the two aspartyl residues on the bottom. HIV-1 proteases are highly selective, and very catalytically active in hydrolyzing peptide bonds. While the mechanism is similar to many known features of aspartic proteases, the full detailed mechanism of this enzyme has not been fully understood [40].

#### 2.3.1. Saquinavir and Nelfinavir—HIV-1 protease inhibitors and their ensuing resistance

The ideal HIV-1 protease inhibitor should be potent and specific for HIV-1 protease compared to other mammalian aspartic acid proteases [41]. The drugs should also have good bioavailability and duration in human bodies. There were no known inhibitors of HIV-1 protease when it was first determined to be a good target for antiviral therapy. A good starting place to look was the type of enzyme that HIV-1 protease was, an aspartic acid protease.

When researchers were designing HIV-1 protease inhibitors, it was noted that there was a stereocenter in the drug that correlated with the drug's activity. The transition state hydroxyl group needed to be in the R-stereochemistry or else the drug completely lost its activity. This discovery led researchers to identify Ro-31-8959, or Saquinavir, as a prime candidate for further studies because of this characteristic. Saquinavir has an IC50 < 0.37 nM for HIV-1 protease and does not inhibit other aspartic acid proteases, making it highly potent. While the drug is potent, it shows poor oral bioavailability—only 4% [41]. Researchers attribute this to the high molecular weight of the drug and the large number of amide bonds. Agouron Pharmaceuticals and Lilly Research Laboratories collaborated to produce Nelfinavir [42]. The structure of Nelfinavir is very similar to the structure of Saquinavir, but Nelfinavir contains a couple of changes. Labile components in Saquinavir were replaced with a hydroxytoluene amide group, however, this modification resulted in reduced potency. Drug developers replaced the phenyl group with a phenylthio group. This phenylthio group was better able to fill the hydrophobic pocket of the HIV-1 protease active site [43]. With an IC50 = 2 nM, Nelfinavir is also a very potent HIV-1 protease inhibitor.

As shown with HIV-1 integrase inhibitors, resistance persists to be a pressing problem in the treatment plans for HIV-1. Because Saquinavir and Nelfinavir have similar structures, there are different, yet highly overlapping sets of amino acids substitution mutations that confer to drug resistance. The mutations that affect the binding site for Saquinavir are G84, I84, or L90. For Nelfinavir, the only difference from the Saquinavir mutation is D30 instead of G48 [41]. While these amino acids affect the binding pocket, there are other overlapping sets of amino acids that when mutated elsewhere in the HIV-1 protease enzyme confers antiviral resistance. These sites include L10, M46, L63, A71, and N88. Because many of the HIV-1 protease inhibitors on the market right now are very similar in structure, it is not surprising that there is a high degree of cross-resistance between the drugs.

#### 2.3.2. Predicting HIV-1 protease resistance with docking studies

The researchers' data could significantly validate the reliability of their docking technique. The researchers then moved on to describing the interactions between HIV-1 integrase and each most potent analog of Raltegravir. On the several compounds that were used in this follow up study, most all of them interacted in the same binding pocket that Raltegravir is active in. If the researchers' predictions are correct, these candidate drugs will fail to replace Raltegravir. The researcher's note, while there is always the possibility for me-too drugs to evolve into blockbuster drugs, the studied HIV-1 integrase "drugs appear to have a small chance of improving

An essential element in the HIV life cycle is HIV-1 protease. It is a retroviral aspartyl protease. HIV-1 protease is a homodimer, with each subunit made up of 99 amino acids [35]. Gag and Pol polyproteins are cleaved by this protease [36]. When these are cleaved at the appropriate places, a mature and infectious HIV virion is produced. When an effective HIV protease is blocked, the HIV virus is not infectious [37]. HIV's ability to replicate and infect additional cells can be disrupted by mutation of the HIV protease active site or inhibition [38]. For this reason, HIV protease has seen a massive amount of research money in developing HIV-1

HIV-1 protease is a homodimeric enzyme. Two aspartic acid residues that are essential for catalysis [39], D25 and D25, are located on each monomer. Asp-Thr-Gly sequence is present in HIV-1 protease, but this is conserved among other mammalian aspartic protease enzymes. There are extended beta-sheet regions on each monomer, and these are known as "the flap". This makes up the hydrophobic substrate binding cavity with the two aspartyl residues on the bottom. HIV-1 proteases are highly selective, and very catalytically active in hydrolyzing peptide bonds. While the mechanism is similar to many known features of aspartic proteases,

The ideal HIV-1 protease inhibitor should be potent and specific for HIV-1 protease compared to other mammalian aspartic acid proteases [41]. The drugs should also have good bioavailability and duration in human bodies. There were no known inhibitors of HIV-1 protease when it was first determined to be a good target for antiviral therapy. A good starting place to look

When researchers were designing HIV-1 protease inhibitors, it was noted that there was a stereocenter in the drug that correlated with the drug's activity. The transition state hydroxyl group needed to be in the R-stereochemistry or else the drug completely lost its activity. This discovery led researchers to identify Ro-31-8959, or Saquinavir, as a prime candidate for further studies because of this characteristic. Saquinavir has an IC50 < 0.37 nM for HIV-1 protease and does not inhibit other aspartic acid proteases, making it highly potent. While the drug is potent, it shows poor oral bioavailability—only 4% [41]. Researchers attribute this to the high molecular weight of the drug and the large number of amide bonds. Agouron

the full detailed mechanism of this enzyme has not been fully understood [40].

was the type of enzyme that HIV-1 protease was, an aspartic acid protease.

2.3.1. Saquinavir and Nelfinavir—HIV-1 protease inhibitors and their ensuing resistance

the clinical outlook of HIV patients with Raltegravir viral strains" [18].

2.3. HIV-1 protease

150 Molecular Docking

protease inhibitors.

There are several different methods to interpret the resistant behavior of HIV-1 from genotypic data. A physics-based approach of docking has seen an influx of use by researchers in evaluating the energy interactions of the protein-inhibitor complexes. This technique has been widely used to look at the interactions between HIV-1 protease and its inhibitors. In 2005, Jenwitheesuk and Samudrala completed a study that used a protein-inhibitor docking approach to determine the correlation between experimentally and computer calculated protease inhibitor binding affinities [44]. The researchers also supplemented their findings with a molecular dynamics protocol [45]. This was used in part because most docking programs utilize a rigid protein protocol. HIV-1 protease has special flaps that are in motion upon binding. Since the structure of target protein is rigid, the opening and closing of the flaps is not performed [46]. This protocol was used to simulate the flexible nature between the ligand and the enzyme. The researchers used the X-ray crystal structures of various wild-type HIV-1 protease-inhibitor complexes. For Saquinavir and Nelfinavir, the researchers selected 1HXB and 1OHR, respectively (Figures 3 and 4). The researchers then substituted the wild-type side chains with a mutant side chain.

When preparing the inhibitor structure, the researchers treated them as an all atom entry. By doing so this filled the empty valences with hydrogen. All the rotatable bonds in the inhibitors were also allowed to rotate freely. The researchers used AutoDock version 3.0.5 with a Lamarckian genetic algorithm to carry out docking calculations. Genetic algorithms use the idea of natural genetics and biological evolution. There are specific values describing the ligand with respect to the protein (translation, orientation, and conformation). These are described at state variables and in the genetic algorithm (GA), each state variable corresponds to a gene. In genetic algorithms, the genotype is from the ligand's state, and the phenotype

protease sequences. This larger sample size could include all of the reported resistant mutations. Jenwitheesuk and Samudrala also added a protein-inhibitor relaxation feature to their protocol. Their protocol was also able to consider the rearrangement of the side chain on the active site surface. The relatively short MD simulation of 0.1 ps had a significant effect on the flap region (which moved away from the binding pocket—RMSD = 0.54 Å), yet was not long enough to affect the main chain of the protein. Using this protocol, the resistance and suscep-

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 153

This study looked at the two key mutations discussed earlier—Asp30Asn and Gly48Val. In this study, docking with the molecular dynamics implementations always failed to identify as a cause of drug resistance. This suggests that researchers should not rely solely on one method or system in making decisions about therapeutic regimens without consulting other methods, resources, and techniques. This study was still able to determine other mutations around the binding pocket. The docking with MD simulation implementation could identify mutations that correspond with high levels of resistance of Amprenavir (another kind of HIV-1 protease inhibitor)—I50V and a combination of I84V + L90 M and I54V + V82A + I84V + L90 M. These

American trypanosomiasis, or Chagas disease, is caused by the protist Trypanosoma cruzi. Many times there are no early signs of infection but over the course of the infection symptoms can range from a mild fever, swollen lymph nodes, or headaches. If the infection progresses further, the symptoms can include enlarged ventricles of the heart, which will ultimately lead to heart failure. This infection is most common in Mexico, Central America, and South America, and an estimated 6.6 million people are living with this parasite [48] The most common ways that the disease is spread are eating contaminated food, from mother to her fetus, and blood or organ transfusions [49]. While the knowledge of this parasite has grown remarkably,

Over the years, there has been a recent interest in drug repurposing (also known as drug repositioning). The process involves using known and approved medications—and sometimes discontinued drugs from other drug trials—and using them for a new clinical applications other than their intended treatment. Drug repurposing is gaining popularity due to the fact that within the past few decades there has been a significant decline in the number of safe and effective drugs being developed for the pharmaceutical market. Pharmaceutical companies are not inclined to fund research or product design because development of a new drug is a long and costly process [51]. One of the major benefits of trying to repurpose drugs is the reduced

Bellera et al. present computer-aided identification of approved drugs Clofazimine, Benidipine, and Saquinavir as potential trypanocidal compounds [50]. The major drug target is cruzipain (Cz). Cz is the major cysteine protease of the parasite. This protease is essential for replication of the intracellular form of the parasite. Bellera et al. compiled a 147 compound dataset. This data set was balanced with 77 Cz inhibitors and 70 non-inhibitors. The researchers then used docking studies on Saquinavir, Benidipine, Clofazimine, and the inactive verapamil. The protein to be

there have been no medications to treat Chagas disease in the last 40 years [50].

tibility predictions from Nelfinavir and Saquinavir were 86 and 94%, respectively [45].

mutations are cross resistant with Nelfinavir and Saquinavir.

cost of researching and developing a novel drug from scratch.

2.4. Repurposing HIV-1 protease inhibitors

Figure 3. 1HXR mutated with interactions between the binding pocket and Saquinavir.

Figure 4. 1OHR mutated with amino acid interactions and Nelfinavir in the binding pocket.

comes from the atomic coordinates [46]. When molecular docking is performed, the fitness of the gene is referred to as the total interaction energy between the ligand and the protein. The GA comes into play by mating random pairs of individuals to induce crossover. In this scenario, some offspring undergo random mutation. The genes are selected from the current generation based off their fitness scores. This process is repeated for multiple generations to produce a ligand and protein interaction that has the most fitness. In the research conducted by Jenwitheesuk and Samudrala [45], there were a total of 27,000 generations. AutoDock generates the energy terms for inter-molecular energy, internal energy of the ligand, and torsional free energy. When the researchers determined the final docked energy of the protein ligand complex, the inter-molecular energy, and the internal energy of the ligand was added.

