**Introduction**

**Chapter 1**

**Provisional chapter**

**Introductory Chapter: Molecular Docking - Overview,**

**Introductory Chapter: Molecular Docking - Overview,** 

DOI: 10.5772/intechopen.78266

**Background, Application and What the Future Holds**

Molecular docking is on the frontline of computational biology and drug discovery. The explosion of structural and chemical information in recent years has rendered the use of efficient algorithms and large supercomputer facilities of uttermost importance in the drug discovery process. Medicinal chemists can now screen *in silico* hundreds of thousands of compounds on a repertoire of receptor molecules and putative pharmacological targets. It goes without saying that molecular docking comes in many shapes and sizes, thus allowing the researcher to balance out speed and exhaustiveness of calculation. Molecular docking can be performed online of freeware servers using just a web browser or it can be fully parameterized on a virtual machine on a cloud supercomputer for high resolution calculation. The main factor that changes here is

the grid resolution and the rigidity and flexibility of both the ligand and the receptor.

Let us start by setting the basis on molecular properties that are required to comprehend the molecular docking chapters that follow in this book. The geometry and the overall structure of a molecule are described by its bond distances, dihedral angles and bond angle [1]. This unique set of angles and distances create a set of coordinates that define the positioning of each atom in that molecular structure in three-dimensional (3D) space. The energy condition of this molecule can also be assessed and evaluated. The energy of a molecule includes all forms of energies, such as kinetic motion (described by vibration, rotation and translation) and forms of the potential energy of the molecule [2]. The potential energy of a molecule

**Background, Application and What the Future Holds**

© 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 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.

Dimitrios Vlachakis

**1. Introduction**

Additional information is available at the end of the chapter

Dimitrios VlachakisAdditional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.78266

**2. Molecular docking in a nutshell**

#### **Introductory Chapter: Molecular Docking - Overview, Background, Application and What the Future Holds Introductory Chapter: Molecular Docking - Overview, Background, Application and What the Future Holds**

DOI: 10.5772/intechopen.78266

Dimitrios Vlachakis

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

http://dx.doi.org/10.5772/intechopen.78266

## **1. Introduction**

Molecular docking is on the frontline of computational biology and drug discovery. The explosion of structural and chemical information in recent years has rendered the use of efficient algorithms and large supercomputer facilities of uttermost importance in the drug discovery process. Medicinal chemists can now screen *in silico* hundreds of thousands of compounds on a repertoire of receptor molecules and putative pharmacological targets. It goes without saying that molecular docking comes in many shapes and sizes, thus allowing the researcher to balance out speed and exhaustiveness of calculation. Molecular docking can be performed online of freeware servers using just a web browser or it can be fully parameterized on a virtual machine on a cloud supercomputer for high resolution calculation. The main factor that changes here is the grid resolution and the rigidity and flexibility of both the ligand and the receptor.

## **2. Molecular docking in a nutshell**

Let us start by setting the basis on molecular properties that are required to comprehend the molecular docking chapters that follow in this book. The geometry and the overall structure of a molecule are described by its bond distances, dihedral angles and bond angle [1]. This unique set of angles and distances create a set of coordinates that define the positioning of each atom in that molecular structure in three-dimensional (3D) space. The energy condition of this molecule can also be assessed and evaluated. The energy of a molecule includes all forms of energies, such as kinetic motion (described by vibration, rotation and translation) and forms of the potential energy of the molecule [2]. The potential energy of a molecule

© 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 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.

Molecular mechanics are based on the ball and spring representation of molecular systems. Here, the atoms are considered to be little balls, with varying properties according to the element, and the bonds are considered to be the springs that make the two interconnecting balls interact with each other. The ball and spring model is described by Hook's law, which

Introductory Chapter: Molecular Docking - Overview, Background, Application and What…

http://dx.doi.org/10.5772/intechopen.78266

The force constant is the constant k. The energy that is contained in the spring and the restoring force of the spring are proportional to the force constant. The force constant will determine the strength of the bond that the spring represents [9]. The vibrational frequency of the

The vibrational frequency (n) has been estimated to be proportional to the square root of the force constant (k) and inversely proportional to the reduced mass of the atoms that participate

All of the above can be combined and through potential energy functions of various structural features, such as bond lengths, bond angles and non-bonded interactions, can describe a forcefield (**Figure 2**) [11]. There are many different ways to set a forcefield depending on the needs of the system under investigation. Usually the factors affecting the energy of a molecular system (bonds, angles, dihedrals, non-bonded, etc.), are evaluated separately and they will contribute to the value of the total energy of the system [12]. The most popular forcefields are the MM2, which is suitable for small molecules, hydrocarbons and some simple heteroatom

**Figure 2.** Total energy is affected by bond distances, bond angles, dihedral angles and finally non-bonded interactions.

(1)

5

evaluates and quantifies the energy of the stretching of the spring [8].

spring is described as:

in a bond [10].

**Figure 1.** Energy changes during the course of a chemical reaction.

can be defined by the analysis of the electrostatic interaction between charges, the magnetic interactions between spinning charges and finally the potential energy of the bonds of the molecule. The total energy is indicative of the reactivity and stability of that a molecule or a system. **Figure 1** is depicts a reaction coordinate diagram that indicates the energy changes during the course of a chemical reaction [3].

Here the products are in the lowest or global minimum, the transition state is at energy maximum and the reactants are at an energy minimum. The dotted lines in the above diagram are indicative of the reactivity of the system (its kinetics) and the thermodynamic stability of the system. Through molecular modelling it is possible to quantify the above characteristics of the system and, for example, predict its reactivity. There are two fields in molecular modelling that attempt to do this: molecular mechanics and quantum mechanics [4].

The docking algorithm is basically split into two main parts: the searching algorithm and the scoring algorithm [5]. The searching algorithm will explore all conformations of the ligand within the space available [6]. Practically, it is impossible to perform all these calculations for every compound so most of the rotational and translational states of each compound will be explored within a given threshold of identical conformations. Each compound is not a rigid body but is a dynamic structure that exists in an ensemble of different conformations. The user can define how fine the docking algorithm will be by altering the various parameters of the task. Very fine calculations are much more accurate, but also much more time consuming. The most popular docking algorithm approaches can involve a coarse grained molecular dynamics simulation or a linear combination of many structures or a genetic algorithm that generates new conformations as it moves along.

The second feature of the docking algorithm is its scoring function [7]. The scoring function must be able to accurately evaluate each different conformation using certain forcefields and rules from physics and return a value that will describe the energy of the system at the given conformation. Low energies indicate better, more stable interactions.

Introductory Chapter: Molecular Docking - Overview, Background, Application and What… http://dx.doi.org/10.5772/intechopen.78266 5

Molecular mechanics are based on the ball and spring representation of molecular systems. Here, the atoms are considered to be little balls, with varying properties according to the element, and the bonds are considered to be the springs that make the two interconnecting balls interact with each other. The ball and spring model is described by Hook's law, which evaluates and quantifies the energy of the stretching of the spring [8].

The force constant is the constant k. The energy that is contained in the spring and the restoring force of the spring are proportional to the force constant. The force constant will determine the strength of the bond that the spring represents [9]. The vibrational frequency of the spring is described as:

can be defined by the analysis of the electrostatic interaction between charges, the magnetic interactions between spinning charges and finally the potential energy of the bonds of the molecule. The total energy is indicative of the reactivity and stability of that a molecule or a system. **Figure 1** is depicts a reaction coordinate diagram that indicates the energy changes

Here the products are in the lowest or global minimum, the transition state is at energy maximum and the reactants are at an energy minimum. The dotted lines in the above diagram are indicative of the reactivity of the system (its kinetics) and the thermodynamic stability of the system. Through molecular modelling it is possible to quantify the above characteristics of the system and, for example, predict its reactivity. There are two fields in molecular modelling

The docking algorithm is basically split into two main parts: the searching algorithm and the scoring algorithm [5]. The searching algorithm will explore all conformations of the ligand within the space available [6]. Practically, it is impossible to perform all these calculations for every compound so most of the rotational and translational states of each compound will be explored within a given threshold of identical conformations. Each compound is not a rigid body but is a dynamic structure that exists in an ensemble of different conformations. The user can define how fine the docking algorithm will be by altering the various parameters of the task. Very fine calculations are much more accurate, but also much more time consuming. The most popular docking algorithm approaches can involve a coarse grained molecular dynamics simulation or a linear combination of many structures or a genetic algorithm that

The second feature of the docking algorithm is its scoring function [7]. The scoring function must be able to accurately evaluate each different conformation using certain forcefields and rules from physics and return a value that will describe the energy of the system at the given

that attempt to do this: molecular mechanics and quantum mechanics [4].

conformation. Low energies indicate better, more stable interactions.

during the course of a chemical reaction [3].

4 Molecular Docking

**Figure 1.** Energy changes during the course of a chemical reaction.

generates new conformations as it moves along.

$$
\Delta \mathbf{n} = \frac{1}{2\pi} \gamma \sqrt{\frac{\mathbf{k}}{\mu}} \tag{1}
$$

The vibrational frequency (n) has been estimated to be proportional to the square root of the force constant (k) and inversely proportional to the reduced mass of the atoms that participate in a bond [10].

All of the above can be combined and through potential energy functions of various structural features, such as bond lengths, bond angles and non-bonded interactions, can describe a forcefield (**Figure 2**) [11]. There are many different ways to set a forcefield depending on the needs of the system under investigation. Usually the factors affecting the energy of a molecular system (bonds, angles, dihedrals, non-bonded, etc.), are evaluated separately and they will contribute to the value of the total energy of the system [12]. The most popular forcefields are the MM2, which is suitable for small molecules, hydrocarbons and some simple heteroatom

**Figure 2.** Total energy is affected by bond distances, bond angles, dihedral angles and finally non-bonded interactions.

functional groups, AMBER or CHARMM, which are parameterised to be used for peptides, nucleic acids and generic macromodels [13].

Overall through molecular mechanics the total energy of a molecule is described as a sum of all the contributions that may arise from loss of equilibrium in bond distances, also known as stretching contribution, bond angles, known as bending contribution, dihedral angles, the torsion contribution and finally non-bonded interaction contributions [14].

$$\mathbf{e}\_{\text{B}}^{\text{today}} \text{ - } \sum\_{i} \mathbf{e}\_{i}^{\text{today}} \text{ + } \sum\_{i} \mathbf{e}\_{i}^{\text{bend angles}} \text{ + } \sum\_{i} \mathbf{e}\_{i}^{\text{the-dual angles}} \text{ + } \sum\_{i} \mathbf{e}\_{i}^{\text{non-bend angles}} \text{ + } \sum\_{i} \sum\_{i} \mathbf{e}\_{ij}^{\text{non-bonded}} \tag{2}$$

The energy that is stored in chemical bonds of a molecule can describe the stretch, bend, and torsion energy whereas it is the steric attraction or repulsion that represents the non-bonded energy [15]. The latter is broken down to two different categories: the van der Waals (VDW) and electrostatic interactions [16].

A very steep energy barrier is generated at the van der Waals radius of each atom. Moreover a very shallow energy well is produced at larger separations (**Figure 3**). The inherent steric size of atoms and elements is dictated by their VDW radii. The same metric is used to describe weak attractive forces between atoms in close proximity [17]. A trivial example of the weak van der Waals attractive forces is the condensation of a gas into liquids. Furthermore it is the van der Waals radii of each element that is used for its visualisation purposes in space filling models of the molecule they participate. Steric repulsion takes place only in the case where two atoms come closer than the sum distance of their VDW radii [18].

$$\mathop{\mathbf{E}\_{\mathbf{ij}}}\_{\mathbf{ij}} \overset{\mathbf{v} \mathbf{D} \mathbf{w}}{\mathop{\mathbf{e}\_{\mathbf{ij}}}} = \mathop{\mathbf{e}\_{\mathbf{ij}}}\_{\mathbf{ij}} \left( \overset{\mathbf{r}\_{\mathbf{ij}} \mathbf{o}}{\overset{\mathbf{r}\_{\mathbf{ij}} \mathbf{o}}{\mathbf{r}\_{\mathbf{ij}}}} \mathbf{1} \mathbf{2} \overset{\mathbf{r}\_{\mathbf{ij}} \mathbf{o}}{\overset{\mathbf{r}\_{\mathbf{ij}} \mathbf{o}}{\mathbf{r}\_{\mathbf{ij}}}} \mathbf{6} \right)$$

As soon as the set of the internal coordinates of a molecular system has been determined, computer algorithms can be used to help find those coordinates which will account for the lowest energy of the system [19]. All bond angles, lengths, dihedral angles and the relative energy between various different conformations of a given system will be evaluated in order to determine the minimum energy conformation [20]. It is crucial to understand that reducing the strain energy of a given molecular system does not mean that the system will reach energy minimum (also known as global minimum). An example is the following figure (**Figure 4**)

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7

An energy minimisation algorithm will allow the rotation of groups, when their bonding allows. The rotation of the groups will give the molecule the opportunity to explore different conformations that will account for different energy values, thus allowing the compound to

Molecular modelling is very useful for investigating, comparing, analysing and visualising chemical structures and for giving qualitative and quantitative information about biological systems [22]. **Figure 5** shows a characteristic example of steric hindrance. Two dimensional models like this only contain qualitative information. Quantitative information can arise through molecular mechanics and in conjunction with a computer, where the physical properties of the molecules can be evaluated and analysed based on a set of predefined criteria concerning various chemical

with two different conformations of butane.

**Figure 5.** Steric hindrance of a small organic compound.

**Figure 4.** Two different conformations of butane.

move towards its global minimum conformation [21].

**Figure 3.** The van der Waals interactions plot and formula.

Introductory Chapter: Molecular Docking - Overview, Background, Application and What… http://dx.doi.org/10.5772/intechopen.78266 7

**Figure 4.** Two different conformations of butane.

functional groups, AMBER or CHARMM, which are parameterised to be used for peptides,

Overall through molecular mechanics the total energy of a molecule is described as a sum of all the contributions that may arise from loss of equilibrium in bond distances, also known as stretching contribution, bond angles, known as bending contribution, dihedral angles, the

The energy that is stored in chemical bonds of a molecule can describe the stretch, bend, and torsion energy whereas it is the steric attraction or repulsion that represents the non-bonded energy [15]. The latter is broken down to two different categories: the van der Waals (VDW)

A very steep energy barrier is generated at the van der Waals radius of each atom. Moreover a very shallow energy well is produced at larger separations (**Figure 3**). The inherent steric size of atoms and elements is dictated by their VDW radii. The same metric is used to describe weak attractive forces between atoms in close proximity [17]. A trivial example of the weak van der Waals attractive forces is the condensation of a gas into liquids. Furthermore it is the van der Waals radii of each element that is used for its visualisation purposes in space filling models of the molecule they participate. Steric repulsion takes place only in the case where

(2)

torsion contribution and finally non-bonded interaction contributions [14].

two atoms come closer than the sum distance of their VDW radii [18].

nucleic acids and generic macromodels [13].

6 Molecular Docking

and electrostatic interactions [16].

**Figure 3.** The van der Waals interactions plot and formula.

**Figure 5.** Steric hindrance of a small organic compound.

As soon as the set of the internal coordinates of a molecular system has been determined, computer algorithms can be used to help find those coordinates which will account for the lowest energy of the system [19]. All bond angles, lengths, dihedral angles and the relative energy between various different conformations of a given system will be evaluated in order to determine the minimum energy conformation [20]. It is crucial to understand that reducing the strain energy of a given molecular system does not mean that the system will reach energy minimum (also known as global minimum). An example is the following figure (**Figure 4**) with two different conformations of butane.

An energy minimisation algorithm will allow the rotation of groups, when their bonding allows. The rotation of the groups will give the molecule the opportunity to explore different conformations that will account for different energy values, thus allowing the compound to move towards its global minimum conformation [21].

Molecular modelling is very useful for investigating, comparing, analysing and visualising chemical structures and for giving qualitative and quantitative information about biological systems [22].

**Figure 5** shows a characteristic example of steric hindrance. Two dimensional models like this only contain qualitative information. Quantitative information can arise through molecular mechanics and in conjunction with a computer, where the physical properties of the molecules can be evaluated and analysed based on a set of predefined criteria concerning various chemical properties (such as bonding, charges, steric hindrance) [23]. Molecular Modelling can be used to study the geometry, the energy and the chemical properties *in silico* so efficiently that nowadays it is possible to predict the outcome of chemical reactions, design reactions, determine the unknown three-dimensional structures of proteins, screen and design new and effective drugs [23].

[9] Cohen NR, editor. Guidebook on Molecular Modeling in Drug Design. San Diego: Aca-

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9

[10] Deuflhard P, Hermans J, Leimkuhler B, Mark AE, Reich S, Skeel RD, editors. Computational Molecular Dynamics: Challenges, Methods, Ideas – Proceedings of the 2nd International Symposium on Algorithms for Macromolecular Modelling, Berlin, May 21-24, 1997. Vol. 4. Lecture Notes in Computational Science and Engineering. Berlin,

[11] Creighton TE, editor. Protein Folding. New York: W.H. Freeman & Company; 1992

[12] Eisenberg D, Crothers D. Physical Chemistry with Applications to the Life Science.

[13] Fersht A. Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and

[14] Frenkel D, Smit B. Understanding Molecular Simulations. From Algorithms to Appli-

[15] Gierasch LM, King J, editors. Protein Folding, Deciphering the Second Half of the

[16] Gould H, Tobochnik J. An Introduction to Computer Simulation Methods: Applications

[17] Grosberg AY, Khokhlov AR. Giant Molecules. Here, There, and Everywhere…. San

[18] Haile JM. Molecular Dynamics Simulations: Elementary Methods. New York: Wiley; 1992

[20] Leach AR. Molecular Modelling. Principles and Applications. Essex, England: Addison

[21] Lipkowitz KB, Boyd DB, editors. Reviews in Computational Chemistry. New York: VCH

[22] Allen MP, Tildesley DJ. Computer Simulation of Liquids. New York: Oxford University

[23] Bates AD, Maxwell A. DNA Topology. In Focus Series. New York: Oxford University

[19] Kalos M, Whitlock PA. Monte Carlo Methods. New York: John Wiley & Sons; 1986

to Physical Systems. Part 1 and 2. Reading, MA: Addison-Wesley; 1988

demic Press; 1996

Heidelberg: Springer-Verlag; 1999

Menlo Park, California: Benjamin Cummings; 1979

cations. San Diego, California: Academic Press; 1996

Genetic Code. Washington D.C.: AAAS; 1990

Diego, California: Academic Press; 1997

Wesley Longman; 1996

Publishers; 1990

Press; 1987

Press; 1993

Protein Folding. New York: W. H. Freeman and Company; 1999

All in all, the future is bright for molecular docking. New technologies are being developed and employed in the race against drug discovery and lethal diseases. Data mining, machine or deep learning, hyper-computers and cloud computers are just few of the emerging technologies in modern molecular docking.

## **Author details**

Dimitrios Vlachakis

Address all correspondence to: dimvl@aua.gr

Laboratory of Genetics, Department of Biotechnology, Agricultural University of Athens, Athens, Greece

## **References**


[9] Cohen NR, editor. Guidebook on Molecular Modeling in Drug Design. San Diego: Academic Press; 1996

properties (such as bonding, charges, steric hindrance) [23]. Molecular Modelling can be used to study the geometry, the energy and the chemical properties *in silico* so efficiently that nowadays it is possible to predict the outcome of chemical reactions, design reactions, determine the unknown

All in all, the future is bright for molecular docking. New technologies are being developed and employed in the race against drug discovery and lethal diseases. Data mining, machine or deep learning, hyper-computers and cloud computers are just few of the emerging technolo-

Laboratory of Genetics, Department of Biotechnology, Agricultural University of Athens,

[1] Branden C, Tooze J. Introduction to Protein Structure. 2nd ed. New York: Garland

[2] Bratley P, Fox BL, Schrage LE. A Guide to Simulation. New York: Springer-Verlag; 1987 [3] BrooksIII CL, Karplus M, Pettitt BM. A Theoretical Perspective of Dynamics, Structure,

[4] Burkert U, Allinger NL. Molecular Mechanics. Washington D.C.: American Chemical

[5] Ryckaert JP, Ciccotti G, Berendsen HJC. Numerical integration of the Cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. Journal

[6] Hess B, Bekker H, Berendsen HJC, Fraaije JGEM. LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry. 1997;**18**:1463-1472

[7] Ferguson DM, Raber DJ. A new approach to probing conformational space with molecular mechanics: Random incremental pulse search. Journal of the American Chemical

[8] Cantor CR, Schimmel PR. Biophysical Chemistry. Vol. 1-3. San Francisco: W.H. Freeman

and Thermodynamics. New York: Wiley Interscience; 1988

of Computational Physics. 1977;**23**:327-341

Society. 1989;**111**:4371-4378

and Company; 1980

three-dimensional structures of proteins, screen and design new and effective drugs [23].

gies in modern molecular docking.

Address all correspondence to: dimvl@aua.gr

**Author details**

8 Molecular Docking

Dimitrios Vlachakis

Athens, Greece

**References**

Publishing Inc; 1999

Society; 1980


**Section 2**

**Molecular Docking for Enzymes**

**Molecular Docking for Enzymes**

**Chapter 2**

Provisional chapter

**Molecular Docking Studies of Enzyme Inhibitors and**

DOI: 10.5772/intechopen.76891

Docking is a powerful approach to perform virtual screening on large library of compounds, rank the conformations using a scoring function, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. Using experimentally proven active compounds, detailed docking studies were performed to determine the mechanism of molecular interaction and its binding mode in the active site of the modeled yeast α-glucosidase and human intestinal maltase-glucoamylase. All active ligands were found to have greater binding affinity with the yeast α-glucosidase as compared to that of human homologs, intestinal, and pancreatic maltase, by an average value of ~1.3 and ~0.8 kcal/ mol, respectively. Thirty quinoline derivatives have been synthesized and evaluated against β-glucuronidase inhibitory potential. Twenty-four analogs, which showed outstanding βglucuronidase activity, have IC50 values ranging between 2.11 0.05 and 46.14 0.95 μM than standard D-saccharic acid 1,4-lactone (IC50 = 48.4 1.25 μM). Structure activity relationship and the interaction of the active compounds and enzyme active site with the help of docking studies were established. In addition, Small series of morpholine hydrazones synthesized to form morpholine hydrazones scaffold. The in vitro anti-cancer potential of all these compounds were checked against human cancer cell lines such as HepG2 (Human hepatocellular liver carcinoma) and MCF-7 (Human breast adenocarcinoma). Molecular

docking studies were also performed to understand the binding interaction.

β-glucuronidase inhibitors, morpholine hydrazone

Keywords: docking studies, α-glucosidase inhibitors, cedryl acetate, quinoline,

© 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.