In the results of this study [45], Jenwitheesuk and Samudrala saw a significant improvement in the correlation coefficient when supplementing their docking procedure with MD simulation to provide a flexible nature of the protein (correlation coefficient changed from 0.38 to 0.87). The researchers were also able to see that their docking with dynamic protocol was 64% accurate for phenotypically resistant profiles and 83% accurate for phenotypically susceptible groups. There was a previous study done by Shenderovich et al. [47]. While this study followed a similar protocol to the one followed by Jenwitheesuk and Samudrala, Shenderovich et al. only used 50 HIV-1 protease sequences. Jenwitheesuk and Samudrala used 1792 HIV-1 protease sequences. This larger sample size could include all of the reported resistant mutations. Jenwitheesuk and Samudrala also added a protein-inhibitor relaxation feature to their protocol. Their protocol was also able to consider the rearrangement of the side chain on the active site surface. The relatively short MD simulation of 0.1 ps had a significant effect on the flap region (which moved away from the binding pocket—RMSD = 0.54 Å), yet was not long enough to affect the main chain of the protein. Using this protocol, the resistance and susceptibility predictions from Nelfinavir and Saquinavir were 86 and 94%, respectively [45].

This study looked at the two key mutations discussed earlier—Asp30Asn and Gly48Val. In this study, docking with the molecular dynamics implementations always failed to identify as a cause of drug resistance. This suggests that researchers should not rely solely on one method or system in making decisions about therapeutic regimens without consulting other methods, resources, and techniques. This study was still able to determine other mutations around the binding pocket. The docking with MD simulation implementation could identify mutations that correspond with high levels of resistance of Amprenavir (another kind of HIV-1 protease inhibitor)—I50V and a combination of I84V + L90 M and I54V + V82A + I84V + L90 M. These mutations are cross resistant with Nelfinavir and Saquinavir.

#### 2.4. Repurposing HIV-1 protease inhibitors

comes from the atomic coordinates [46]. When molecular docking is performed, the fitness of the gene is referred to as the total interaction energy between the ligand and the protein. The GA comes into play by mating random pairs of individuals to induce crossover. In this scenario, some offspring undergo random mutation. The genes are selected from the current generation based off their fitness scores. This process is repeated for multiple generations to produce a ligand and protein interaction that has the most fitness. In the research conducted by Jenwitheesuk and Samudrala [45], there were a total of 27,000 generations. AutoDock generates the energy terms for inter-molecular energy, internal energy of the ligand, and torsional free energy. When the researchers determined the final docked energy of the protein ligand complex, the inter-molecular energy, and the internal energy of the ligand was added. In the results of this study [45], Jenwitheesuk and Samudrala saw a significant improvement in the correlation coefficient when supplementing their docking procedure with MD simulation to provide a flexible nature of the protein (correlation coefficient changed from 0.38 to 0.87). The researchers were also able to see that their docking with dynamic protocol was 64% accurate for phenotypically resistant profiles and 83% accurate for phenotypically susceptible groups. There was a previous study done by Shenderovich et al. [47]. While this study followed a similar protocol to the one followed by Jenwitheesuk and Samudrala, Shenderovich et al. only used 50 HIV-1 protease sequences. Jenwitheesuk and Samudrala used 1792 HIV-1

Figure 3. 1HXR mutated with interactions between the binding pocket and Saquinavir.

152 Molecular Docking

Figure 4. 1OHR mutated with amino acid interactions and Nelfinavir in the binding pocket.

American trypanosomiasis, or Chagas disease, is caused by the protist Trypanosoma cruzi. Many times there are no early signs of infection but over the course of the infection symptoms can range from a mild fever, swollen lymph nodes, or headaches. If the infection progresses further, the symptoms can include enlarged ventricles of the heart, which will ultimately lead to heart failure. This infection is most common in Mexico, Central America, and South America, and an estimated 6.6 million people are living with this parasite [48] The most common ways that the disease is spread are eating contaminated food, from mother to her fetus, and blood or organ transfusions [49]. While the knowledge of this parasite has grown remarkably, there have been no medications to treat Chagas disease in the last 40 years [50].

Over the years, there has been a recent interest in drug repurposing (also known as drug repositioning). The process involves using known and approved medications—and sometimes discontinued drugs from other drug trials—and using them for a new clinical applications other than their intended treatment. Drug repurposing is gaining popularity due to the fact that within the past few decades there has been a significant decline in the number of safe and effective drugs being developed for the pharmaceutical market. Pharmaceutical companies are not inclined to fund research or product design because development of a new drug is a long and costly process [51]. One of the major benefits of trying to repurpose drugs is the reduced cost of researching and developing a novel drug from scratch.

Bellera et al. present computer-aided identification of approved drugs Clofazimine, Benidipine, and Saquinavir as potential trypanocidal compounds [50]. The major drug target is cruzipain (Cz). Cz is the major cysteine protease of the parasite. This protease is essential for replication of the intracellular form of the parasite. Bellera et al. compiled a 147 compound dataset. This data set was balanced with 77 Cz inhibitors and 70 non-inhibitors. The researchers then used docking studies on Saquinavir, Benidipine, Clofazimine, and the inactive verapamil. The protein to be used in the docking studies was 1ME4. This protein was a crystal structure of one reversible inhibitor that was complexed with Cz. The compounds were docked according to the Lamarckian genetic algorithm. The active site was defined as a 19 <sup>15</sup> 15 Å3 grid. The researchers performed 100 docking runs for each compound. The docking active site was treated as a rigid molecule and the ligands were treated as flexible. The researchers used Autodock 4.2 to analyze the results of their docking study. The binding results from the docking studies correlated with experimental evidence. The scores for Saquinavir, Benidipine, and Coldazimine were 12.76, 8.42, and 7.36 kcal/mol, respectively. However, the inactive verapamil compound was only 6.37 kcal/mol [50].

(PGPH) in the β1subunit, trypsin-like (T-L) in the β2 subunit and CT-L activities in the β5

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 155

Degradation of proteins in the cytoplasm and nucleus of eukaryotic cells can affect: regulation of cellular pathways particularly cell growth and proliferation, apoptosis, DNA repair, transcription, immune responses, and signaling processes [56]. Inhibition of proteasomes has therefore become an attractive target for anticancer therapies. The drug Bortezomib was developed by Millennium Pharmaceuticals Inc. and received regular approval by the Food and Drug Administration in 2005 as the first proteasome inhibitor to be used for the treatment of multiple myeloma [57]. Bortezomib is a peptide boronate inhibitor of the proteasome and it selectively binds to the protein to inhibit its chymotryptic-like activity [58]. The anticancer effects demonstrated by Bortezomib are mainly observed by the inhibition of the transcription factor NFkB and the promotion of apoptosis in rapidly dividing cells. While Bortezomib is considered a successful cancer treatment, many reports of adverse side effects have driven

In the race to discover a more efficacious proteasome inhibitor, molecular docking has been an extremely beneficial tool utilized by researchers to expedite the exacting process. In silico high throughput screening of multiple chemical libraries identified the compound PI-083 as a potential inhibitor due to its potency (IC50 = 1 μM). Molecular docking of PI-083 to the 20S proteasome was performed by the GLIDE computer program, version 3.0 (Schrödinger, LLC, New York, NY). The GLIDE program used for the docking and grid generation was set using default options and parameters. The X-ray structure of yeast 20S proteasome complexed with Bortezomib revealed that the pyrazine ring in the Bortezomib forms a hydrogen bond with Asp114 from the β6 subunit of the proteasome. As visualized in Figure 5, Bortezomib also forms hydrogen bonds with T21, T1, G47, and A49 residues located in the active site. PI-083 possesses a pyridine ring and it was docked to a model derived from the Bortezomibproteasome complex (PBD ID: 2F16). The docking studies revealed that PI-083 and Bortezomib have similar binding mechanisms to the active site of the CT-L enzyme within the proteasome [55]. The molecular docking combined with in vivo studies of PI-083 are indicative that the compound is successful in tumor suppression which insinuates a need for further clinical

Figure 5. The Bortezomib ligand positioned in the active site of the yeast 20S proteasome crystal structure. The key

researchers to develop a more potent and selective proteasome inhibitor [59].

research in regards to PI-083 as an anticancer therapy.

residues T1, T21, G47, A49, and D114 in the active site are shown.

subunit [55].

## 3. Identification of medicine for cancer

Cancer is one of the most devastating and destructive diseases that is known to be a persistent public health threat. As of the year 2016, cancer is the second leading cause of death in the United States. There were an estimated 1,685,210 new cases and 595,690 deaths resulting from cancer [52]. Along with the high rate of incidence exacerbating the pressure already felt by researchers to discover a cure, the mechanisms of the disease add another level of complexity that must be outmaneuvered. Many cancer cells lack molecular targets making it extremely difficult for anticancer chemotherapeutics to be fully effective. Toxicity against normal tissues can develop from anticancer therapy, which leads to unwanted side effects. Due to the adverse effects, many anticancer chemotherapeutics are given at suboptimal doses which typically results in failure of therapy, drug resistance, and metastatic disease [53]. The complications associated with cancer demonstrate the critical need for the development of new anticancer therapies that are successful with minimal undesired reactions. In order to aid in the task, many researchers are turning to in silico methods to expedite the process. Molecular docking is one of the most popular and reliable softwares available for drug discovery, design, and repurposing. Many researchers utilize molecular docking in cancer research because it provides great insight into protein-ligand interactions, ligand binding mechanisms, and knowledge of the optimal orientation of the ligand bound to its target to form the most stable complex. Molecular docking is an essential computational method that has demonstrated a promising future for the evolution of more effective and potent anticancer therapies.

#### 3.1. Docking for identifying novel proteasome inhibitors and understanding the binding mechanisms

A variety of cancer therapeutics already exists and is available to patients; many of these therapies attempt to have a specific molecular target in order to eradicate the cancerous cells. One protein that receives extensive attention due to its pivotal biological role in eukaryotic cells is the proteasome. There are two major types of proteasomes such as the 20S proteasome, which is responsible for intracellular protein degradation and the 26S proteasome complex, which functions in the ubiquitin pathway as an ATP-dependent proteasome [54]. The 26S proteasome has three proteolytic activities including peptidyl glutamyl peptide hydrolase (PGPH) in the β1subunit, trypsin-like (T-L) in the β2 subunit and CT-L activities in the β5 subunit [55].

used in the docking studies was 1ME4. This protein was a crystal structure of one reversible inhibitor that was complexed with Cz. The compounds were docked according to the Lamarckian genetic algorithm. The active site was defined as a 19 <sup>15</sup> 15 Å3 grid. The researchers performed 100 docking runs for each compound. The docking active site was treated as a rigid molecule and the ligands were treated as flexible. The researchers used Autodock 4.2 to analyze the results of their docking study. The binding results from the docking studies correlated with experimental evidence. The scores for Saquinavir, Benidipine, and Coldazimine were 12.76, 8.42, and 7.36 kcal/mol, respectively. However, the inactive verapamil compound was only

Cancer is one of the most devastating and destructive diseases that is known to be a persistent public health threat. As of the year 2016, cancer is the second leading cause of death in the United States. There were an estimated 1,685,210 new cases and 595,690 deaths resulting from cancer [52]. Along with the high rate of incidence exacerbating the pressure already felt by researchers to discover a cure, the mechanisms of the disease add another level of complexity that must be outmaneuvered. Many cancer cells lack molecular targets making it extremely difficult for anticancer chemotherapeutics to be fully effective. Toxicity against normal tissues can develop from anticancer therapy, which leads to unwanted side effects. Due to the adverse effects, many anticancer chemotherapeutics are given at suboptimal doses which typically results in failure of therapy, drug resistance, and metastatic disease [53]. The complications associated with cancer demonstrate the critical need for the development of new anticancer therapies that are successful with minimal undesired reactions. In order to aid in the task, many researchers are turning to in silico methods to expedite the process. Molecular docking is one of the most popular and reliable softwares available for drug discovery, design, and repurposing. Many researchers utilize molecular docking in cancer research because it provides great insight into protein-ligand interactions, ligand binding mechanisms, and knowledge of the optimal orientation of the ligand bound to its target to form the most stable complex. Molecular docking is an essential computational method that has demonstrated a

promising future for the evolution of more effective and potent anticancer therapies.