Molecular Docking Studies of Enzyme Inhibitors and

**Cytotoxic Chemical Entities**

Cytotoxic Chemical Entities

Sadia Sultan, Gurmeet Kaur Surindar Singh, Kamran Ashraf and Muhammad Ashraf

Sadia Sultan, Gurmeet Kaur Surindar Singh, Kamran Ashraf and Muhammad Ashraf

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.76891

Abstract

#### **Molecular Docking Studies of Enzyme Inhibitors and Cytotoxic Chemical Entities** Molecular Docking Studies of Enzyme Inhibitors and Cytotoxic Chemical Entities

DOI: 10.5772/intechopen.76891

Sadia Sultan, Gurmeet Kaur Surindar Singh, Kamran Ashraf and Muhammad Ashraf Sadia Sultan, Gurmeet Kaur Surindar Singh, Kamran Ashraf and Muhammad Ashraf

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.76891

#### Abstract

Docking is a powerful approach to perform virtual screening on large library of compounds, rank the conformations using a scoring function, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. Using experimentally proven active compounds, detailed docking studies were performed to determine the mechanism of molecular interaction and its binding mode in the active site of the modeled yeast α-glucosidase and human intestinal maltase-glucoamylase. All active ligands were found to have greater binding affinity with the yeast α-glucosidase as compared to that of human homologs, intestinal, and pancreatic maltase, by an average value of ~1.3 and ~0.8 kcal/ mol, respectively. Thirty quinoline derivatives have been synthesized and evaluated against β-glucuronidase inhibitory potential. Twenty-four analogs, which showed outstanding βglucuronidase activity, have IC50 values ranging between 2.11 0.05 and 46.14 0.95 μM than standard D-saccharic acid 1,4-lactone (IC50 = 48.4 1.25 μM). Structure activity relationship and the interaction of the active compounds and enzyme active site with the help of docking studies were established. In addition, Small series of morpholine hydrazones synthesized to form morpholine hydrazones scaffold. The in vitro anti-cancer potential of all these compounds were checked against human cancer cell lines such as HepG2 (Human hepatocellular liver carcinoma) and MCF-7 (Human breast adenocarcinoma). Molecular docking studies were also performed to understand the binding interaction.

Keywords: docking studies, α-glucosidase inhibitors, cedryl acetate, quinoline, β-glucuronidase inhibitors, morpholine hydrazone

© 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

Due to the current problems and complicated challenges faced by medicinal chemists docking is a most demanding and efficient discipline in order to rational design new therapeutic agents for treating the human disease. Previously, the strategy for discovering new drugs consisted of taking a lead structure and developing a chemical program for finding analog molecules exhibiting the desired biological properties, the whole process involved several trial and error cycles patiently developed and analyzed by medicinal chemists utilizing their experience to ultimately select a candidate analog for further development. The entire process when looked at today, conceptually inelegant. These days picture are quite reverse after the emergence of computational chemistry discipline in science world. The concepts used in three-dimensional (3D) drug design are quite simple. New molecules are conceived either on the basis of similarities with known reference structures or on the basis of their complementarity with the 3D structure of known active sites. Molecular modeling is a discipline that contributes to the understanding of these processes in a qualitative and sometimes quantitative way [1, 2].

acetate (5), 3α,10β-dihydroxycedryl acetate (6), 3α,10α-dihydroxycedryl acetate (7), 10β,14α–dihydroxy cedryl acetate (8), 3β,10β-cedr-8(15)-ene-3,10-diol (9), and 3α, 8β, 10β -dihydroxycedrol (10) as mentioned in Figure 1. Compounds one, two, and four showed α-glucosidase inhibitory activity whereby one was more potent than the standard inhibitor,

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15

The structures have been also optimized computationally at Hartree-Fock (HF) level of theory using valence triple-zeta plus diffuse and polarization functions (6–311++G\*) basis sets for H, C, N, and O atoms to get insight into the 3D structure of these metabolites. GAMESS package [8] has been used for all quantum chemical calculations. Molecular docking studies have been also performed to delineate the ligand-protein interactions at molecular level using autodock vina programs [9]. Avogadro [10], Gabedit [11], VMD [12], and Chimera [13] have

Compounds one, two, four, and six were tested for inhibition of the α-glucosidase enzyme. For the first time, the cedrol (2) and cedryl acetate (1) demonstrated α-glucosidase inhibitory with the latter being more potent than the former. This is possibly due to the presence of an Ac group at C (8). Overall compounds one, two, and four showed more than or comparable activity to the standard inhibitors (Table 1). Apparently, the polar OH group lowers the inhibitory activity toward the enzyme, as observed in compounds four and six (inactive) in

The biological activity of ligands is a function of their 3D structures. Thus, it is crucial to have an accurate description of the ligand in 3D space. Hartree-Fock (HF) approach have been used

been used for the structure building, analysis, and visualization for our calculations.

acarbose, against yeast α-glucosidase.

2.2. α-Glucosidase inhibitory activity

comparison to one.

2.3. Geometry optimization

Figure 1. Structure of cedryl acetate and its microbial derivatives.

In this chapter we have presented a brief introduction of the available molecular docking methods, and their development and applications in drug discovery especially for synthetic and bio-transformed derivatives.

## 2. Quantum mechanical calculations and molecular docking studies of α-glucosidase inhibitors

Inhibitors of a-glucosidase regarded as a convincing therapeutic target in the development of drugs against diseases such as obesity, diabetes, HIV, and cancer [3, 4]. In this connection, few synthetic a-glucosidase inhibitors (AGI's), such as acarbose, miglitol, and voglibose are in use since last two decades. Among the six drug classes for the management of diabetes mellitus (DM), α-glucosidase inhibitors are one of them. These inhibitors are quite target specific as they act in the intestine locally, in contrast to other oral anti-hyperglycemic drugs, which in addition, alter certain biochemical processes in the human body [5]. Therefore, discovery and development of novel α-glucosidase inhibitors are urgently needed.

#### 2.1. Cedrol, cedryl acetate: microbial transformed metabolites

Development of novel α-glucosidase inhibitors requires screening of a large number of compounds. Cedryl acetate (1) and cedrol (2) are examples of newly identified α-glucosidase inhibitors that exhibit potent inhibitory activity. The most potent compound one was selected for microbial transformation and the transformed products were screened for the same activity. We successfully identified several α-glucosidase inhibitors that are more potent than acarbose [6]. However, this was the first report describing the α-glucosidase inhibitory activity of cedrol (2), cedryl acetate (1), [7] and some of the transformed products of cedryl acetate including 10βhydroxycedryl acetate (3), 2α, 10β-dihydroxycedryl acetate (4), 2α-hydroxy-10-oxocedryl

acetate (5), 3α,10β-dihydroxycedryl acetate (6), 3α,10α-dihydroxycedryl acetate (7), 10β,14α–dihydroxy cedryl acetate (8), 3β,10β-cedr-8(15)-ene-3,10-diol (9), and 3α, 8β, 10β -dihydroxycedrol (10) as mentioned in Figure 1. Compounds one, two, and four showed α-glucosidase inhibitory activity whereby one was more potent than the standard inhibitor, acarbose, against yeast α-glucosidase.

The structures have been also optimized computationally at Hartree-Fock (HF) level of theory using valence triple-zeta plus diffuse and polarization functions (6–311++G\*) basis sets for H, C, N, and O atoms to get insight into the 3D structure of these metabolites. GAMESS package [8] has been used for all quantum chemical calculations. Molecular docking studies have been also performed to delineate the ligand-protein interactions at molecular level using autodock vina programs [9]. Avogadro [10], Gabedit [11], VMD [12], and Chimera [13] have been used for the structure building, analysis, and visualization for our calculations.

#### 2.2. α-Glucosidase inhibitory activity

1. Introduction

14 Molecular Docking

and bio-transformed derivatives.

α-glucosidase inhibitors

Due to the current problems and complicated challenges faced by medicinal chemists docking is a most demanding and efficient discipline in order to rational design new therapeutic agents for treating the human disease. Previously, the strategy for discovering new drugs consisted of taking a lead structure and developing a chemical program for finding analog molecules exhibiting the desired biological properties, the whole process involved several trial and error cycles patiently developed and analyzed by medicinal chemists utilizing their experience to ultimately select a candidate analog for further development. The entire process when looked at today, conceptually inelegant. These days picture are quite reverse after the emergence of computational chemistry discipline in science world. The concepts used in three-dimensional (3D) drug design are quite simple. New molecules are conceived either on the basis of similarities with known reference structures or on the basis of their complementarity with the 3D structure of known active sites. Molecular modeling is a discipline that contributes to the understanding of these processes in a qualitative and sometimes quantitative way [1, 2].

In this chapter we have presented a brief introduction of the available molecular docking methods, and their development and applications in drug discovery especially for synthetic

2. Quantum mechanical calculations and molecular docking studies of

development of novel α-glucosidase inhibitors are urgently needed.

2.1. Cedrol, cedryl acetate: microbial transformed metabolites

Inhibitors of a-glucosidase regarded as a convincing therapeutic target in the development of drugs against diseases such as obesity, diabetes, HIV, and cancer [3, 4]. In this connection, few synthetic a-glucosidase inhibitors (AGI's), such as acarbose, miglitol, and voglibose are in use since last two decades. Among the six drug classes for the management of diabetes mellitus (DM), α-glucosidase inhibitors are one of them. These inhibitors are quite target specific as they act in the intestine locally, in contrast to other oral anti-hyperglycemic drugs, which in addition, alter certain biochemical processes in the human body [5]. Therefore, discovery and

Development of novel α-glucosidase inhibitors requires screening of a large number of compounds. Cedryl acetate (1) and cedrol (2) are examples of newly identified α-glucosidase inhibitors that exhibit potent inhibitory activity. The most potent compound one was selected for microbial transformation and the transformed products were screened for the same activity. We successfully identified several α-glucosidase inhibitors that are more potent than acarbose [6]. However, this was the first report describing the α-glucosidase inhibitory activity of cedrol (2), cedryl acetate (1), [7] and some of the transformed products of cedryl acetate including 10βhydroxycedryl acetate (3), 2α, 10β-dihydroxycedryl acetate (4), 2α-hydroxy-10-oxocedryl Compounds one, two, four, and six were tested for inhibition of the α-glucosidase enzyme. For the first time, the cedrol (2) and cedryl acetate (1) demonstrated α-glucosidase inhibitory with the latter being more potent than the former. This is possibly due to the presence of an Ac group at C (8). Overall compounds one, two, and four showed more than or comparable activity to the standard inhibitors (Table 1). Apparently, the polar OH group lowers the inhibitory activity toward the enzyme, as observed in compounds four and six (inactive) in comparison to one.

#### 2.3. Geometry optimization

The biological activity of ligands is a function of their 3D structures. Thus, it is crucial to have an accurate description of the ligand in 3D space. Hartree-Fock (HF) approach have been used

Figure 1. Structure of cedryl acetate and its microbial derivatives.


2.4. Molecular docking studies

The most ideal is to obtain the orientation of ligand in 3D space into the protein binding site for determination of ligand activity. The ligand-protein binding mode and interaction are a very crucial to understand the catalytic activity. This modeled protein has been used as our target protein. In Addition, to elucidate their binding activity with mammalian α-glucosidase, we performed molecular docking studies of the human intestinal and pancreatic maltase glucoamylase with the active compounds. We found no significant difference in the binding affinity of active ligands with yeast α-glucosidase and the human pancreatic maltase glucoamylase. However, some differences in the binding energy were observed when ligands bind with the human intestinal maltase (Table 1). The structural changes in the binding sites of these proteins are

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Figure 2. (a) Homology model of the yeast α-glucosidase (yellow color) showing the ligand cluster (variable color; licorice) into the binding site. The red color indicates the amino acid residues (labeled in white) surrounding the binding

site (b). The lower picture (c) displays the binding site cavity with the ligand cluster.

Table 1. α-Glucosidase inhibitory activity of compounds 1, 2, 4 and 6 with their predicted binding energies in the active sites of yeast and mammalian α-glucosidases.

to obtain the structural details of all metabolites that were probed through the geometry optimization in the gaseous-phase with valence triple-zeta plus diffuse and polarization functions (6–311++G\*) basis sets. We found in all the compounds studied, the distance of the bond between C and OH is 1.421 Å. The optimized geometry of these compounds also, showed a short length of carbonyl groups (C=O and COC=OCH3) distance of 1.208 Å. However, the bond order was slightly higher by a value of 0.11 in the case of C=O as expected. The carbon– oxygen bond in C-OCOCH3 was slightly larger as compared to that in CO-COCH3 (1.402 and 1.338 Å, respectively) due to a lower bond order by a value of 0.233. The presence of acetate group (-O-CO-CH3) in the molecule was lowered the dipole moment of the molecule as could be seen in Table 2. These compounds with a low dipole moment seem to be most active. However, due to limited experimental inhibitory assay data, it was difficult to make a generalize conclusion.


Table 2. Dipole moment of metabolites calculated at HF/6–311++G\* level of theory and basis sets.

#### 2.4. Molecular docking studies

to obtain the structural details of all metabolites that were probed through the geometry optimization in the gaseous-phase with valence triple-zeta plus diffuse and polarization functions (6–311++G\*) basis sets. We found in all the compounds studied, the distance of the bond between C and OH is 1.421 Å. The optimized geometry of these compounds also, showed a short length of carbonyl groups (C=O and COC=OCH3) distance of 1.208 Å. However, the bond order was slightly higher by a value of 0.11 in the case of C=O as expected. The carbon– oxygen bond in C-OCOCH3 was slightly larger as compared to that in CO-COCH3 (1.402 and 1.338 Å, respectively) due to a lower bond order by a value of 0.233. The presence of acetate group (-O-CO-CH3) in the molecule was lowered the dipole moment of the molecule as could be seen in Table 2. These compounds with a low dipole moment seem to be most active. However, due to limited experimental inhibitory assay data, it was difficult to make a gener-

Table 1. α-Glucosidase inhibitory activity of compounds 1, 2, 4 and 6 with their predicted binding energies in the active

alize conclusion.

Compound IC50\* (in

16 Molecular Docking

mM S.E.M)

sites of yeast and mammalian α-glucosidases.

Binding energy in kcal/mol (Yeast aglucosidase)

 94 15 8.4 6.9 6.5 7.9 130 15 7.4 6.6 6.2 7.9 690 16 7.9 6.3 7.1 7.6 Inactive 8.2 6.4 6.5 7.6 Acarbose 780 20 — — —— Deoxynojirimycin 425.6 8.14 — — ——

Compound Dipole (Debye)

Table 2. Dipole moment of metabolites calculated at HF/6–311++G\* level of theory and basis sets.

1 2.03 2 3.03 3 2.87 4 3.87 5 5.07 6 3.90 7 4.09 8 3.93 9 2.65 10 6.01

Binding energy in kcal/mol

N-terminal domain (3L4T.pdb) Binding energy in kcal/mol (Human pancreatic amylase; 1 U33.pdb)

(Human maltase glucoamylase)

C-terminal domain (3TOP.pdb) The most ideal is to obtain the orientation of ligand in 3D space into the protein binding site for determination of ligand activity. The ligand-protein binding mode and interaction are a very crucial to understand the catalytic activity. This modeled protein has been used as our target protein. In Addition, to elucidate their binding activity with mammalian α-glucosidase, we performed molecular docking studies of the human intestinal and pancreatic maltase glucoamylase with the active compounds. We found no significant difference in the binding affinity of active ligands with yeast α-glucosidase and the human pancreatic maltase glucoamylase. However, some differences in the binding energy were observed when ligands bind with the human intestinal maltase (Table 1). The structural changes in the binding sites of these proteins are

Figure 2. (a) Homology model of the yeast α-glucosidase (yellow color) showing the ligand cluster (variable color; licorice) into the binding site. The red color indicates the amino acid residues (labeled in white) surrounding the binding site (b). The lower picture (c) displays the binding site cavity with the ligand cluster.

postulated to be the cause of this less affinity of ligands toward intestinal maltase as compared to the yeast α-glucosidase. Figure 2a shows the homology model of the yeast α-glucosidase with the ligand cluster into the binding site. Figure 2b displays the close view of the binding site with the best predicted orientation of ligands 1–15, obtained from the molecular docking studies, almost overlapping with each other to form a cluster. The amino acid residues forming the binding site cavity have been labeled in white. The cavity can be clearly visualized when the protein is shown with the surface model as depicted in Figure 2c.

in agreement with the enzymatic assay. The metabolite 2, showed no interaction with the residues. The acetate group of metabolite two has been hydrolyzed to form hydroxyl group that may play a partial role in its low activity (Figure 3b) as compared to the compound one. Metabolites four and six are acetylated and they do form H-bonds with Asp349 and Arg439, thereby showing a good ligand-protein binding energy, however, their activity is dramatically lowered or diminished as compared to compound one. This attenuate activity of metabolites four and six may be associated with their high-polarity arising from the introduction of two hydroxyl groups into the rings, partially due to the fact that the neighboring residues around -

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3. Molecular docking studies of novel quinoline derivatives as potent

3.1. Novel quinoline derivatives as potent β-glucuronidase inhibitors

Glucuronidase has been used in numerous biotechnology and research applications. Glucuronidase as a gene has been studied as a positive selection marker for transformed plants, bacteria, and fungi carrying glucuronidase gene [14, 15]. It is also widely has been used for the structural investigations of proteoglycans and for research purposes in many diagnostic

Quinoline is an aromatic compound having an aza-heterocyclic ring. It possesses a weak tertiary base that can undergo both nucleophilic and electrophilic substitution reactions. The quinoline moiety is present in several pharmacologically active compounds as it does not harm

Various classes of compounds that showed considerable potential as β-glucuronidase inhibitors involved benzothiazole, bisindolylmethanes, bisindolylmethane-hydrazone, benzimidazole, unsymmetrical heterocyclic thioureas, 2,5-disubtituted-1,3,4-oxadiazoles with benzimidazole backbone, and benzohydrazone–oxadiazole [17]. In continuation of this work our study identified novel series of potent β-glucuronidase inhibitors of quinoline for further investigation [18].

Thirty analogs of quinoline were synthesized, which have varied degree of β-glucorinadase inhibition ranging in between 2.11 0.05 and 80.10 1.80 μM, when compared with the standard inhibitor D-saccharic acid 1,4 lactone having IC50 value 48.4 1.25 μM. Out of these thirty analogs, twenty four analogs 1–30 showed outstanding β-glucorinadase inhibitory potential with IC50 values (Table 3) analogs 17, 20, 21, and 27–29 showed good β-glucorinadase inhibitory potential. The structure–activity relationship studies suggested that the βglucuronidase inhibitory activities of this class of compounds are mainly dependent upon the

OH are slightly hydrophobic in nature.

β-glucuronidase inhibitors

humans, when it is orally absorbed or inhaled.

3.2. β-Glucorinadase inhibitory activity

substitutions on the phenyl ring.

research laboratories [16].

Figure 3 displays the interactions of individual metabolites one, two, four, and six with the yeast α-glucosidase protein. Polar amino acid residues, that is, Asp349 and Arg439 have strong H-bonding with the acetate group of the ligand. Cedryl acetate (1) exhibits the strongest binding affinity with the protein as inferred by its lowest binding energy (8.4 kcal/mol), the values are given in Table 1. Compound one had the lowest IC50 of 94 15 μM, which makes it

Figure 3. Ligand-protein interaction studies of compounds (a) 1, (b) 2, (c) 4, and (d) 6. The hydrogen bonds are shown as black dotted lines. The H-bond distances in Å are given in boxes. The amino acid residues in the binding pockets are indicated as red.

in agreement with the enzymatic assay. The metabolite 2, showed no interaction with the residues. The acetate group of metabolite two has been hydrolyzed to form hydroxyl group that may play a partial role in its low activity (Figure 3b) as compared to the compound one. Metabolites four and six are acetylated and they do form H-bonds with Asp349 and Arg439, thereby showing a good ligand-protein binding energy, however, their activity is dramatically lowered or diminished as compared to compound one. This attenuate activity of metabolites four and six may be associated with their high-polarity arising from the introduction of two hydroxyl groups into the rings, partially due to the fact that the neighboring residues around - OH are slightly hydrophobic in nature.

## 3. Molecular docking studies of novel quinoline derivatives as potent β-glucuronidase inhibitors

Glucuronidase has been used in numerous biotechnology and research applications. Glucuronidase as a gene has been studied as a positive selection marker for transformed plants, bacteria, and fungi carrying glucuronidase gene [14, 15]. It is also widely has been used for the structural investigations of proteoglycans and for research purposes in many diagnostic research laboratories [16].

#### 3.1. Novel quinoline derivatives as potent β-glucuronidase inhibitors

Quinoline is an aromatic compound having an aza-heterocyclic ring. It possesses a weak tertiary base that can undergo both nucleophilic and electrophilic substitution reactions. The quinoline moiety is present in several pharmacologically active compounds as it does not harm humans, when it is orally absorbed or inhaled.

Various classes of compounds that showed considerable potential as β-glucuronidase inhibitors involved benzothiazole, bisindolylmethanes, bisindolylmethane-hydrazone, benzimidazole, unsymmetrical heterocyclic thioureas, 2,5-disubtituted-1,3,4-oxadiazoles with benzimidazole backbone, and benzohydrazone–oxadiazole [17]. In continuation of this work our study identified novel series of potent β-glucuronidase inhibitors of quinoline for further investigation [18].

#### 3.2. β-Glucorinadase inhibitory activity

postulated to be the cause of this less affinity of ligands toward intestinal maltase as compared to the yeast α-glucosidase. Figure 2a shows the homology model of the yeast α-glucosidase with the ligand cluster into the binding site. Figure 2b displays the close view of the binding site with the best predicted orientation of ligands 1–15, obtained from the molecular docking studies, almost overlapping with each other to form a cluster. The amino acid residues forming the binding site cavity have been labeled in white. The cavity can be clearly visualized when the

Figure 3 displays the interactions of individual metabolites one, two, four, and six with the yeast α-glucosidase protein. Polar amino acid residues, that is, Asp349 and Arg439 have strong H-bonding with the acetate group of the ligand. Cedryl acetate (1) exhibits the strongest binding affinity with the protein as inferred by its lowest binding energy (8.4 kcal/mol), the values are given in Table 1. Compound one had the lowest IC50 of 94 15 μM, which makes it

Figure 3. Ligand-protein interaction studies of compounds (a) 1, (b) 2, (c) 4, and (d) 6. The hydrogen bonds are shown as black dotted lines. The H-bond distances in Å are given in boxes. The amino acid residues in the binding pockets are

indicated as red.

18 Molecular Docking

protein is shown with the surface model as depicted in Figure 2c.