3.1. Docking for identifying novel proteasome inhibitors and understanding the

A variety of cancer therapeutics already exists and is available to patients; many of these therapies attempt to have a specific molecular target in order to eradicate the cancerous cells. One protein that receives extensive attention due to its pivotal biological role in eukaryotic cells is the proteasome. There are two major types of proteasomes such as the 20S proteasome, which is responsible for intracellular protein degradation and the 26S proteasome complex, which functions in the ubiquitin pathway as an ATP-dependent proteasome [54]. The 26S proteasome has three proteolytic activities including peptidyl glutamyl peptide hydrolase

6.37 kcal/mol [50].

154 Molecular Docking

binding mechanisms

3. Identification of medicine for cancer

Degradation of proteins in the cytoplasm and nucleus of eukaryotic cells can affect: regulation of cellular pathways particularly cell growth and proliferation, apoptosis, DNA repair, transcription, immune responses, and signaling processes [56]. Inhibition of proteasomes has therefore become an attractive target for anticancer therapies. The drug Bortezomib was developed by Millennium Pharmaceuticals Inc. and received regular approval by the Food and Drug Administration in 2005 as the first proteasome inhibitor to be used for the treatment of multiple myeloma [57]. Bortezomib is a peptide boronate inhibitor of the proteasome and it selectively binds to the protein to inhibit its chymotryptic-like activity [58]. The anticancer effects demonstrated by Bortezomib are mainly observed by the inhibition of the transcription factor NFkB and the promotion of apoptosis in rapidly dividing cells. While Bortezomib is considered a successful cancer treatment, many reports of adverse side effects have driven researchers to develop a more potent and selective proteasome inhibitor [59].

In the race to discover a more efficacious proteasome inhibitor, molecular docking has been an extremely beneficial tool utilized by researchers to expedite the exacting process. In silico high throughput screening of multiple chemical libraries identified the compound PI-083 as a potential inhibitor due to its potency (IC50 = 1 μM). Molecular docking of PI-083 to the 20S proteasome was performed by the GLIDE computer program, version 3.0 (Schrödinger, LLC, New York, NY). The GLIDE program used for the docking and grid generation was set using default options and parameters. The X-ray structure of yeast 20S proteasome complexed with Bortezomib revealed that the pyrazine ring in the Bortezomib forms a hydrogen bond with Asp114 from the β6 subunit of the proteasome. As visualized in Figure 5, Bortezomib also forms hydrogen bonds with T21, T1, G47, and A49 residues located in the active site. PI-083 possesses a pyridine ring and it was docked to a model derived from the Bortezomibproteasome complex (PBD ID: 2F16). The docking studies revealed that PI-083 and Bortezomib have similar binding mechanisms to the active site of the CT-L enzyme within the proteasome [55]. The molecular docking combined with in vivo studies of PI-083 are indicative that the compound is successful in tumor suppression which insinuates a need for further clinical research in regards to PI-083 as an anticancer therapy.

Figure 5. The Bortezomib ligand positioned in the active site of the yeast 20S proteasome crystal structure. The key residues T1, T21, G47, A49, and D114 in the active site are shown.

Molecular docking has not only been successful in identifying potential proteasome inhibitors but it has also been beneficial in understanding the binding mechanism of proteasome inhibitors to the proteasome. One study conducted by Zhang et al. was focused on MG132 (Z-Leu-Leu-Leu-al), which is a structural component of peptide aldehydes selective and potent against the proteasome. Using the Insight II software, the proteins and ligands were prepared for docking. MG132 was then covalently docked to the β5 subunit of the 20S proteasome using GOLD version 4.0. The results showed that the docking of MG132 proposed two binding modes with low docking energies. More thorough analysis and the use of molecular dynamics simulations revealed that binding mode I was more stable than mode II. The computational methods utilized in this study resulted in the generation of a model that was able to reexamine the correlation of the structure and activity of proteasome inhibitors, specifically the interactions that take place at the P2 and P4 sites [60]. Observing the binding mode is advantageous for the improvement of existing proteasome inhibitors but also for the development of more potent inhibitors.

The 17 peptide aldehydes were then docked using GOLD software 4.0 with the β5 of the 20S proteasome based on the crystal structure of the first known inhibitor MG101 complexed with the 20S proteasome. The results of the docking experiment indicated that the size and length of the P3 side chain is critical to the activity of the peptide aldehyde. Compounds 3 and 4 which

providing the most active inhibition. The results from docking indicated that when a phenyl ester was used to replace a tert-butyl ester at P3 in the Boc-series, the Asp(OBzl) residue in compound 10 exhibited more active inhibition than Glu(OBzl) residue in compound 12. Also in the Boc-series, Ser(OBzl) in compound 15 has the most suitable length side chain because it demonstrated the most active inhibition to CT-L active site [61]. The docking results generated from this study highlighted the importance of the P3-position substitutes are vital for inhibitor

Peptide aldehydes are not the only compounds being considered as proteasome inhibitors for cancer therapeutics. Santoro et al. investigated whether or not cationic and anionic porphyrins can be used as inhibitors of the proteasome. Porphyrins are hydrophilic compounds that possess tumor localizing properties and are used in conjunction with red light for photodynamic therapy for the treatment of tumorous cells [62]. Cationic and anionic porphyrins were docked using AutoDock Vina to the 20S proteasome complexed with Bortezomib (PDB: 2F16). The cationic porphyrin H2T4 demonstrated similar inhibitory activity in all three catalytic sites of the proteasome when observed during in vivo studies. Docking of planar H2T4 with the 20S proteasome revealed the binding mechanism of the porphyrin to the proteasome. The results from the docking studies reconcile with the results of the inhibition studies, indicative that H2T4 has the potential to be a proteasome inhibitor. Along with the active ability of the porphyrin to inhibit the proteasome, the molecules also possess low toxicity, making them an attractive class of compounds to continue to evaluate as a form of anticancer therapy [63].

Besides proteasomes, several isoforms of carbonic anhydrases (CAs) have become an attractive anticancer drug target. Carbonic anhydrases are ubiquitous metalloenzymes broken up into four unrelated gene families; the α-CAs, β-CAs, γ-CAs, and δ-CAs. Mammals have 16 α-CAs isozymes that are different in their tissue distribution, catalytic activity, and subcellular localization [64]. The α-CAs are of particular interest because they have well established catalytic and inhibition mechanisms [65]. One α-CA in particular, CA IX, has potential to act as an anticancer drug target as it has the ability to act as a biological marker for certain tumors [66]. CA IX is an extracellular transmembrane-bound protein located in the gastrointestinal tract. When the enzyme is present in hypoxic conditions, CA IX is overexpressed and is observed to be associated with different types of cancer cells via the hypoxia inducible factor-1 (HIF-1). Overexpression also causes the environmental pH of a tumor to be lowered to acidic conditions [66]. The appeal of the CA IX as a potential anticancer drug is demonstrated by the fact

Amresh et al. used molecular docking and several others in silico methods to discover five potential CA IX inhibitors. AutoDock 4.2 was used to dock all the inhibitors to the crystal

Bu) residues at the P3 site

157

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898

are part of the Cbz series synthesized by Ma et al. possess Glu(O<sup>t</sup>

3.2. Docking for identifying inhibitors of CAs

that the enzyme has restricted expression in normal tissues (Table 2).

potency, which is essential for designing more effective proteasome inhibitors.

Ma et al. used the binding mechanism of MG132 as a comparison for docking their own series of peptide aldehyde derivatives in which they synthesized. A total of 17 different peptide aldehydes were developed and are listed in Table 2. Eight of the peptides are in the Cbz class at the R4 position and the other nine peptides are in the Boc class at the R4 position.


Table 2. Peptide aldehyde derivatives for the inhibition of 20S proteasome activity.

The 17 peptide aldehydes were then docked using GOLD software 4.0 with the β5 of the 20S proteasome based on the crystal structure of the first known inhibitor MG101 complexed with the 20S proteasome. The results of the docking experiment indicated that the size and length of the P3 side chain is critical to the activity of the peptide aldehyde. Compounds 3 and 4 which are part of the Cbz series synthesized by Ma et al. possess Glu(O<sup>t</sup> Bu) residues at the P3 site providing the most active inhibition. The results from docking indicated that when a phenyl ester was used to replace a tert-butyl ester at P3 in the Boc-series, the Asp(OBzl) residue in compound 10 exhibited more active inhibition than Glu(OBzl) residue in compound 12. Also in the Boc-series, Ser(OBzl) in compound 15 has the most suitable length side chain because it demonstrated the most active inhibition to CT-L active site [61]. The docking results generated from this study highlighted the importance of the P3-position substitutes are vital for inhibitor potency, which is essential for designing more effective proteasome inhibitors.

Peptide aldehydes are not the only compounds being considered as proteasome inhibitors for cancer therapeutics. Santoro et al. investigated whether or not cationic and anionic porphyrins can be used as inhibitors of the proteasome. Porphyrins are hydrophilic compounds that possess tumor localizing properties and are used in conjunction with red light for photodynamic therapy for the treatment of tumorous cells [62]. Cationic and anionic porphyrins were docked using AutoDock Vina to the 20S proteasome complexed with Bortezomib (PDB: 2F16). The cationic porphyrin H2T4 demonstrated similar inhibitory activity in all three catalytic sites of the proteasome when observed during in vivo studies. Docking of planar H2T4 with the 20S proteasome revealed the binding mechanism of the porphyrin to the proteasome. The results from the docking studies reconcile with the results of the inhibition studies, indicative that H2T4 has the potential to be a proteasome inhibitor. Along with the active ability of the porphyrin to inhibit the proteasome, the molecules also possess low toxicity, making them an attractive class of compounds to continue to evaluate as a form of anticancer therapy [63].

#### 3.2. Docking for identifying inhibitors of CAs

Molecular docking has not only been successful in identifying potential proteasome inhibitors but it has also been beneficial in understanding the binding mechanism of proteasome inhibitors to the proteasome. One study conducted by Zhang et al. was focused on MG132 (Z-Leu-Leu-Leu-al), which is a structural component of peptide aldehydes selective and potent against the proteasome. Using the Insight II software, the proteins and ligands were prepared for docking. MG132 was then covalently docked to the β5 subunit of the 20S proteasome using GOLD version 4.0. The results showed that the docking of MG132 proposed two binding modes with low docking energies. More thorough analysis and the use of molecular dynamics simulations revealed that binding mode I was more stable than mode II. The computational methods utilized in this study resulted in the generation of a model that was able to reexamine the correlation of the structure and activity of proteasome inhibitors, specifically the interactions that take place at the P2 and P4 sites [60]. Observing the binding mode is advantageous for the improvement of existing proteasome inhibitors but also for the development of

Ma et al. used the binding mechanism of MG132 as a comparison for docking their own series of peptide aldehyde derivatives in which they synthesized. A total of 17 different peptide aldehydes were developed and are listed in Table 2. Eight of the peptides are in the Cbz class at the R4 position and the other nine peptides are in the Boc class at the R4 position.

Compounds R4 position P3 position P2 position

 Cbz Phe Leu Cbz Arg(NO2) Leu Cbz Arg(Tos) Leu Cbz Napa Leu Boc Asp(OBzl) Phe Boc Asp(OBzl) Leu Boc Glu(OBzl) Phe Boc Glu(OBzl) Leu Boc Pro Phe Boc Pro Leu Boc Ser(OBzl) Leu Boc Thr(OBzl) Leu Boc Tyr(OBzl) Leu

Bu) Phe

Bu) Leu

Bu) Phe

Bu) Leu

1 Cbz Asp(O<sup>t</sup>

2 Cbz Asp(O<sup>t</sup>

3 Cbz Glu(O<sup>t</sup>

4 Cbz Glu(O<sup>t</sup>

Table 2. Peptide aldehyde derivatives for the inhibition of 20S proteasome activity.

more potent inhibitors.