Thirty analogs of quinoline were synthesized, which have varied degree of β-glucorinadase inhibition ranging in between 2.11 0.05 and 80.10 1.80 μM, when compared with the standard inhibitor D-saccharic acid 1,4 lactone having IC50 value 48.4 1.25 μM. Out of these thirty analogs, twenty four analogs 1–30 showed outstanding β-glucorinadase inhibitory potential with IC50 values (Table 3) analogs 17, 20, 21, and 27–29 showed good β-glucorinadase inhibitory potential. The structure–activity relationship studies suggested that the βglucuronidase inhibitory activities of this class of compounds are mainly dependent upon the substitutions on the phenyl ring.

The most potent inhibition was noted in analog 13 that have hydroxy groups at 3, 4-positions on the phenyl part. Making comparison of analog 13 having IC50 value 2.11 0.05 μM with other dihydroxy analogs such as 12, 14, and 15 having IC50 values 3.10 0.10, 5.01 0.20, and

18.10 0.40 24 18.00 0.30

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9.60 0.20 25 22.30 0.45

46.14 0.95 26 38.50 0.80

3.10 0.10 27 51.00 1.25

2.11 0.05 28 80.10 1.80

5.01 0.20 29 69.40 1.30

2.60 0.05 30 46.10 0.85

D-Saccharic acid 1,4-lactone 48.4 1.25 <sup>a</sup> IC50 values are expressed as mean ± standard error of mean.

Table 3. Different quinoline derivatives and their β-glucuronidase activity.

Molecular Docking Studies of Enzyme Inhibitors and Cytotoxic Chemical Entities http://dx.doi.org/10.5772/intechopen.76891 

Table 3. Different quinoline derivatives and their β-glucuronidase activity.

Quinoline hydrazones (1-30) No R IC50 SEM<sup>a</sup> [μM] No R IC50 SEMa [μM] 18.40 0.45 16 16.60 0.55

Molecular Docking

42.25 0.80 17 78.90 1.50

24.20 0.40 18 44.10 0.70

9.20 0.30 19 19.40 0.90

37.01 0.70 20 68.38 1.25

26.30 0.50 21 49.38 0.90

38.50 0.80 22 9.60 0.20

6.70 0.25 23 8.30 0.20

The most potent inhibition was noted in analog 13 that have hydroxy groups at 3, 4-positions on the phenyl part. Making comparison of analog 13 having IC50 value 2.11 0.05 μM with other dihydroxy analogs such as 12, 14, and 15 having IC50 values 3.10 0.10, 5.01 0.20, and 2.60 0.05 μM, respectively, analog 13 was found to be superior than other. In analog 13 the two hydroxy groups are present at meta-para position while in analog 12 the two hydroxy groups are present at ortho-para positions, in analog 14 the two hydroxy groups are present at ortho-meta positions, and in analog 15 the two hydroxy groups are present at ortho-meta positions. The little bit difference in the activity of these analogs may be due to the difference in position of the substituents on the phenyl part.

Similarly, effect of substituent position was also observed in other analogs such as 4, 5, and 6 having fluoro group. If we compare analog four, a ortho analog, having IC50 value 9.20 0.30 μM with analog five, a meta analog, and six, a para analog having IC50 values 37.01 0.70 and 26.30 0.50 μM, respectively. In analog four the fluoro group is present at ortho position while in analog five the floro group is present at meta position and in analog six the floro group is present at para position. These three analogs demonstrated minute differences in their activity possibly due to the difference in the position of the substituents of the phenyl section. This was also observed in monohydroxy analogs. From these findings, we concluded that the factors that influence the inhibitory potentials of these analogs include the nature, position, and the number of substituents.

### 3.3. Docking studies

Molecular docking is a useful tool to obtain data on binding mode and to validate experimental results of active derivatives within the active site of β-D-glucuronidase. By using X-ray crystal structure of the human β-glucuronidase enzyme at 2.6 Å resolution (PDB ID: 1BHG) [19], it can be used to identify predict the binding modes involved in the inhibition activity.

was found to be to most active compound in this series, because of the hydroxyl (OH) at C-4 involved in hydrogen bonding with Oε2 of Glu451 side chain (1.99 Å). The complex is stabilized by π-donor hydrogen bond formation between the benzene ring on quinoline moiety and with hydroxyl (OH) of Tyr508 (3.73 Å). Two interactions were detected in hydrazone linkage between carboxamide and the surrounding residues. The hydrazone carbonyl (C=O) oxygen linked by a hydrogen bonding with the nitrogen on the backbone of Tyr504 (2.77 Å), another hydrogen bond forms between the NH group and the oxygen on side chain of Asn484 with a bond length of 3.10 Å. The two hydroxyls on the benzylidene moiety at meta positions also, involved in hydrogen bonds with indole nitrogen at Trp528 backbone having a distance of 2.11

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Figure 5. Best binding position of compound 13 in active pocket of β-glucuronidase enzyme.

Compound 15 showed that hydroxyl (OH) at C-4 of quinoline moiety for compound formed hydrogen bonding with Oε2 of Glu451 side chain at a longer distance (2.24 Å) as compared to previous compound (Figure 6). In this compound the quinoline benzene rings on forms a π-donor hydrogen bond with hydroxyl (OH) of Tyr508 at (3.96 Å). It was also observed that hydrazone linkage was oxygen of carbonyl (C=O) interacts with side chain of Tyr504 through a hydrogen bond at a distance of 2.80 Å. Both form hydrogen bonds were formed between hydroxyls at ortho and meta position on the benzylidene moiety and nitrogen of indole backbone of Tyr508 at a distance of 2.19 and 1.99 Å, respectively. Compound 15 was found to be a

In third most active compound 12 (Figure 7), it was observed that hydroxyl (OH) at Carbon no 4 exhibited hydrogen bonding with Oε2 of Glu451 side chain with a distance of 2.11 Å. On the other hand we noted that a more stable π-donor hydrogen bond with hydroxyl (OH) of Tyr508 at (3.77 Å) and benzene ring on quinoline moiety when compared with derivative 14. Docking studies also showed the hydrazone linkage interaction of oxygen of carbonyl (C=O) with side chain of Tyr504 through a hydrogen bond with length of 2.99 Å. There is also a hydrogen

and 1.99 Å, respectively.

second most active inhibitor.

Utilizing docking approach, we identified the stable binding mode of six most active compounds (8, 12–15, and 23) that was further used in characterizing their inhibitory activity. Compounds with the most stable binding conformation suggest to strongly alignment to the core of β-glucuronidase. In Figure 4 shows that the quinolone moiety of these active compounds are oriented toward the active pocket and share some common interaction with catalytically important amino acids such as Glu450, Glu541, and Tyr504.

We predict that the hydrogen bonding interaction between the hydroxyl at C-4 of quinoline moiety and Glu451 plays a vital role. According to the docking result compound 13 (Figure 5),

Figure 4. Active compounds aligned well into the binding cavity of β-glucuronidase enzyme.

Figure 5. Best binding position of compound 13 in active pocket of β-glucuronidase enzyme.

2.60 0.05 μM, respectively, analog 13 was found to be superior than other. In analog 13 the two hydroxy groups are present at meta-para position while in analog 12 the two hydroxy groups are present at ortho-para positions, in analog 14 the two hydroxy groups are present at ortho-meta positions, and in analog 15 the two hydroxy groups are present at ortho-meta positions. The little bit difference in the activity of these analogs may be due to the difference

Similarly, effect of substituent position was also observed in other analogs such as 4, 5, and 6 having fluoro group. If we compare analog four, a ortho analog, having IC50 value 9.20 0.30 μM with analog five, a meta analog, and six, a para analog having IC50 values 37.01 0.70 and 26.30 0.50 μM, respectively. In analog four the fluoro group is present at ortho position while in analog five the floro group is present at meta position and in analog six the floro group is present at para position. These three analogs demonstrated minute differences in their activity possibly due to the difference in the position of the substituents of the phenyl section. This was also observed in monohydroxy analogs. From these findings, we concluded that the factors that influence the inhibitory potentials of these analogs include the nature, position, and the number of substituents.

Molecular docking is a useful tool to obtain data on binding mode and to validate experimental results of active derivatives within the active site of β-D-glucuronidase. By using X-ray crystal structure of the human β-glucuronidase enzyme at 2.6 Å resolution (PDB ID: 1BHG) [19],

Utilizing docking approach, we identified the stable binding mode of six most active compounds (8, 12–15, and 23) that was further used in characterizing their inhibitory activity. Compounds with the most stable binding conformation suggest to strongly alignment to the core of β-glucuronidase. In Figure 4 shows that the quinolone moiety of these active compounds are oriented toward the active pocket and share some common interaction with

We predict that the hydrogen bonding interaction between the hydroxyl at C-4 of quinoline moiety and Glu451 plays a vital role. According to the docking result compound 13 (Figure 5),

it can be used to identify predict the binding modes involved in the inhibition activity.

catalytically important amino acids such as Glu450, Glu541, and Tyr504.

Figure 4. Active compounds aligned well into the binding cavity of β-glucuronidase enzyme.

in position of the substituents on the phenyl part.

3.3. Docking studies

22 Molecular Docking

was found to be to most active compound in this series, because of the hydroxyl (OH) at C-4 involved in hydrogen bonding with Oε2 of Glu451 side chain (1.99 Å). The complex is stabilized by π-donor hydrogen bond formation between the benzene ring on quinoline moiety and with hydroxyl (OH) of Tyr508 (3.73 Å). Two interactions were detected in hydrazone linkage between carboxamide and the surrounding residues. The hydrazone carbonyl (C=O) oxygen linked by a hydrogen bonding with the nitrogen on the backbone of Tyr504 (2.77 Å), another hydrogen bond forms between the NH group and the oxygen on side chain of Asn484 with a bond length of 3.10 Å. The two hydroxyls on the benzylidene moiety at meta positions also, involved in hydrogen bonds with indole nitrogen at Trp528 backbone having a distance of 2.11 and 1.99 Å, respectively.

Compound 15 showed that hydroxyl (OH) at C-4 of quinoline moiety for compound formed hydrogen bonding with Oε2 of Glu451 side chain at a longer distance (2.24 Å) as compared to previous compound (Figure 6). In this compound the quinoline benzene rings on forms a π-donor hydrogen bond with hydroxyl (OH) of Tyr508 at (3.96 Å). It was also observed that hydrazone linkage was oxygen of carbonyl (C=O) interacts with side chain of Tyr504 through a hydrogen bond at a distance of 2.80 Å. Both form hydrogen bonds were formed between hydroxyls at ortho and meta position on the benzylidene moiety and nitrogen of indole backbone of Tyr508 at a distance of 2.19 and 1.99 Å, respectively. Compound 15 was found to be a second most active inhibitor.

In third most active compound 12 (Figure 7), it was observed that hydroxyl (OH) at Carbon no 4 exhibited hydrogen bonding with Oε2 of Glu451 side chain with a distance of 2.11 Å. On the other hand we noted that a more stable π-donor hydrogen bond with hydroxyl (OH) of Tyr508 at (3.77 Å) and benzene ring on quinoline moiety when compared with derivative 14. Docking studies also showed the hydrazone linkage interaction of oxygen of carbonyl (C=O) with side chain of Tyr504 through a hydrogen bond with length of 2.99 Å. There is also a hydrogen

are normally under tight control [20, 21]. In modern life, cancer is one of the big health killers. According to the American Association for cancer research (AACR) cancer progress report 2013, it expected that 580,350 Americans would die from the various type of cancer in the same year. Luckily, ultimate evolution has made against cancer. Approximately, from 1990 to 2012

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Currently chemotherapy is an ultimate clinic treatment to repel cancer [23]. Cisplatin drug has been commonly used in cancer treatment for decades [24, 25]. Though, its clinical value tends to be inadequate by the abrupt increase of drug resistance or new side effects [26]. Consequently, the exploration of unusual chemotherapeutic agents has sparked the great attention of

The morpholine scaffold has been found to be an outstanding pharmacophore in medicinal chemistry and a number of molecules having morpholine skeleton are the clinically approved drugs [27]. N-substituted morpholines are used in the treatment of inflammatory diseases, such as migraine and asthma [28]. Morpholines derivatives have reported to possess activity such as platelet aggregation inhibitors, anti-eme-tics, and bronchodilators [29]. Morpholine analogs establish a new antifungal chemical entity not allied with other presently available medications with anti-fungal potential. The benefit in synthesizing morpholine analogs resides in the fact that these molecules offer chlorohydrates that are water soluble for pharmacological

Recently, we have reported synthesis, characterization, anti-cancer activity, and molecular docking studies of morpholine derivatives [32]. A small series of morpholine hydrazones synthesized by treating 5-morpholinothiophene-2-carbaldehyde with different aryl hydrazides to form morpholine hydrazones scaffold (1–17) (Table 4). The in vitro anti-cancer potential of all these compounds were checked against human cancer cell lines such as HepG2 (Human hepatocellular liver carcinoma) and MCF-7 (Human breast adenocarcinoma). Analogs 13 had similar substantial cytotoxic effects toward HepG2 with IC50 value 6.31 1.03 μmol/L when compared with the standard doxorubicin (IC50 value 6.00 0.80 μmol/L); while compounds five, eight, and nine showed potent cytotoxicity against MCF-7 with IC50 value 7.08 0.42, 1.26 0.34, and 11.22 0.22 μmol/L, respectively, when compared with the standard Tamoxifen (IC50 = 11.00 0.40 μmol/L). Molecular docking studies also performed to understand the

All synthesized analogs (1–17) were screened against two human cancer cell lines, human breast carcinoma (MCF-7) and human liver carcinoma (HepG2). The potentials of these analogs calculated in IC50 value shown in Table 5. Among the series 10 compounds showed

Among them compound eight was found to be the excellent inhibitor against MCF-7 with IC50 value 1.26 0.34 μmol/L, which is more potent than the standard inhibitor Tamoxifen (IC50 = 11.00 0.40 μmol/L). Secondly, the compound five was found to be more potent with IC50 value 7.08 0.42 μmol/L almost two fold better than the standard. The analogs such as

potential against HepG2 and six compounds showed potential against MCF-7.

almost 1,024,400 lives saved [22].

scientists from varied disciplines.

assays [30, 31].

binding interaction.

4.1. In vitro anti-cancer activity

Figure 6. Binding positions of compound 15 in an active pocket of the β-glucuronidase enzyme.

Figure 7. Binding position of compound 12 in an active pocket of the β-glucuronidase enzyme.

bonding of hydroxyl at ortho position on the benzylidene with the oxygen of Asn502 (1.87 Å), while the other another hydrogen bonding of hydroxyl at para position on the benzylidene with the nitrogen of indole backbone of Trp528 (1.89 Å).

## 4. Morpholine hydrazone scaffold: synthesis, anticancer activity, and docking studies

Cancer is a broad term to describe a disease that characterized by the uncontrolled proliferation of cells resulting from the disruption or dysfunction of regulatory signaling pathways that are normally under tight control [20, 21]. In modern life, cancer is one of the big health killers. According to the American Association for cancer research (AACR) cancer progress report 2013, it expected that 580,350 Americans would die from the various type of cancer in the same year. Luckily, ultimate evolution has made against cancer. Approximately, from 1990 to 2012 almost 1,024,400 lives saved [22].

Currently chemotherapy is an ultimate clinic treatment to repel cancer [23]. Cisplatin drug has been commonly used in cancer treatment for decades [24, 25]. Though, its clinical value tends to be inadequate by the abrupt increase of drug resistance or new side effects [26]. Consequently, the exploration of unusual chemotherapeutic agents has sparked the great attention of scientists from varied disciplines.

The morpholine scaffold has been found to be an outstanding pharmacophore in medicinal chemistry and a number of molecules having morpholine skeleton are the clinically approved drugs [27]. N-substituted morpholines are used in the treatment of inflammatory diseases, such as migraine and asthma [28]. Morpholines derivatives have reported to possess activity such as platelet aggregation inhibitors, anti-eme-tics, and bronchodilators [29]. Morpholine analogs establish a new antifungal chemical entity not allied with other presently available medications with anti-fungal potential. The benefit in synthesizing morpholine analogs resides in the fact that these molecules offer chlorohydrates that are water soluble for pharmacological assays [30, 31].

Recently, we have reported synthesis, characterization, anti-cancer activity, and molecular docking studies of morpholine derivatives [32]. A small series of morpholine hydrazones synthesized by treating 5-morpholinothiophene-2-carbaldehyde with different aryl hydrazides to form morpholine hydrazones scaffold (1–17) (Table 4). The in vitro anti-cancer potential of all these compounds were checked against human cancer cell lines such as HepG2 (Human hepatocellular liver carcinoma) and MCF-7 (Human breast adenocarcinoma). Analogs 13 had similar substantial cytotoxic effects toward HepG2 with IC50 value 6.31 1.03 μmol/L when compared with the standard doxorubicin (IC50 value 6.00 0.80 μmol/L); while compounds five, eight, and nine showed potent cytotoxicity against MCF-7 with IC50 value 7.08 0.42, 1.26 0.34, and 11.22 0.22 μmol/L, respectively, when compared with the standard Tamoxifen (IC50 = 11.00 0.40 μmol/L). Molecular docking studies also performed to understand the binding interaction.

#### 4.1. In vitro anti-cancer activity

bonding of hydroxyl at ortho position on the benzylidene with the oxygen of Asn502 (1.87 Å), while the other another hydrogen bonding of hydroxyl at para position on the benzylidene

Cancer is a broad term to describe a disease that characterized by the uncontrolled proliferation of cells resulting from the disruption or dysfunction of regulatory signaling pathways that

4. Morpholine hydrazone scaffold: synthesis, anticancer activity, and

Figure 7. Binding position of compound 12 in an active pocket of the β-glucuronidase enzyme.

Figure 6. Binding positions of compound 15 in an active pocket of the β-glucuronidase enzyme.

with the nitrogen of indole backbone of Trp528 (1.89 Å).

docking studies

24 Molecular Docking

All synthesized analogs (1–17) were screened against two human cancer cell lines, human breast carcinoma (MCF-7) and human liver carcinoma (HepG2). The potentials of these analogs calculated in IC50 value shown in Table 5. Among the series 10 compounds showed potential against HepG2 and six compounds showed potential against MCF-7.

Among them compound eight was found to be the excellent inhibitor against MCF-7 with IC50 value 1.26 0.34 μmol/L, which is more potent than the standard inhibitor Tamoxifen (IC50 = 11.00 0.40 μmol/L). Secondly, the compound five was found to be more potent with IC50 value 7.08 0.42 μmol/L almost two fold better than the standard. The analogs such as

two, seven, nine, and 11 also showed potent inhibition for this cell line, while remaining

S. No. HepG2 MCF-7 S. No. HepG2 MCF-7 2 — 30.0 1.00 9 40.0 0.93 11.22 0.22 4 7.94 7.94 — 11 19.95 1.31 41.67 1.62

Molecular Docking Studies of Enzyme Inhibitors and Cytotoxic Chemical Entities

http://dx.doi.org/10.5772/intechopen.76891

27

 19.95 0.63 7.08 0.42 12 31.0 2.26 — 12.59 1.22 — 13 6.31 1.03 — 20.0 0.32 14.13 1.42 14 56.23 0.56 — — 1.26 0.34 15 15.85 0.82 —

Compound 13 showed potent inhibition against HepG2 with IC50 value 6.31 1.03 μmol/L when compared with the standard Doxorubicin (IC50 value 6.00 0.80 μmol/L). Compound four and six were found second and third most active analogs among the series with IC50 value 7.94 7.94 and 12.59 1.22 μM, respectively. Other analogs such as five, seven, nine, 11, 12,

Molecular docking studies were performed to investigate the binding mode of the active

The molecular docking procedure was widely used to predict the binding interaction of the compound in the binding pocket of the enzyme. The 3D crystal structure of the topoisomerase II enzyme (PDB id: 4FM9) was retrieved from the protein data bank. All the ions and water molecules removed and the hydrogen atoms added to the enzyme by the 3D protonation using the Molecular Operating Environment (MOE) software. The target enzymes were then energy minimized by the default parameters of the MOE for the stability and further assessment of the enzyme. The structures of the analogs of the morpholinothiophene hydrazone compounds built in MOE and energy minimized using the MMFF94x force field and gradient 0.05. The active site pocket of the enzyme found out by the site-finder implemented in the MOE software. The synthesized compounds docked into the active site of the target enzyme in MOE by the default parameters, that is, placement: Triangle matcher, Rescoring, and London dG. For each ligand, 10 conformations gen-

Molecular docking studies predicted the proper orientation of the compound five inside the binding pocket of topoisomerase II enzyme. From the docking conformation of this active compound, we have observed a docking score of (11.4975), which correlates well to the biological activities (IC50 = 19.95 0.63 μmol/L in HepG2 and 7.08 0.42 μmol/L in MCF-7

4.1.1. Molecular docking analysis of morpholinothiophene hydrazone compounds

Table 5. Anti-cancer activity data (IC50 values in μmol/L) of morpholine derivatives (1–17).

erated. The top-ranked conformation of each compound used for further analysis.

analogs found to be completely in active.

Doxorubicin 6.00 0.80 —

Tamoxifen — 11.00 0.40 Cisplatin 12.00 0.33 15.00 0.80

compounds.

14, and 15 also showed good to moderate potential.

Table 4. Various analogs of morpholine.


Table 5. Anti-cancer activity data (IC50 values in μmol/L) of morpholine derivatives (1–17).

two, seven, nine, and 11 also showed potent inhibition for this cell line, while remaining analogs found to be completely in active.

Compound 13 showed potent inhibition against HepG2 with IC50 value 6.31 1.03 μmol/L when compared with the standard Doxorubicin (IC50 value 6.00 0.80 μmol/L). Compound four and six were found second and third most active analogs among the series with IC50 value 7.94 7.94 and 12.59 1.22 μM, respectively. Other analogs such as five, seven, nine, 11, 12, 14, and 15 also showed good to moderate potential.

Molecular docking studies were performed to investigate the binding mode of the active compounds.

#### 4.1.1. Molecular docking analysis of morpholinothiophene hydrazone compounds

Table 4. Various analogs of morpholine.

26 Molecular Docking

The molecular docking procedure was widely used to predict the binding interaction of the compound in the binding pocket of the enzyme. The 3D crystal structure of the topoisomerase II enzyme (PDB id: 4FM9) was retrieved from the protein data bank. All the ions and water molecules removed and the hydrogen atoms added to the enzyme by the 3D protonation using the Molecular Operating Environment (MOE) software. The target enzymes were then energy minimized by the default parameters of the MOE for the stability and further assessment of the enzyme. The structures of the analogs of the morpholinothiophene hydrazone compounds built in MOE and energy minimized using the MMFF94x force field and gradient 0.05. The active site pocket of the enzyme found out by the site-finder implemented in the MOE software. The synthesized compounds docked into the active site of the target enzyme in MOE by the default parameters, that is, placement: Triangle matcher, Rescoring, and London dG. For each ligand, 10 conformations generated. The top-ranked conformation of each compound used for further analysis.