156 Molecular Docking

Besides proteasomes, several isoforms of carbonic anhydrases (CAs) have become an attractive anticancer drug target. Carbonic anhydrases are ubiquitous metalloenzymes broken up into four unrelated gene families; the α-CAs, β-CAs, γ-CAs, and δ-CAs. Mammals have 16 α-CAs isozymes that are different in their tissue distribution, catalytic activity, and subcellular localization [64]. The α-CAs are of particular interest because they have well established catalytic and inhibition mechanisms [65]. One α-CA in particular, CA IX, has potential to act as an anticancer drug target as it has the ability to act as a biological marker for certain tumors [66]. CA IX is an extracellular transmembrane-bound protein located in the gastrointestinal tract. When the enzyme is present in hypoxic conditions, CA IX is overexpressed and is observed to be associated with different types of cancer cells via the hypoxia inducible factor-1 (HIF-1). Overexpression also causes the environmental pH of a tumor to be lowered to acidic conditions [66]. The appeal of the CA IX as a potential anticancer drug is demonstrated by the fact that the enzyme has restricted expression in normal tissues (Table 2).

Amresh et al. used molecular docking and several others in silico methods to discover five potential CA IX inhibitors. AutoDock 4.2 was used to dock all the inhibitors to the crystal

and is associated with aggressive tumor behavior [69]. The EGFR is the main activator in the downstream pathways for survival and growth signals such as p42/44 MAPK and PI3K/AKT pathways [70]. Inhibition of these pathways leads to apoptosis of cancer cells, making the

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 159

The mutations G719S, L858R, T790M, G719S/T790M, and T790M/L858R are commonly seen in patients with cancer because they modify the EGFR kinase activity [71]. García-Godoy et al. used molecular docking in order to study the interactions of EGFR inhibitors on the wild-type EGFR and mutant EGFR. For the wild-type human EGFR, the EGFR (PDB: 4ZAU) was complexed with the ligand AZD9291. Docking was also conducted on the EGFR containing the G719S mutation and the L858R mutation. The EGFR (PBD ID: 2ITN) was used with the G719S mutation and the EGFR (PDB: 2 EB3) was used with the L858R mutation. Both EGFRs were in complex with AMP-PNP. Results of this docking study indicated that in both complexes, M793 was an important residue in facilitating interactions between the ligand and the active site [71]. In the final docking study, docking was performed on the EGFR double mutants T790M/L858R and T790M/G719S. In the instance where the EGFR mutant T790M/ L858R was docked, the EGFR (PDB: 4JR5) was used and it was complexed with the ligand 3QY. The double mutant EGFR T790M/G719S (PDB: 3UG2) was also used and it was complexed with getfitinib (PDB: IRE). In both of the docking studies, the results revealed that there is a critical interaction between the ligand and the Met793 residue in the active site of the mutant EGFR [71]. Analysis of the results concluded that the interactions displayed in each case can be crucial evidence to why different cancer patients are more or less sensitive to certain treatments. This provides insight into how certain therapies should be considered circumstantial based on the mutation a patient may possess. The in silico methodology utilized in this study set a precedent for other researchers to use molecular docking to discover more

Mahajan et al. discerned the value of the EGFR as a target for anticancer therapy; using molecular docking they were able to discover potential EGFR inhibitors. Screening of 50,000 compounds was performed by LigPrep (version3.3; Schrodinger, LLC, 2015) in order to prepare a library of drugs to be tested by several in silico methods. After the library was prepared with LigPrep, the compounds were then screened against EGFR drug target using e-Pharmacophore, docking, pharmacophore, substructure, and similarity search [72]. The protein used in the docking studies is complexed with the inhibitor tak-285 and it was chosen for the study because it has the best X-ray resolution (1.50 Å) of the EGFR structure (PDB: 3POZ). The downloaded protein was prepared for docking using the Protein Preparation Wizard. Docking the compounds was performed by the Glide module (version3.6; Schrodinger, LLC, 2015) software and the first round of docking studies used the high throughput virtual screening setting. After all compounds had been screened, the top 30% of the best scoring compounds were then redocked using standard precision (SP) docking. Once those compounds had been docked, the top 30% of the best scoring compounds in SP docking were then re-docked using extra precision (XP) docking. A total of 1534 had been selected as compounds that bound to the EGFR with a respectable docking score [72]. Docking, along with e-Pharmacophore and pharmacophore in silico methods were able to narrow 50,000 compounds down to 200 compounds that showed potential for EGFR inhibition. Further computational methodology of the compounds revealed

EGFR a particularly promising area of cancer research.

drugs for EGFR inhibition.

Figure 6. The critical residues in the active site located on the CA IX (PDB: 3IAI): L91, L93, L198, V121, L135, L141, V143, P201, P202, W5, W209, F245, H96, H119, E106, T199, T200, H94, D132, Q92, N62, H64, S65, Q67, T69, and Q92 are displayed.

structure of CA IX (PDB: 3IAI) visualized in Figure 6. Coulombic electrostatic potential, van der Waals interaction represented as a Lennard-Jones12-6 dispersion/repulsion term and hydrogen bonding were addressed when evaluating the binding energy during docking. Docking orientations within 2.0 Å in root-mean square deviation tolerance were the parameters set in order to obtain the most favorable free energy of binding. The inhibitors with the best docking poses and scores were then subjected to post-docking energy minimization on Discovery Studio 3.5. The final structures were analyzed using PyMOL visualization programs and the receptor-inhibitor complexes were used to develop the pharmacophore model for further evaluation [67]. Docking simulations were also performed in order to identify the residues present in the active site of CA IX that interact with the inhibitors. The docking study revealed that residues: L91, L93, L198, V121, L135, L141, V143, P201, P202, W5, W209, F245, H96, H119, E106, T199, T200, H94, D132, Q92, and V131 formed either hydrophobic or aromatic interactions with the inhibitor. N62, H64, S65, Q67, T69, and Q92 were identified as the hydrophilic residues in the active site as well [67]. The results of the docking studies established 10 novel compounds as CA IX inhibitors. Further analysis of the docking scores narrowed the list even further to the top five scoring compounds which were: ZINC03363328, ZINC08828920, ZINC12941947, ZINC03622539, and ZINC16650541 [67]. The information obtained from this study has demonstrated the value of molecular docking in identifying new CA IX inhibitors that provide a promising future as an anticancer therapy.

#### 3.3. Docking for identifying inhibitors of EGFR

The epidermal growth factor receptor (EGFR) is another enticing biological target in the development of anticancer therapeutics. The EGFR is a family of tyrosine kinases that regulate many developmental, metabolic, and physiological processes. Binding of the epidermal growth factor to the family of kinases leads to homodimerization or heterodimerization of the EGFR. Mutations of EGFR gene, over expressed copies of the gene and EGFR protein overexpression lead to dysregulated TK activity which is observed in many tumors [68]. Overexpression of EGFR is frequently observed in breast, lung, ovarian, and prostate cancer and is associated with aggressive tumor behavior [69]. The EGFR is the main activator in the downstream pathways for survival and growth signals such as p42/44 MAPK and PI3K/AKT pathways [70]. Inhibition of these pathways leads to apoptosis of cancer cells, making the EGFR a particularly promising area of cancer research.

The mutations G719S, L858R, T790M, G719S/T790M, and T790M/L858R are commonly seen in patients with cancer because they modify the EGFR kinase activity [71]. García-Godoy et al. used molecular docking in order to study the interactions of EGFR inhibitors on the wild-type EGFR and mutant EGFR. For the wild-type human EGFR, the EGFR (PDB: 4ZAU) was complexed with the ligand AZD9291. Docking was also conducted on the EGFR containing the G719S mutation and the L858R mutation. The EGFR (PBD ID: 2ITN) was used with the G719S mutation and the EGFR (PDB: 2 EB3) was used with the L858R mutation. Both EGFRs were in complex with AMP-PNP. Results of this docking study indicated that in both complexes, M793 was an important residue in facilitating interactions between the ligand and the active site [71]. In the final docking study, docking was performed on the EGFR double mutants T790M/L858R and T790M/G719S. In the instance where the EGFR mutant T790M/ L858R was docked, the EGFR (PDB: 4JR5) was used and it was complexed with the ligand 3QY. The double mutant EGFR T790M/G719S (PDB: 3UG2) was also used and it was complexed with getfitinib (PDB: IRE). In both of the docking studies, the results revealed that there is a critical interaction between the ligand and the Met793 residue in the active site of the mutant EGFR [71]. Analysis of the results concluded that the interactions displayed in each case can be crucial evidence to why different cancer patients are more or less sensitive to certain treatments. This provides insight into how certain therapies should be considered circumstantial based on the mutation a patient may possess. The in silico methodology utilized in this study set a precedent for other researchers to use molecular docking to discover more drugs for EGFR inhibition.

structure of CA IX (PDB: 3IAI) visualized in Figure 6. Coulombic electrostatic potential, van der Waals interaction represented as a Lennard-Jones12-6 dispersion/repulsion term and hydrogen bonding were addressed when evaluating the binding energy during docking. Docking orientations within 2.0 Å in root-mean square deviation tolerance were the parameters set in order to obtain the most favorable free energy of binding. The inhibitors with the best docking poses and scores were then subjected to post-docking energy minimization on Discovery Studio 3.5. The final structures were analyzed using PyMOL visualization programs and the receptor-inhibitor complexes were used to develop the pharmacophore model for further evaluation [67]. Docking simulations were also performed in order to identify the residues present in the active site of CA IX that interact with the inhibitors. The docking study revealed that residues: L91, L93, L198, V121, L135, L141, V143, P201, P202, W5, W209, F245, H96, H119, E106, T199, T200, H94, D132, Q92, and V131 formed either hydrophobic or aromatic interactions with the inhibitor. N62, H64, S65, Q67, T69, and Q92 were identified as the hydrophilic residues in the active site as well [67]. The results of the docking studies established 10 novel compounds as CA IX inhibitors. Further analysis of the docking scores narrowed the list even further to the top five scoring compounds which were: ZINC03363328, ZINC08828920, ZINC12941947, ZINC03622539, and ZINC16650541 [67]. The information obtained from this study has demonstrated the value of molecular docking in identifying new

Figure 6. The critical residues in the active site located on the CA IX (PDB: 3IAI): L91, L93, L198, V121, L135, L141, V143, P201, P202, W5, W209, F245, H96, H119, E106, T199, T200, H94, D132, Q92, N62, H64, S65, Q67, T69, and Q92 are

CA IX inhibitors that provide a promising future as an anticancer therapy.

The epidermal growth factor receptor (EGFR) is another enticing biological target in the development of anticancer therapeutics. The EGFR is a family of tyrosine kinases that regulate many developmental, metabolic, and physiological processes. Binding of the epidermal growth factor to the family of kinases leads to homodimerization or heterodimerization of the EGFR. Mutations of EGFR gene, over expressed copies of the gene and EGFR protein overexpression lead to dysregulated TK activity which is observed in many tumors [68]. Overexpression of EGFR is frequently observed in breast, lung, ovarian, and prostate cancer

3.3. Docking for identifying inhibitors of EGFR

displayed.

158 Molecular Docking

Mahajan et al. discerned the value of the EGFR as a target for anticancer therapy; using molecular docking they were able to discover potential EGFR inhibitors. Screening of 50,000 compounds was performed by LigPrep (version3.3; Schrodinger, LLC, 2015) in order to prepare a library of drugs to be tested by several in silico methods. After the library was prepared with LigPrep, the compounds were then screened against EGFR drug target using e-Pharmacophore, docking, pharmacophore, substructure, and similarity search [72]. The protein used in the docking studies is complexed with the inhibitor tak-285 and it was chosen for the study because it has the best X-ray resolution (1.50 Å) of the EGFR structure (PDB: 3POZ). The downloaded protein was prepared for docking using the Protein Preparation Wizard. Docking the compounds was performed by the Glide module (version3.6; Schrodinger, LLC, 2015) software and the first round of docking studies used the high throughput virtual screening setting. After all compounds had been screened, the top 30% of the best scoring compounds were then redocked using standard precision (SP) docking. Once those compounds had been docked, the top 30% of the best scoring compounds in SP docking were then re-docked using extra precision (XP) docking. A total of 1534 had been selected as compounds that bound to the EGFR with a respectable docking score [72]. Docking, along with e-Pharmacophore and pharmacophore in silico methods were able to narrow 50,000 compounds down to 200 compounds that showed potential for EGFR inhibition. Further computational methodology of the compounds revealed

conducted using the Glide program. The docking results showed that Chlorpromazine binds to a hydrophobic pocket formed by residues from COX4 and transmembrane helices of COX1. L129, K122, M119, and Y126 were identified as being important residues that displayed interactions with the Chlorpromazine [73]. The results also indicated that the Chlorpromazine overlaps with residues of COX11, preventing the subunit from interacting with the rest of the CcO complex [73]. The study provides critical evidence on the repurposing for Chlorpromazine as a treatment for chemoresistant gliomas and persuades future research on Chlorpromazine as

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 161

In silico methods have been played an essential role in the battle against many of world's most devastating diseases. Cancer is debilitating, painful, and in some cases lethal; there is a massive urgency for researchers to find a cure so patients no longer have to suffer. Molecular docking has been on the forefront for the development, design, and discovery for new anticancer therapeutics. Among other things, one of the most important features of molecular docking is that it provides researchers with the opportunity to examine specific interactions between the ligand and the molecular target that are not well understood by in vivo and in vitro methods. Detailed knowledge of the binding interactions and mechanisms of the ligand to the target is critical for the production of new drugs or the improvement of the already existing drugs. Molecular docking is a dependable, economic, and an expeditious process that is of

Influenza, commonly referred to as the flu, is a viral infection that can be mild or severe, depending on the strain, and the host it infects. Due to the rapidly mutating nature of the influenza virus, new vaccines must be made and administered annually. Each year, researchers must determine which strains of the influenza virus are most likely to become prevalent in the coming flu season; annual flu vaccines are manufactured based on those recommendations [76]. Unfortunately, there is always the threat that the virus may mutate after that decision has been made, rendering vaccines ineffective. In that case, flu outbreaks and even pandemics may occur. In a pandemic, vaccination will no longer be a feasible option, and antiviral agents will

There are two types of antiviral drugs that have been used to treat influenza. The first marketed influenza antivirals were Adamantanes, specifically Amantadine and Rimantadine (Figure 8A, B). Adamantanes function by blocking the M2 proton channel [78]. This class of drugs was effective against influenza type A, but drug resistance developed rapidly [79, 80]. Hayden et al. conducted a study in which 17 Rimantadine-resistant influenza strains were recovered from 13 patients [81]. The M2 coding sequences of 17 resistant strains were then compared to 8 drug sensitive strains, and it was determined that all resistant strains had a nonsynonymous substitution in RNA segment 7. The most common mutation was S31N,

paramount importance in the advancement of anticancer therapeutics.

4. Identification of medicine for other prevalent diseases

an anticancer therapy.

4.1. Influenza

become a critical resource [77].

Figure 7. The tak-285 inhibitor complexed with the EGFR tyrosine kinase domain. The critical residue Met793 is shown in the active site of the EGFR tyrosine kinase.

that 87 out of the 200 compounds form an H-bond with M793, a critical residue in the inhibition of EGFR which can be visualized in Figure 7. Docking also revealed the structural similarity between the compounds and how the compounds orient themselves in the active site [72]. The 87 compounds were then categorized into 12 structural moieties which provided critical structural modification suggestions that would be beneficial in the development of more potent EGFR inhibitors [72].

#### 3.4. Repurposing approved drugs to anticancer applications

Molecular docking for drug repurposing is another effective and beneficial method that many researchers utilize in order to discover new indications for already existing drugs. The technique is especially favorable when assessing different pharmaceuticals as potential anticancer therapies. Avastin, which was originally developed for metastatic colon cancer and non-small cell lung cancer, has now been approved for metastatic breast cancer. Rituxan, which was intended for non-Hodgkin's Lymphoma has been repurposed for chronic lymphocytic leukemia and rheumatoid arthritis [51]. Molecular docking to make predictions of the physical interactions between the ligand and the target has been a successful practice in drug repurposing.

Avastin and Rituxan are not the only two drugs that have been repurposed for anticancer therapeutics. Oliva et al. used molecular docking to aid in the study of repurposing the FDA approved psychotropic drug Chlorpromazine. Evidence had shown that Chlorpromazine had antiproliferative activity against colon and brain tumors [73]. The drug accomplished this by inhibiting cytochrome c oxidase (CcO), which is the terminal electron acceptor enzyme of the mitochondrial respiratory chain and is composed of 13 subunits [74, 75] . Cytochrome c oxidase subunit 4 isoform 1 (COX4-1) was the focus of the study because in patients with glioblastoma, increased expression of COX4-1 has been associated with Temozolomide chemoresistance [73]. In vitro studies indicated that Chlorpromazine inhibited CcO when COX4-1 is expressed, however the binding mechanism was not well understood. Using Schrödinger Suite 2015 (Schrödinger, LLC, New York, NY, 2015), two human CcO homology models were constructed based on the mouse CcO crystal structure (PDB: 2Y69) using the Prime program. The Chlorpromazine ligand was prepared using the LigPrep program and the docking studies were conducted using the Glide program. The docking results showed that Chlorpromazine binds to a hydrophobic pocket formed by residues from COX4 and transmembrane helices of COX1. L129, K122, M119, and Y126 were identified as being important residues that displayed interactions with the Chlorpromazine [73]. The results also indicated that the Chlorpromazine overlaps with residues of COX11, preventing the subunit from interacting with the rest of the CcO complex [73]. The study provides critical evidence on the repurposing for Chlorpromazine as a treatment for chemoresistant gliomas and persuades future research on Chlorpromazine as an anticancer therapy.

In silico methods have been played an essential role in the battle against many of world's most devastating diseases. Cancer is debilitating, painful, and in some cases lethal; there is a massive urgency for researchers to find a cure so patients no longer have to suffer. Molecular docking has been on the forefront for the development, design, and discovery for new anticancer therapeutics. Among other things, one of the most important features of molecular docking is that it provides researchers with the opportunity to examine specific interactions between the ligand and the molecular target that are not well understood by in vivo and in vitro methods. Detailed knowledge of the binding interactions and mechanisms of the ligand to the target is critical for the production of new drugs or the improvement of the already existing drugs. Molecular docking is a dependable, economic, and an expeditious process that is of paramount importance in the advancement of anticancer therapeutics.

## 4. Identification of medicine for other prevalent diseases

#### 4.1. Influenza

that 87 out of the 200 compounds form an H-bond with M793, a critical residue in the inhibition of EGFR which can be visualized in Figure 7. Docking also revealed the structural similarity between the compounds and how the compounds orient themselves in the active site [72]. The 87 compounds were then categorized into 12 structural moieties which provided critical structural modification suggestions that would be beneficial in the development of more potent

Figure 7. The tak-285 inhibitor complexed with the EGFR tyrosine kinase domain. The critical residue Met793 is shown in

Molecular docking for drug repurposing is another effective and beneficial method that many researchers utilize in order to discover new indications for already existing drugs. The technique is especially favorable when assessing different pharmaceuticals as potential anticancer therapies. Avastin, which was originally developed for metastatic colon cancer and non-small cell lung cancer, has now been approved for metastatic breast cancer. Rituxan, which was intended for non-Hodgkin's Lymphoma has been repurposed for chronic lymphocytic leukemia and rheumatoid arthritis [51]. Molecular docking to make predictions of the physical interactions between the ligand and the target has been a successful practice in

Avastin and Rituxan are not the only two drugs that have been repurposed for anticancer therapeutics. Oliva et al. used molecular docking to aid in the study of repurposing the FDA approved psychotropic drug Chlorpromazine. Evidence had shown that Chlorpromazine had antiproliferative activity against colon and brain tumors [73]. The drug accomplished this by inhibiting cytochrome c oxidase (CcO), which is the terminal electron acceptor enzyme of the mitochondrial respiratory chain and is composed of 13 subunits [74, 75] . Cytochrome c oxidase subunit 4 isoform 1 (COX4-1) was the focus of the study because in patients with glioblastoma, increased expression of COX4-1 has been associated with Temozolomide chemoresistance [73]. In vitro studies indicated that Chlorpromazine inhibited CcO when COX4-1 is expressed, however the binding mechanism was not well understood. Using Schrödinger Suite 2015 (Schrödinger, LLC, New York, NY, 2015), two human CcO homology models were constructed based on the mouse CcO crystal structure (PDB: 2Y69) using the Prime program. The Chlorpromazine ligand was prepared using the LigPrep program and the docking studies were

3.4. Repurposing approved drugs to anticancer applications

EGFR inhibitors [72].

160 Molecular Docking

the active site of the EGFR tyrosine kinase.

drug repurposing.

Influenza, commonly referred to as the flu, is a viral infection that can be mild or severe, depending on the strain, and the host it infects. Due to the rapidly mutating nature of the influenza virus, new vaccines must be made and administered annually. Each year, researchers must determine which strains of the influenza virus are most likely to become prevalent in the coming flu season; annual flu vaccines are manufactured based on those recommendations [76]. Unfortunately, there is always the threat that the virus may mutate after that decision has been made, rendering vaccines ineffective. In that case, flu outbreaks and even pandemics may occur. In a pandemic, vaccination will no longer be a feasible option, and antiviral agents will become a critical resource [77].

There are two types of antiviral drugs that have been used to treat influenza. The first marketed influenza antivirals were Adamantanes, specifically Amantadine and Rimantadine (Figure 8A, B). Adamantanes function by blocking the M2 proton channel [78]. This class of drugs was effective against influenza type A, but drug resistance developed rapidly [79, 80]. Hayden et al. conducted a study in which 17 Rimantadine-resistant influenza strains were recovered from 13 patients [81]. The M2 coding sequences of 17 resistant strains were then compared to 8 drug sensitive strains, and it was determined that all resistant strains had a nonsynonymous substitution in RNA segment 7. The most common mutation was S31N,

Figure 8. Two dimensional structures of the Adamantanes, (A) amantadine [SMILES: NC13CC2CC(CC(C1)C2)C3] and (B) Rimantadine [SMILES: NC(C)C13CC2CC(CC(C1)C2)C3].

which was found in 14 separate isolates. The other mutations found were A30V, A30T, and V27A. By 2009, all strains of influenza A had become resistant to Adamantanes [82].