Molecular docking studies predicted the proper orientation of the compound five inside the binding pocket of topoisomerase II enzyme. From the docking conformation of this active compound, we have observed a docking score of (11.4975), which correlates well to the biological activities (IC50 = 19.95 0.63 μmol/L in HepG2 and 7.08 0.42 μmol/L in MCF-7

Malaysia. One of our author Gurmeet Kaur Surindar Singh would also like to acknowledge Universiti Teknologi MARA for the financial support under the reference number UiTM 600- IRMI/FRGS 5/3 (28/2015), Ministry of Higher Education Malaysia. We also would like to high-

Molecular Docking Studies of Enzyme Inhibitors and Cytotoxic Chemical Entities

http://dx.doi.org/10.5772/intechopen.76891

29

Sadia Sultan1,2,3\*, Gurmeet Kaur Surindar Singh1,3, Kamran Ashraf1 and Muhammad Ashraf<sup>4</sup>

\*Address all correspondence to: sadiasultan301@yahoo.com; drsadia@puncakalam.uitm.edu.my 1 Faculty of Pharmacy, Universiti Teknologi MARA, Puncak Alam Campus, Bandar Puncak

2 Atta-ur-Rahman Institute for Natural Products Discovery (AuRIns), Universiti Teknologi MARA, Puncak Alam Campus, Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia

[1] Meyer EF, Swanson MS, Williams JA. Pharmacology & Therapeutics. 2000;85:113-121

[2] Abraham DJ. Burger's Medicinal Chemistry Drug Discovery. 6th ed. Vol. 12003. pp. 847-900 [3] Pili R, Chang J, Partis RA, Mueller RA, Chrest FJ. A. Passaniti Cancer. Research. 1995;55:2920 [4] Zitzmann N, Mehta AS, Carrouée S, et al. Proceedings of the National Academy of

[5] Hitoshi S, Nagao M, Harada T, Nakajima Y, Tanimura-Inagaki K, et al. Journal of Diabetes

[6] Choudhary MI, Atif M, Shah SAA, Sultan S, Erum S, Khan SN, Atta-ur-Rahman. Interna-

[7] Sultan S, Choudhary MI, Nahar Khan S, Fatima U, Ali RA, Atif M, Atta-ur-Rahman, Fatmi

[9] Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Journal of

[11] Humphrey W, Dalke A, Schulten K. Journal of Molecular Graphics. 1996;14:33-38

3 Pharmaceutical and Life Sciences Core, Universiti Teknologi MARA, Shah Alam,

light and acknowledge our previous published research under reference 7, 18, and 32.

Author details

References

Alam, Selangor Darul Ehsan, Malaysia

Shah Alam, Selangor Darul Ehsan, Malaysia

Investigation. 2014;23:206-212

Cheminformatics. 2012;4:17

4 Merck Pharmaceutical Private Limited, Karachi, Pakistan

Sciences of the United States of America. 1999;96:11878

tional Journal of Pharmaceutical Science. 2014;6:375-378

MQ. European Journal of Medicinal Chemistry. 2013;62:764-770

[10] Allouche AR. Journal of Computational Chemistry. 2011;32:174-182

[8] Trott O, Olson AJ. Journal of Computational Chemistry. 2010;31:455-461

Figure 8. Docking conformations of compound five in the active site of topoisomerase II enzyme.

cell lines). The compound was observed making two interactions with active residues of the active site pocket of the enzyme. The oxygen atom of the morpholine moiety of the compound formed side chain acceptor interaction with the Lys 990 residue of the binding pocket. Arg 929 was observed making the hydrogen bond with the –NH group of the hydrazine moiety of the ligand as shown in the Figure 8. The electro-negative nature of Cl, O, and S of the substituent moiety may increase the polarizability of the ligand by electrons withdrawing inductive effect resulting in the enhanced potency and interactions.

## 5. Conclusion

The molecular docking is now fully recognized and integrated in the research process. In the past the emergence of this new discipline had occasionally encountered some opposition here and there. At presents, the science is mature and there are a growing number of success stories that continuously expand the armory of drug research. Several considerations that can greatly improve the success and enrichment of true bioactive hit compounds are commonly overlooked at the initial stages of a molecular docking study. In this chapter, we tried to cover several of these considerations, including few examples, of molecular docking studies of natural and synthetic analogs of potent α-glucosidase inhibitors, β-glucuronidase inhibitors, and cytotoxicity from our own findings. These molecular studies were performed for different classes of bioactive compounds in order to understand the binding interaction of the active compounds. It was concluded that the nature, position as well as the number of substituents affects the inhibitory potential of these analogs.

## Acknowledgements

Sadia Sultan would like to acknowledge Universiti Teknologi MARA for the financial support under the reference number UiTM 600-IRMI/FRGS 5/3 (0119/2016), Ministry of Higher Education Malaysia. One of our author Gurmeet Kaur Surindar Singh would also like to acknowledge Universiti Teknologi MARA for the financial support under the reference number UiTM 600- IRMI/FRGS 5/3 (28/2015), Ministry of Higher Education Malaysia. We also would like to highlight and acknowledge our previous published research under reference 7, 18, and 32.

## Author details

Sadia Sultan1,2,3\*, Gurmeet Kaur Surindar Singh1,3, Kamran Ashraf1 and Muhammad Ashraf<sup>4</sup>

\*Address all correspondence to: sadiasultan301@yahoo.com; drsadia@puncakalam.uitm.edu.my

1 Faculty of Pharmacy, Universiti Teknologi MARA, Puncak Alam Campus, Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia

2 Atta-ur-Rahman Institute for Natural Products Discovery (AuRIns), Universiti Teknologi MARA, Puncak Alam Campus, Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia

3 Pharmaceutical and Life Sciences Core, Universiti Teknologi MARA, Shah Alam, Shah Alam, Selangor Darul Ehsan, Malaysia

4 Merck Pharmaceutical Private Limited, Karachi, Pakistan

## References

cell lines). The compound was observed making two interactions with active residues of the active site pocket of the enzyme. The oxygen atom of the morpholine moiety of the compound formed side chain acceptor interaction with the Lys 990 residue of the binding pocket. Arg 929 was observed making the hydrogen bond with the –NH group of the hydrazine moiety of the ligand as shown in the Figure 8. The electro-negative nature of Cl, O, and S of the substituent moiety may increase the polarizability of the ligand by electrons withdrawing inductive effect

Figure 8. Docking conformations of compound five in the active site of topoisomerase II enzyme.

The molecular docking is now fully recognized and integrated in the research process. In the past the emergence of this new discipline had occasionally encountered some opposition here and there. At presents, the science is mature and there are a growing number of success stories that continuously expand the armory of drug research. Several considerations that can greatly improve the success and enrichment of true bioactive hit compounds are commonly overlooked at the initial stages of a molecular docking study. In this chapter, we tried to cover several of these considerations, including few examples, of molecular docking studies of natural and synthetic analogs of potent α-glucosidase inhibitors, β-glucuronidase inhibitors, and cytotoxicity from our own findings. These molecular studies were performed for different classes of bioactive compounds in order to understand the binding interaction of the active compounds. It was concluded that the nature, position as well as the number of substituents

Sadia Sultan would like to acknowledge Universiti Teknologi MARA for the financial support under the reference number UiTM 600-IRMI/FRGS 5/3 (0119/2016), Ministry of Higher Education

resulting in the enhanced potency and interactions.

affects the inhibitory potential of these analogs.

Acknowledgements

5. Conclusion

28 Molecular Docking


[12] Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. Journal of Computational Chemistry. 2004;25:1605-1612

**Chapter 3**

**Provisional chapter**

**Molecular Docking for Detoxifying Enzyme Studies**

In this chapter, we pointed some relevant results obtained by protein-ligand docking simulations in the context of insecticide and herbicide resistance performed by glutathione S-transferases (GSTs), a detoxifying superfamily enzyme. We present here some in silico evidences of GST binding against chemical insecticides in the malaria and dengue vectors (*Anopheles gambiae* and *Aedes aegypti* mosquitoes) and against chemical herbicides used on rice (*Oryza sativa*) culture. Our findings suggest that some members from epsilon class (GSTE2, GSTE5) can metabolize some insecticide compounds and that a tau class member (GSTU4) can metabolize some herbicides. The results reinforce the importance of docking studies for enzyme activity comprehension. These information can allow in the future the implementation of new strategies for mosquito control and herbicide management on rice culture through biotechnological improvements designed to specific GST targets. Induced mutations on catalytic binding sites of GSTU4 could improve rice herbicide resistance and minimize produce damage, while rational compounds can be designed to inhibit GSTE members to decline insecticide resistance on mosquito control. In both cases, biotechnological tools could be developed focusing on GSTs that would

reduce environmental impact by the use of insecticide and herbicide.

enzymes, mosquito control, rice culture, bioinformatics

**Keywords:** GSTs, insecticide resistance, herbicide resistance, AutoDock, detoxifying

Mechanisms of resistance to chemical insecticides include the pathways of metabolization of toxic compounds, because of overexpression of detoxification enzymes or structural modifications in these enzymes. Glutathione S-transferases (GSTs) are one of the most important groups of enzymes involved in this type of resistance and comprise enzymes that catalyze

reactions that transform various xenobiotic compounds into soluble products [1].

**Molecular Docking for Detoxifying Enzyme Studies**

© 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 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.

DOI: 10.5772/intechopen.73920

Rafael Trindade Maia and Vinícius Costa Amador

Rafael Trindade Maia and Vinícius Costa Amador

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.73920

**Abstract**

**1. Introduction**


#### **Molecular Docking for Detoxifying Enzyme Studies Molecular Docking for Detoxifying Enzyme Studies**

DOI: 10.5772/intechopen.73920

Rafael Trindade Maia and Vinícius Costa Amador Rafael Trindade Maia and Vinícius Costa Amador

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.73920

#### **Abstract**

[12] Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE.

[14] Wenzl P, Wong L, Kwang-won K, Jefferson RA. Molecular Biology and Evolution. 2005;22:

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[17] Khan KM, Rahim F, Halim SA, Taha M, Khan M, Perveen S, Haq Z, Mesaik MA,

[18] Taha M, Sultan S, Nuzar HA, Imran S, Ismail NH, Rahim F, Ullah H. Bioorganic and

[19] Taha M, Ismail NH, Imran S, Ali M, Jamil W, Uddin N, Kashif SM. RSC Advances. 2016;6:

[22] Sawyers CL, Abate-Shen C, Anderson KC, et al. AACR cancer progress report 2013.