The second class of influenza drugs is neuraminidase inhibitors. Neuraminidase, also referred to as sialidase, is an enzyme involved in the release of viral progeny. At the end of the viral replication cycle, neuraminidase cleaves O-sialic acid, also called NeuAc5 (N-acetyl-alphaneuraminate), during the budding process that releases viral progeny that then infect other cells. Because inhibition of this enzyme greatly reduces the spread of the virus throughout the body, it is an attractive drug target [83]. There are currently two neuraminidase inhibitors on the market: Zanamivir (Relenza) and Oseltamivir (Tamiflu). Zanamivir (4-guanidino-Neu5- Ac2en) was created using computer-assisted rational design based on the X-ray diffraction structure of influenza neuraminidase, which was first solved by Varghese et al. (now PDB: 7NN9) [84]. In further studies, Colman et al. characterized the active site of this protein, identifying a large pocket containing "an unusually large number of charged residues," including R119 and E1201 [86]. Von Itzstein et al. used GRID software to analyze the active site of influenza neuraminidase and its interactions with various novel inhibitors [87]. The inhibitor with the most energetically favorable interactions was 4-guanidino-Neu5Ac2en, now known as Zanamivir. It was noted that one of the terminal amino groups of Zanamivir's guanidyl group interacted with the glutamic acid 119 carboxyl group (Figure 9A, B). Von Itzstein et al. went on to conduct Zanamivir trials on influenza infected ferrets and mice, which validated the results of their computational studies [87]. Hayden et al. conducted randomized double blind trials that concluded Zanamivir was both effective and safe for use to treat influenza A and B [88]. The drug became FDA approved in 1999 and has since been used in conjunction with annual vaccines to prevent and minimize influenza outbreaks [89].

falciparum. This disease can cause flu-like symptoms, and can be fatal if left untreated [90]. Malaria is typically treated with quinine drugs such as Chloroquine, Hydroxychloroquine, or Amodiaquine (Figure 10A, B), which function by interfering with heme polymerization [91]. Interference with this function leads to increased levels of hemoglobin and ferriprotoporphyrin IX (FPIX), which can be toxic to the parasite. P. falciparum has developed resistance to chloroquine (and similar drugs); in resistant cells, quinine drugs are actively transported out of the parasitic vacuole [92]. This form of resistance has become widespread, resulting in a

Figure 9. (A) (PDB ID: 5 L17) this structure shows Zanamivir bound to influenza a neuraminidase. Zanamivir, shown with green carbons, interacts with R119, E120, L135, D152, R153, W180, I224, R226, E229, E278, E279, R294, R372, and Y406 (cyan carbons). (B) Ligand interaction diagram showing a closer look at how these residues interact with the ligand. Note the interaction between amino groups and acidic residues (primarily glutamic acid) and the interactions between

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 163

In P. falciparum, M18 aspartyl aminopeptidase (PfM18AAP) and its interactions with membrane proteins are essential for parasite survival, making it an attractive antimalarial drug target. Using molecular docking and other computational methods, Kumari et al. determined structural requirements for PfM18AAP inhibitors using GOLD v5.2 and the Schrödinger Maestro 9.1 GLIDE program [12]. This study selected and screened just under 30,000 compounds

Figure 10. Two-dimensional structures of the common quinine drugs, (A) Amodiaquine [SMILES: Clc1cc2nccc(c2cc1)

Nc3cc(c(O)cc3)CN(CC)CC] and (B) Chloroquine [SMILES: Clc1cc2nccc(c2cc1)NC(C)CCCN(CC)CC].

need for new antimalarial drugs.

hydroxyl groups and basic residues (primarily arginine).

#### 4.2. Malaria

Malaria is an infectious disease caused by a parasitic protist and spread by mosquitoes. There are several different species of this parasite; the most deadly, and most prevalent is Plasmodium

<sup>1</sup> Colman et al. (1983) refers to Arg 119 and Glu 120 as Arg 118 and Glu 119. This text uses the more up to date numbering used in [85].

Figure 9. (A) (PDB ID: 5 L17) this structure shows Zanamivir bound to influenza a neuraminidase. Zanamivir, shown with green carbons, interacts with R119, E120, L135, D152, R153, W180, I224, R226, E229, E278, E279, R294, R372, and Y406 (cyan carbons). (B) Ligand interaction diagram showing a closer look at how these residues interact with the ligand. Note the interaction between amino groups and acidic residues (primarily glutamic acid) and the interactions between hydroxyl groups and basic residues (primarily arginine).

which was found in 14 separate isolates. The other mutations found were A30V, A30T, and

Figure 8. Two dimensional structures of the Adamantanes, (A) amantadine [SMILES: NC13CC2CC(CC(C1)C2)C3] and

The second class of influenza drugs is neuraminidase inhibitors. Neuraminidase, also referred to as sialidase, is an enzyme involved in the release of viral progeny. At the end of the viral replication cycle, neuraminidase cleaves O-sialic acid, also called NeuAc5 (N-acetyl-alphaneuraminate), during the budding process that releases viral progeny that then infect other cells. Because inhibition of this enzyme greatly reduces the spread of the virus throughout the body, it is an attractive drug target [83]. There are currently two neuraminidase inhibitors on the market: Zanamivir (Relenza) and Oseltamivir (Tamiflu). Zanamivir (4-guanidino-Neu5- Ac2en) was created using computer-assisted rational design based on the X-ray diffraction structure of influenza neuraminidase, which was first solved by Varghese et al. (now PDB: 7NN9) [84]. In further studies, Colman et al. characterized the active site of this protein, identifying a large pocket containing "an unusually large number of charged residues," including R119 and E1201 [86]. Von Itzstein et al. used GRID software to analyze the active site of influenza neuraminidase and its interactions with various novel inhibitors [87]. The inhibitor with the most energetically favorable interactions was 4-guanidino-Neu5Ac2en, now known as Zanamivir. It was noted that one of the terminal amino groups of Zanamivir's guanidyl group interacted with the glutamic acid 119 carboxyl group (Figure 9A, B). Von Itzstein et al. went on to conduct Zanamivir trials on influenza infected ferrets and mice, which validated the results of their computational studies [87]. Hayden et al. conducted randomized double blind trials that concluded Zanamivir was both effective and safe for use to treat influenza A and B [88]. The drug became FDA approved in 1999 and has since been used in

V27A. By 2009, all strains of influenza A had become resistant to Adamantanes [82].

(B) Rimantadine [SMILES: NC(C)C13CC2CC(CC(C1)C2)C3].

conjunction with annual vaccines to prevent and minimize influenza outbreaks [89].

Malaria is an infectious disease caused by a parasitic protist and spread by mosquitoes. There are several different species of this parasite; the most deadly, and most prevalent is Plasmodium

Colman et al. (1983) refers to Arg 119 and Glu 120 as Arg 118 and Glu 119. This text uses the more up to date numbering

4.2. Malaria

162 Molecular Docking

used in [85].

1

falciparum. This disease can cause flu-like symptoms, and can be fatal if left untreated [90]. Malaria is typically treated with quinine drugs such as Chloroquine, Hydroxychloroquine, or Amodiaquine (Figure 10A, B), which function by interfering with heme polymerization [91]. Interference with this function leads to increased levels of hemoglobin and ferriprotoporphyrin IX (FPIX), which can be toxic to the parasite. P. falciparum has developed resistance to chloroquine (and similar drugs); in resistant cells, quinine drugs are actively transported out of the parasitic vacuole [92]. This form of resistance has become widespread, resulting in a need for new antimalarial drugs.

In P. falciparum, M18 aspartyl aminopeptidase (PfM18AAP) and its interactions with membrane proteins are essential for parasite survival, making it an attractive antimalarial drug target. Using molecular docking and other computational methods, Kumari et al. determined structural requirements for PfM18AAP inhibitors using GOLD v5.2 and the Schrödinger Maestro 9.1 GLIDE program [12]. This study selected and screened just under 30,000 compounds

Figure 10. Two-dimensional structures of the common quinine drugs, (A) Amodiaquine [SMILES: Clc1cc2nccc(c2cc1) Nc3cc(c(O)cc3)CN(CC)CC] and (B) Chloroquine [SMILES: Clc1cc2nccc(c2cc1)NC(C)CCCN(CC)CC].

for binding activity. Based on the results, it was concluded that the best inhibitors had one hydrogen donor, one hydrophobic group, and two aromatic rings. Molecular docking and pharmacophore modeling have been used to search for novel inhibitors using those criteria.

reported [97–99]. In 2015, the first ZIKV epidemic began in Brazil. As outbreaks become more

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 165

Non-structural protein 5 methyl transferase (NS5 MTase) is crucial for the maintained stability of a flaviviral genome, and the ability to evade immune response [100] which makes it an attractive target for antiviral activity. Zhang et al. used docking simulations (AutoDock 4.2) to determine potential designs for novel NS5 MTase inhibitors and binding sites [101]; the authors of this study found that dengue virus inhibitor compound 10 found by Lim et al. [102] (PDB: 3P8Z) may bind to ZIKV NS5MTase. Ramharack and Soliman utilized several different computational tools in their study. Preliminary methods included homology modeling, binding site prediction, and pharmacophore modeling [103]. To narrow down the results from these studies, they used molecular docking [AutoDock Vina]. Out of 31 compounds subjected to docking studies, 3 were chosen for the next step, molecular dynamic simulation. It was concluded that two of their compounds showed "substantial stability in complex with

Hepatitis C is another virus that is closely related to Zika. Hepatitis C is commonly treated with polymerase inhibitors (Ribavirin and Sofosbuvir). Sacramento et al. used docking simulations (MODELER 9.16) to model binding between Hepatitis C polymerase inhibitors and Zika RNA polymerase (PDB: 4WTG) [104]. These simulations, as well as their in vitro trials suggested that these drugs intended for treatment of Hepatitis C may be effective against Zika as well. A study by Elfiky supported these results through further docking simulations

Tuberculosis (TB) and infectious disease caused by Mycobacterium tuberculosis. Human infection with TB dates back all the way to ancient Egypt, India, and China [106]. TB is spread through the air, usually by a cough or sneeze from an infected person. TB kills nearly 2 million people each year, mostly in Africa [107]. The most effective treatments for non-resistant TB are Isoniazid and Rifampin. Unfortunately, TB drug resistance has become extensive [107]. There are three categories of resistant TB strains: multidrug resistant (MDR), extensively drugresistant (XDR), and totally drug-resistant (TDR). In order to be classified as MDR TB, the strain must be resistant to both Isoniazid and Rifampin [108]. A TB strain is classified as XDR if it is resistant to Isoniazid, Rifampin, and "is also resistant to three or more of the six classes of second line TB drugs," [108]. TDR strains are resistant to all known TB drugs [109]. Dramatic increases of drug resistance have prompted researchers to seek new drug targets; in order to reduce research costs and get results as quickly as possible, many are turning to docking

Shikimate kinase is a protein involved in an amino acid biosynthesis pathway in M. tuberculosis [110]. Interruption of this pathway prevents synthesis of essential amino acids, leading to incomplete proteins, which leads to cell death. Vianna and de Azevedo used docking simulations (MOLDOCK) to identify novel SK inhibitors; these compounds were compared to staurosporine, which has demonstrated SK inhibition in vitro [111]. The novel inhibitors were

and more severe, it is becoming increasingly urgent to find a drug to treat ZIKV.

the target enzyme (ZIKV NS5)," [103].

(SCIGRESS software with PDB: 2J7U) [105].

simulations for preliminary trials.

4.4. Tuberculosis

The lactate dehydrogenase enzyme of P. falciparum (PfLDH) is a target of quinine drugs, and is another potential target for novel antimalarial drugs. This enzyme is important for glycolysis, and its inhibition can potentially result in death of the parasite [93]. Compounds similar to nicotinamide adenine dinucleotide (NADH) are believed to be excellent candidates for PfLDH inhibition [94]. Penna-Coutinho et al. used molecular docking (with software MolDock) to select potential drug candidates [95]. NADH and 50 potential drug candidates were docked to PfLDH in complex with Oxamate (PDB: 1LDG), the substrate that NADH binds to (Figure 11); the compounds that had the most similar docking score to NADH were selected for in vitro tests. The in vitro tests confirmed the activity of the highest scoring compounds, Itraconazole, Atorvastatin, and Posaconazole. In further tests, these same compounds inhibited parasite growth in mice infected with Plasmodium berghei, another species of the malaria parasite. These compounds require further testing, but could potentially progress to clinical trials and eventually be marketed as antimalarial drugs.

#### 4.3. Zika

The Zika virus (ZIKV), named for the Ugandan forest in which it was originally found, was first isolated in monkeys [96]. ZIKV belongs to a genus of viruses known as flaviviruses; other viruses belonging to this genus are dengue fever, yellow fever, hepatitis, and West Nile. ZIKV can be transmitted by mosquitoes or sexual contact. Symptoms of the virus include fever, joint pain, and rash for up to 7 days. ZIKV has also been associated with Guillain-Barre syndrome [97], an autoimmune disease. The virus can also be transmitted from mother to fetus, which can result in severe birth defects. From 2007 to 2014, several small outbreaks of the virus were

Figure 11. Structure of plasmodium falciparum lactate dehydrogenase in complex with Oxamate and NADH. NADH [SMILES: O=C(N)c1ccc[n+](c1)[C@@H]2O[C@@H]([C@@H](O)[C@H]2O)COP([O-])(=O)OP(=O)(O)OC[C@H]5O[C@@H] (n4cnc3c(ncnc34)N)[C@H](O)[C@@H]5O]; Oxamate [SMILES: C(=O)(C(=O)O)N].

reported [97–99]. In 2015, the first ZIKV epidemic began in Brazil. As outbreaks become more and more severe, it is becoming increasingly urgent to find a drug to treat ZIKV.

Non-structural protein 5 methyl transferase (NS5 MTase) is crucial for the maintained stability of a flaviviral genome, and the ability to evade immune response [100] which makes it an attractive target for antiviral activity. Zhang et al. used docking simulations (AutoDock 4.2) to determine potential designs for novel NS5 MTase inhibitors and binding sites [101]; the authors of this study found that dengue virus inhibitor compound 10 found by Lim et al. [102] (PDB: 3P8Z) may bind to ZIKV NS5MTase. Ramharack and Soliman utilized several different computational tools in their study. Preliminary methods included homology modeling, binding site prediction, and pharmacophore modeling [103]. To narrow down the results from these studies, they used molecular docking [AutoDock Vina]. Out of 31 compounds subjected to docking studies, 3 were chosen for the next step, molecular dynamic simulation. It was concluded that two of their compounds showed "substantial stability in complex with the target enzyme (ZIKV NS5)," [103].

Hepatitis C is another virus that is closely related to Zika. Hepatitis C is commonly treated with polymerase inhibitors (Ribavirin and Sofosbuvir). Sacramento et al. used docking simulations (MODELER 9.16) to model binding between Hepatitis C polymerase inhibitors and Zika RNA polymerase (PDB: 4WTG) [104]. These simulations, as well as their in vitro trials suggested that these drugs intended for treatment of Hepatitis C may be effective against Zika as well. A study by Elfiky supported these results through further docking simulations (SCIGRESS software with PDB: 2J7U) [105].

#### 4.4. Tuberculosis

for binding activity. Based on the results, it was concluded that the best inhibitors had one hydrogen donor, one hydrophobic group, and two aromatic rings. Molecular docking and pharmacophore modeling have been used to search for novel inhibitors using those criteria.

The lactate dehydrogenase enzyme of P. falciparum (PfLDH) is a target of quinine drugs, and is another potential target for novel antimalarial drugs. This enzyme is important for glycolysis, and its inhibition can potentially result in death of the parasite [93]. Compounds similar to nicotinamide adenine dinucleotide (NADH) are believed to be excellent candidates for PfLDH inhibition [94]. Penna-Coutinho et al. used molecular docking (with software MolDock) to select potential drug candidates [95]. NADH and 50 potential drug candidates were docked to PfLDH in complex with Oxamate (PDB: 1LDG), the substrate that NADH binds to (Figure 11); the compounds that had the most similar docking score to NADH were selected for in vitro tests. The in vitro tests confirmed the activity of the highest scoring compounds, Itraconazole, Atorvastatin, and Posaconazole. In further tests, these same compounds inhibited parasite growth in mice infected with Plasmodium berghei, another species of the malaria parasite. These compounds require further testing, but could potentially progress to clinical trials and eventually be

The Zika virus (ZIKV), named for the Ugandan forest in which it was originally found, was first isolated in monkeys [96]. ZIKV belongs to a genus of viruses known as flaviviruses; other viruses belonging to this genus are dengue fever, yellow fever, hepatitis, and West Nile. ZIKV can be transmitted by mosquitoes or sexual contact. Symptoms of the virus include fever, joint pain, and rash for up to 7 days. ZIKV has also been associated with Guillain-Barre syndrome [97], an autoimmune disease. The virus can also be transmitted from mother to fetus, which can result in severe birth defects. From 2007 to 2014, several small outbreaks of the virus were

Figure 11. Structure of plasmodium falciparum lactate dehydrogenase in complex with Oxamate and NADH. NADH [SMILES: O=C(N)c1ccc[n+](c1)[C@@H]2O[C@@H]([C@@H](O)[C@H]2O)COP([O-])(=O)OP(=O)(O)OC[C@H]5O[C@@H]

(n4cnc3c(ncnc34)N)[C@H](O)[C@@H]5O]; Oxamate [SMILES: C(=O)(C(=O)O)N].

marketed as antimalarial drugs.

4.3. Zika

164 Molecular Docking

Tuberculosis (TB) and infectious disease caused by Mycobacterium tuberculosis. Human infection with TB dates back all the way to ancient Egypt, India, and China [106]. TB is spread through the air, usually by a cough or sneeze from an infected person. TB kills nearly 2 million people each year, mostly in Africa [107]. The most effective treatments for non-resistant TB are Isoniazid and Rifampin. Unfortunately, TB drug resistance has become extensive [107]. There are three categories of resistant TB strains: multidrug resistant (MDR), extensively drugresistant (XDR), and totally drug-resistant (TDR). In order to be classified as MDR TB, the strain must be resistant to both Isoniazid and Rifampin [108]. A TB strain is classified as XDR if it is resistant to Isoniazid, Rifampin, and "is also resistant to three or more of the six classes of second line TB drugs," [108]. TDR strains are resistant to all known TB drugs [109]. Dramatic increases of drug resistance have prompted researchers to seek new drug targets; in order to reduce research costs and get results as quickly as possible, many are turning to docking simulations for preliminary trials.

Shikimate kinase is a protein involved in an amino acid biosynthesis pathway in M. tuberculosis [110]. Interruption of this pathway prevents synthesis of essential amino acids, leading to incomplete proteins, which leads to cell death. Vianna and de Azevedo used docking simulations (MOLDOCK) to identify novel SK inhibitors; these compounds were compared to staurosporine, which has demonstrated SK inhibition in vitro [111]. The novel inhibitors were docked to a number of structures for MtSK (PDB: 2DFN, 1U8A, 1WE2, 1ZYU, 2G1K, 2IYQ, 2IYR, 2IYS, 2IYX, 2IYY, 2IYZ, and 3BAF).

need several months or even years to screen them all manually or automatically in the lab, if it is possible we can obtain them all. More and more researchers are turning to computational methods to design drugs in an efficient manner that has the possibility to save money for pharmaceutical companies. While these in silico methods are not yet ready to replace in vivo and in vitro methods and have only brought a few medications to the market, such Raltegravir and Dorzolamide [117], they still provide a valuable insight into the molecular interactions between the ligand and protein. As seen above, there are situations in which computational methods are not always able to accurately determine the results. For this reason, many researchers use in silico methods in tandem with other research methods to verify or elucidate standing results. Each time these computational methods verify already established experimental results, their validity in the drug design market has the opportunity to go up. It seems that many researchers are starting to rely on computational results from molecular docking and other computational methods in their research. Often these methods cannot solely generate results that will create a novel drug. However, computational methods are slowly solidifying their place in the pharmaceutical industry as a necessary step toward designing new drugs.

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 167

1. Most of these projects were designed to recognize a new inhibitor(s) to an enzyme which plays an essential role in a key metabolic/proliferation pathway or the infectious procedure

2. One or more determined PDB structures of the target protein with good resolution were used, and, often, the key residues of the catalytic reaction, binding/inhibition mechanisms,

3. Other computational methods or tools were also used in sequence or in parallel, such as structure prediction, binding site prediction, pharmacophore model, QSAR model, and

Enzymes and membrane proteins (receptors) are two major drug targets. According to previous studies, there is severe bias on the number of determined structures deposit on PDB [118, 119]. A large proportion of solved structures belong to soluble proteins, especially enzymes. It not only made structures of enzymes easier to obtain for molecular docking, but it also made scoring functions/force fields of molecular docking and other related computational approaches to be more accurate for enzymes than membrane proteins. However, we have noticed the importance of membrane receptors, glycol-proteins and non-structure proteins. How to create a reliable strategy to determine or predict the structures of these important drug targets remains a big

Drug resistance is also a major issue in the failures of treatment of both cancers and infectious diseases. Due to the advancement of docking calculation, we will be able to predict the possible drug resistances and side effects before the treatment or even the drug approval in the future. Therefore, the back-up drugs should be developed and utilized even before drug

To summarize the cases we reported above (Please see Table 3):

4. Drug repurposing has received more and more attention.

and drug resistances were revealed based on the docking results.

of a pathogen.

MD simulation.

challenge in molecular docking.

Another response to drug resistance is drug repurposing. The advantage of drug repurposing is that potential drugs have already been shown not to have severe side effects, which speeds up the process and saves money. Studies of this nature often utilize molecular docking and other computational methods to save even more time and money by screening more potential drugs in a shorter time frame. Kahlous et al. selected 1991 FDA-approved (nonantibiotic) drugs and tested them for antibiotic activity against Staphylococcus aureus [PDB: 2XCS and 2XCT] by docking (OpenEye HYBRID) drug structures to known antibiotic targets [112]. These results were then compared to a variety of market antibiotics. The drug candidates were narrowed down to 34 potential candidates for further testing. Among the top candidates were Diclofenac (antiinflammatory), Drotaverine (antispasmodic), Flurbiprofen (antiinflammatory), Ibuprofen (antiinflammatory), and Niacin (vitamin B3). Brindha et al. conducted a similar study, specifically targeting tuberculosis [113]. This study screened 1554 FDA-approved drugs (Schrödinger GLIDE) for their ability to bind to protein kinase B of M. tuberculosis (PDB: 2FUM), a known antibiotic target. Fourteen of these drugs were determined suitable for further exploration as TB drugs. The top three candidates from this study were Flavin adenine dinucleotide (treats vitamin B2 deficiency), Valrubicin (treats bladder cancer), and Arcarbose (treat/manage type II diabetes).

## 5. Summary and discussion

While in silico methods must be reaffirmed by in vitro and in vivo testing, computational methods have been gaining popularity in the drug design industry by proving they are critical in the discovery of medications. Several drugs that are currently available to the public for the treatment of different diseases have been developed based on in silico approaches. For example, Zanamivir, used to treat influenza, was developed using computer-assisted design [84]. Through these studies, Zanamivir was identified as the inhibitor having the most energetically favorable interactions with influenza neuraminidase. The results from the docking study of Zanamivir were convincing enough to move forward with in vivo testing; the results of in vivo studies reaffirmed the results of the in silico tests [87]. Nelfinavir and Saquinavar are used in the treatment of HIV and were also developed by computational methods. Docking studies also revealed how the HIV protease developed resistance toward Nelfinavir and Saquinavar which was beneficial in improving the potency of the drugs [45]. Based on these successful examples, it is clear that computational methods are capable of developing new pharmaceuticals and provide evidence to other researchers that this is a reliable and effective technique in drug discovery.

The cost of bringing a drug to market and the amount of drug resistance profiles emerging are major factors that researchers need to address when designing a drug. It may cause more than 1 billion dollars and 10 years to bring a drug to the market [114]. As we have collected millions of pharmaceutical compounds in a database like Pubchem [115] and ChEMBL [116], we will need several months or even years to screen them all manually or automatically in the lab, if it is possible we can obtain them all. More and more researchers are turning to computational methods to design drugs in an efficient manner that has the possibility to save money for pharmaceutical companies. While these in silico methods are not yet ready to replace in vivo and in vitro methods and have only brought a few medications to the market, such Raltegravir and Dorzolamide [117], they still provide a valuable insight into the molecular interactions between the ligand and protein. As seen above, there are situations in which computational methods are not always able to accurately determine the results. For this reason, many researchers use in silico methods in tandem with other research methods to verify or elucidate standing results. Each time these computational methods verify already established experimental results, their validity in the drug design market has the opportunity to go up. It seems that many researchers are starting to rely on computational results from molecular docking and other computational methods in their research. Often these methods cannot solely generate results that will create a novel drug. However, computational methods are slowly solidifying their place in the pharmaceutical industry as a necessary step toward designing new drugs.

To summarize the cases we reported above (Please see Table 3):

docked to a number of structures for MtSK (PDB: 2DFN, 1U8A, 1WE2, 1ZYU, 2G1K, 2IYQ,

Another response to drug resistance is drug repurposing. The advantage of drug repurposing is that potential drugs have already been shown not to have severe side effects, which speeds up the process and saves money. Studies of this nature often utilize molecular docking and other computational methods to save even more time and money by screening more potential drugs in a shorter time frame. Kahlous et al. selected 1991 FDA-approved (nonantibiotic) drugs and tested them for antibiotic activity against Staphylococcus aureus [PDB: 2XCS and 2XCT] by docking (OpenEye HYBRID) drug structures to known antibiotic targets [112]. These results were then compared to a variety of market antibiotics. The drug candidates were narrowed down to 34 potential candidates for further testing. Among the top candidates were Diclofenac (antiinflammatory), Drotaverine (antispasmodic), Flurbiprofen (antiinflammatory), Ibuprofen (antiinflammatory), and Niacin (vitamin B3). Brindha et al. conducted a similar study, specifically targeting tuberculosis [113]. This study screened 1554 FDA-approved drugs (Schrödinger GLIDE) for their ability to bind to protein kinase B of M. tuberculosis (PDB: 2FUM), a known antibiotic target. Fourteen of these drugs were determined suitable for further exploration as TB drugs. The top three candidates from this study were Flavin adenine dinucleotide (treats vitamin B2 deficiency), Valrubicin (treats bladder cancer), and Arcarbose

While in silico methods must be reaffirmed by in vitro and in vivo testing, computational methods have been gaining popularity in the drug design industry by proving they are critical in the discovery of medications. Several drugs that are currently available to the public for the treatment of different diseases have been developed based on in silico approaches. For example, Zanamivir, used to treat influenza, was developed using computer-assisted design [84]. Through these studies, Zanamivir was identified as the inhibitor having the most energetically favorable interactions with influenza neuraminidase. The results from the docking study of Zanamivir were convincing enough to move forward with in vivo testing; the results of in vivo studies reaffirmed the results of the in silico tests [87]. Nelfinavir and Saquinavar are used in the treatment of HIV and were also developed by computational methods. Docking studies also revealed how the HIV protease developed resistance toward Nelfinavir and Saquinavar which was beneficial in improving the potency of the drugs [45]. Based on these successful examples, it is clear that computational methods are capable of developing new pharmaceuticals and provide evidence to other researchers that this is a reliable and effective technique in drug discovery. The cost of bringing a drug to market and the amount of drug resistance profiles emerging are major factors that researchers need to address when designing a drug. It may cause more than 1 billion dollars and 10 years to bring a drug to the market [114]. As we have collected millions of pharmaceutical compounds in a database like Pubchem [115] and ChEMBL [116], we will

2IYR, 2IYS, 2IYX, 2IYY, 2IYZ, and 3BAF).

166 Molecular Docking

(treat/manage type II diabetes).

5. Summary and discussion


Enzymes and membrane proteins (receptors) are two major drug targets. According to previous studies, there is severe bias on the number of determined structures deposit on PDB [118, 119]. A large proportion of solved structures belong to soluble proteins, especially enzymes. It not only made structures of enzymes easier to obtain for molecular docking, but it also made scoring functions/force fields of molecular docking and other related computational approaches to be more accurate for enzymes than membrane proteins. However, we have noticed the importance of membrane receptors, glycol-proteins and non-structure proteins. How to create a reliable strategy to determine or predict the structures of these important drug targets remains a big challenge in molecular docking.

Drug resistance is also a major issue in the failures of treatment of both cancers and infectious diseases. Due to the advancement of docking calculation, we will be able to predict the possible drug resistances and side effects before the treatment or even the drug approval in the future. Therefore, the back-up drugs should be developed and utilized even before drug


experiments in the biological labs, and we can do even more than conventional approaches. For example, we can predict the potential side effects or drug resistances. Other computational tools such as (3D structure or binding site) prediction models, molecular dynamic simulation, and kinetic modeling have been also well established and applied in different steps of drug discovery to provide more information of target protein or drug efficacy, narrow down the searching spaces/reduce computational load, and/or validate the results of docking. Moreover, drug repurposing is another important application of molecular docking that helps us to enhance

Has Molecular Docking Ever Brought us a Medicine? http://dx.doi.org/10.5772/intechopen.72898 169

In the Era of "Big Data", the accumulated number of protein structures and upgraded computation software and hardware generally improved all related computational methods, not just molecular docking. Based on the progress of the knowledge on protein folding, structural flexibility and molecular recognition, molecular docking has matured. As the core technology of virtual drug discovery, molecular docking will be widely applied to many stages of the drug

Mark Andrew Phillips, Marisa A. Stewart, Darby L. Woodling and Zhong-Ru Xie\*

Computational Drug Discovery Laboratory, School of Electrical and Computer Engineering,

[1] Pagadala NS, Syed K, Tuszynski J. Software for molecular docking: A review. Biophys-

[2] Wong CF. Flexible receptor docking for drug discovery. Expert Opinion on Drug Dis-

[3] Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-

[4] Yuriev E, Ramsland PA. Latest developments in molecular docking: 2010–2011 in

[5] Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The protein data bank. Nucleic Acids Research. 2000;28(1):235-242

[6] Berman HM, Henrick K, Nakamura H, Markley J, Bourne PE, Westbrook J. Realism

based drug design strategies. Molecules. 2015;20(7):13384-13421

review. Journal of Molecular Recognition. 2013;26(5):215-239

about PDB. Nature Biotechnology. 2007;25(8):845-846

the cost- and time-effectiveness of drug development.

\*Address all correspondence to: paulxie@uga.edu

ical Reviews. 2017;9(2):91-102

covery. 2015;10(11):1189-1200

College of Engineering, University of Georgia, Athens, GA, USA

discovery process.

Author details

References

Table 3. Summary of the cases presented.

resistance occurs. The improved reliability of molecular docking also facilitates the precision medicine.

As we see in Figure 1, the computational approaches play key roles in different steps of the drug discovery process: obtaining the protein structures, binding site prediction, virtual drug screening, binding verification, binding affinity estimation, prediction of drug resistances, binding kinetic modeling, and so on. Molecular docking assists in achieving many objectives in the steps mentioned above effectively and efficiently. Often, it is cheaper, faster than performing experiments in the biological labs, and we can do even more than conventional approaches. For example, we can predict the potential side effects or drug resistances. Other computational tools such as (3D structure or binding site) prediction models, molecular dynamic simulation, and kinetic modeling have been also well established and applied in different steps of drug discovery to provide more information of target protein or drug efficacy, narrow down the searching spaces/reduce computational load, and/or validate the results of docking. Moreover, drug repurposing is another important application of molecular docking that helps us to enhance the cost- and time-effectiveness of drug development.

In the Era of "Big Data", the accumulated number of protein structures and upgraded computation software and hardware generally improved all related computational methods, not just molecular docking. Based on the progress of the knowledge on protein folding, structural flexibility and molecular recognition, molecular docking has matured. As the core technology of virtual drug discovery, molecular docking will be widely applied to many stages of the drug discovery process.

## Author details

Mark Andrew Phillips, Marisa A. Stewart, Darby L. Woodling and Zhong-Ru Xie\*

\*Address all correspondence to: paulxie@uga.edu

Computational Drug Discovery Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, USA

## References

resistance occurs. The improved reliability of molecular docking also facilitates the precision

4WTG Modeler Ribavirin, Sofosbuvir To model and

MOLDOCK New candidates To identify new

Diclofenac et al. Drug repurposing

As we see in Figure 1, the computational approaches play key roles in different steps of the drug discovery process: obtaining the protein structures, binding site prediction, virtual drug screening, binding verification, binding affinity estimation, prediction of drug resistances, binding kinetic modeling, and so on. Molecular docking assists in achieving many objectives in the steps mentioned above effectively and efficiently. Often, it is cheaper, faster than performing

medicine.

Disease Target protein PDB

168 Molecular Docking

HIV Integrase 1QS4,

HIV Protease 1HXB,

anhydrases IX

Cancer EGFR 2ITN

aminopeptidase

dehydrogenase

Polymerase

Table 3. Summary of the cases presented.

Malaria M18 aspartyl

Malaria Lactate

Zika RNA

TB Shikimate kinase

TB Shikimate kinase

Cancer Carbonic

Cancer Proteasome 2F16 Glide,

ID

1BIS, 1BLE

1OHR

et al.

2DFN et al.

2XCS, 2XCT, 2FUM OpenEye HYBRID, Glide

4EME GOLD, Glide

Docking Software

GOLD

AutoDock, jMetalCpp

3IAI AutoDock ZINC03363328,

Cancer CcO 2Y69 Glide Chlorpromazine Drug repurposing Influenza Neuraminidase 7NN9 GRID Zanamivir To analyze the active

Drug(s) Purpose Other

binding mode

To identify new drug, to understand the binding mechanism

resistance

To discover inhibitors

site

drugs

drugs

drugs

CHEMBL588000 et al. To identify new

To study the effects of mutations

To select potential

compare ligand binding

inhibitors

GOLD S-1360 To predict the

PI-083, MG132, peptide

AMPPNP, Dacomitinib,

aldehydes

ZINC08828920, ZINC12941947, ZINC03622539, ZINC1665054

et al.

1LDG MolDock Itraconazole, Atorvastatin, Posaconazole

Zika NS5MTase 3P8Z AutoDock New candidates To identify new

AutoDock Saquinavir, Nelfinavir To predict the drug

computational method(s) used

MD simulation

MD simulation

Post-docking energy minimization

Optimization algorithms

Pharmacophore and QSAR models

MD simulation


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## *Edited by Dimitrios P. Vlachakis*

Molecular docking has always been and will be on the forefront of developments in the eminent field of drug design and medicinal chemistry. At the early days, drug discovery was based on blackboard drawings and expert intuition. However, as times move on, the amount of available information and overall knowledge base that needs to be analyzed cannot be processed manually. This, coupled by the rapid growth in computational infrastructure and processing power, has allowed for the efficient use of molecular docking tools and algorithms to be considered in the greater field of drug discovery. In the postgenomic era, molecular docking has become the key player for the screening of hundreds of thousands of compounds against a repertoire of pharmacological targets.

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Molecular Docking

Molecular Docking