[23] Robert NJ, Diéras V, Glaspy J, et al. Journal of Clinical Oncology. 2011;29:1252-1260

[25] Sak A, Grehl S, Engelhard M, et al. Clinical Cancer Research. 2009;15:2927-2934

[27] Andrs M, Korabecny J, Jun D, et al. Journal of Medicinal Chemistry. 2015;58:41-71

[29] Arrieta A, Oteagui D, Zubia A, et al. The Journal of Organic Chemistry. 2007;72:4313-4322

[32] Taha M, Shah SAA, Afifi M, Sultan S, Ismail NH. Chinese Chemical Letters. 2017;28:607-611

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30 Molecular Docking

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[31] Kerkenaar A. Prous Science Publ., 1987;1:523-542. R.G.M.P

In this chapter, we pointed some relevant results obtained by protein-ligand docking simulations in the context of insecticide and herbicide resistance performed by glutathione S-transferases (GSTs), a detoxifying superfamily enzyme. We present here some in silico evidences of GST binding against chemical insecticides in the malaria and dengue vectors (*Anopheles gambiae* and *Aedes aegypti* mosquitoes) and against chemical herbicides used on rice (*Oryza sativa*) culture. Our findings suggest that some members from epsilon class (GSTE2, GSTE5) can metabolize some insecticide compounds and that a tau class member (GSTU4) can metabolize some herbicides. The results reinforce the importance of docking studies for enzyme activity comprehension. These information can allow in the future the implementation of new strategies for mosquito control and herbicide management on rice culture through biotechnological improvements designed to specific GST targets. Induced mutations on catalytic binding sites of GSTU4 could improve rice herbicide resistance and minimize produce damage, while rational compounds can be designed to inhibit GSTE members to decline insecticide resistance on mosquito control. In both cases, biotechnological tools could be developed focusing on GSTs that would reduce environmental impact by the use of insecticide and herbicide.

**Keywords:** GSTs, insecticide resistance, herbicide resistance, AutoDock, detoxifying enzymes, mosquito control, rice culture, bioinformatics

## **1. Introduction**

Mechanisms of resistance to chemical insecticides include the pathways of metabolization of toxic compounds, because of overexpression of detoxification enzymes or structural modifications in these enzymes. Glutathione S-transferases (GSTs) are one of the most important groups of enzymes involved in this type of resistance and comprise enzymes that catalyze reactions that transform various xenobiotic compounds into soluble products [1].

© 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 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.

In eukaryotic organisms, these enzymes are classified into cytosolic GSTs, microsomal GSTs (associated with membranes), and mitochondrial GSTs [2, 3]. In insects, only two of these classes were found: cytosolic and microsomal [4]. In the present study, we found no GST of the mitochondrial class in insects to date [5, 6]. Microsomal GSTs catalyze reactions very similar to cytosolic ones, with trimeric structure and being associated with plasma membranes, although they have different structures and origins than cytosolic one [7, 8]. However, cytosolic GSTs have already been identified as important for resistance to chemical insecticides [5, 9, 10], while microsomal GSTs have not yet been related to resistance to insecticides [5].

insecticide and herbicide resistance. Molecular docking is based on molecular recognition and often is referred as a "lock-and-key" problem. In general, the best-fit orientation is obtained by shape complementarity and a score function based on binding energy affinity. In proteinligand simulations, dockings generally are applied in a stochastic search algorithm to achieve the best binding complexes, and the energy can be estimated by molecular mechanic force

Molecular Docking for Detoxifying Enzyme Studies http://dx.doi.org/10.5772/intechopen.73920 33

The atomic coordinates of AgGSTE2 and AgGSTE5 were from their respective PDB files, as well as their ligand, the tripeptide glutathione, or GSH (C10 H17 N3 O6 S). The geometry of

An isoform of AgGSTE2 (AgGSTE2mut) with two mutations, I114T and F120L (isoleucine for threonine at residue 114, phenylalanine for leucine at amino acid 120) was also submitted to the simulations. The three proteins (AgGSTE2, AgGSTE2mut, and GSTE5) were simulated with and without the GSH linker. For the construction of the mutant (AgGSTE2mut), the nonmutant protein geometries (AgGSTE2) were used, and the residues in the PDB file were

The receptors used in the docking analyses were the crystallographic structure of AgGSTE2 and its mutant (AgGSTE2mut) and the structure of the model constructed for AgGSTE5. The ligands used were the insecticides DDT, carbaryl, cypermethrin, and malathion, being all these synthetic and commercially used organic insecticides normally used to control Culicidae vectors (**Table 1**). The atomic coordinates of the compounds were obtained from the ZINC data-

Molecular docking is a computational technique that aims to calculate atomic interactions between a small binding molecule and a macromolecule in search of the lower energy conformation. The AutoDock 4.2.2 program [18] was used to convert the files into PDB format for the form *pdbqt*, which is the file format used by AutoDock. The ligands were marked with *Gasteiger* load parameters and only the nonpolar hydrogens explicitly represented. The *Gasteiger* charge parameters provide charges properties of each atom, by the *SetPartialCharge* method, an algorithm that includes partial charges. In this algorithm, it

**Singlet Name Access number** DDT Dichlorodiphenyltrichloroethane ZINC01530011 Carbaryl 1-Naphthyl methylcarbamate ZINC00001090 Cypermethrin Cypermethrin ZINC71789490 Malathion Malathion ZINC1530800

**Table 1.** Compounds used as ligands for the calculation of docking.

**2. Molecular docking between mosquitoes' GSTs and chemical** 

the ligand was obtained from the PDB database.

replaced manually in the two subunits.

base (http://zinc.docking.org/).

fields.

**insecticides**

In insects, cytosolic GSTs are represented, at least, by six classes: delta, epsilon, omega, sigma, theta, and zeta [5, 11, 12]. In the present study, it was found that these genes were found to be similar to those of other species, such as the *A. gambiae* malaria vector and the fruit fly *Drosophila melanogaster* [11]. The delta and epsilon classes are arthropod-specific and represent more than 65% of the total cytosolic GSTs found in these organisms [11]. Most GSTs found in insects and involved in the target (omega, sigma, theta, and zeta) have a much broader distribution between taxonomic groups, from bacteria to vertebrates [13, 14].

Members of delta, sigma, and epsilon classes were initially called class I, II, and III, respectively, and later, with the increase in the number of sequences deposited in databases and classification studies, the nomenclature was adopted based on the Greek alphabet in agreement with the system of nomenclature of GSTs of mammalians [15].

This classification was supported by phylogenetic analyses in both mammalian and insect GSTs [4, 13]. Currently the nomenclature of insect GSTs consists of three parts: the name of the species of which GST belongs, the specific class of GST, and the number that specifies the order in which the routine was discovered. In this way, the name AgGSTD1 is used to designate a GST of *A. gambiae*, member of delta class, being the first protein of this class to be discovered [12].

Cytosolic GSTs are composed of two subunits of approximately 25 kDa each, which may be homodimeric or heterodimeric. Each subunit has a specific glutathione binding site (G-site), near an electrophilic site (H-site). The G-site is located at the N-terminus of the protein and is a highly conserved region in the GSTs. However, the H-site residues that interact with the hydrophobic substrates are found at the C-terminus. The H-site diversity causes the GSTs to present different specificities in relation to the substrates they metabolize [16, 17]. The GST-catalyzed reaction consists of promoting the conjugation of the reduced glutathione tripeptide (GSH) to a specific and generally cytotoxic compound which, upon binding to such electrophilic grouping, will pass from the reduced state to the oxidized state and form a more soluble compound and easier to excrete from the cell. This phase of conjugation represents phase II of the cellular detoxification process, and the GSTs represent the most important enzymes of this phase, although others are involved. The GST enzymes display a big variety of substrate catalytic reactions. As multispecific and promiscuous proteins, the GSTs represent potential targets of inhibitors selection and design. In *Aedes aegypti*, hematin binds to GSTs resulting in activity inhibition [18].

Molecular docking is a computational technique that aims to predict the best orientation between two molecules. Usually, one of the compounds is small compound that is bounded to a macromolecule (protein). This powerful approach is an excellent tool that helps to understand relevant physiological processes in a wide range of organisms and systems, such as insecticide and herbicide resistance. Molecular docking is based on molecular recognition and often is referred as a "lock-and-key" problem. In general, the best-fit orientation is obtained by shape complementarity and a score function based on binding energy affinity. In proteinligand simulations, dockings generally are applied in a stochastic search algorithm to achieve the best binding complexes, and the energy can be estimated by molecular mechanic force fields.

## **2. Molecular docking between mosquitoes' GSTs and chemical insecticides**

In eukaryotic organisms, these enzymes are classified into cytosolic GSTs, microsomal GSTs (associated with membranes), and mitochondrial GSTs [2, 3]. In insects, only two of these classes were found: cytosolic and microsomal [4]. In the present study, we found no GST of the mitochondrial class in insects to date [5, 6]. Microsomal GSTs catalyze reactions very similar to cytosolic ones, with trimeric structure and being associated with plasma membranes, although they have different structures and origins than cytosolic one [7, 8]. However, cytosolic GSTs have already been identified as important for resistance to chemical insecticides [5, 9, 10], while microsomal GSTs have not yet been related to resistance to insecticides [5].

In insects, cytosolic GSTs are represented, at least, by six classes: delta, epsilon, omega, sigma, theta, and zeta [5, 11, 12]. In the present study, it was found that these genes were found to be similar to those of other species, such as the *A. gambiae* malaria vector and the fruit fly *Drosophila melanogaster* [11]. The delta and epsilon classes are arthropod-specific and represent more than 65% of the total cytosolic GSTs found in these organisms [11]. Most GSTs found in insects and involved in the target (omega, sigma, theta, and zeta) have a much broader distri-

Members of delta, sigma, and epsilon classes were initially called class I, II, and III, respectively, and later, with the increase in the number of sequences deposited in databases and classification studies, the nomenclature was adopted based on the Greek alphabet in agree-

This classification was supported by phylogenetic analyses in both mammalian and insect GSTs [4, 13]. Currently the nomenclature of insect GSTs consists of three parts: the name of the species of which GST belongs, the specific class of GST, and the number that specifies the order in which the routine was discovered. In this way, the name AgGSTD1 is used to designate a GST of *A. gambiae*, member of delta class, being the first protein of this class to be discovered [12].

Cytosolic GSTs are composed of two subunits of approximately 25 kDa each, which may be homodimeric or heterodimeric. Each subunit has a specific glutathione binding site (G-site), near an electrophilic site (H-site). The G-site is located at the N-terminus of the protein and is a highly conserved region in the GSTs. However, the H-site residues that interact with the hydrophobic substrates are found at the C-terminus. The H-site diversity causes the GSTs to present different specificities in relation to the substrates they metabolize [16, 17]. The GST-catalyzed reaction consists of promoting the conjugation of the reduced glutathione tripeptide (GSH) to a specific and generally cytotoxic compound which, upon binding to such electrophilic grouping, will pass from the reduced state to the oxidized state and form a more soluble compound and easier to excrete from the cell. This phase of conjugation represents phase II of the cellular detoxification process, and the GSTs represent the most important enzymes of this phase, although others are involved. The GST enzymes display a big variety of substrate catalytic reactions. As multispecific and promiscuous proteins, the GSTs represent potential targets of inhibitors selection and design. In *Aedes aegypti*, hematin binds to GSTs resulting in activity inhibition [18].

Molecular docking is a computational technique that aims to predict the best orientation between two molecules. Usually, one of the compounds is small compound that is bounded to a macromolecule (protein). This powerful approach is an excellent tool that helps to understand relevant physiological processes in a wide range of organisms and systems, such as

bution between taxonomic groups, from bacteria to vertebrates [13, 14].

32 Molecular Docking

ment with the system of nomenclature of GSTs of mammalians [15].

The atomic coordinates of AgGSTE2 and AgGSTE5 were from their respective PDB files, as well as their ligand, the tripeptide glutathione, or GSH (C10 H17 N3 O6 S). The geometry of the ligand was obtained from the PDB database.

An isoform of AgGSTE2 (AgGSTE2mut) with two mutations, I114T and F120L (isoleucine for threonine at residue 114, phenylalanine for leucine at amino acid 120) was also submitted to the simulations. The three proteins (AgGSTE2, AgGSTE2mut, and GSTE5) were simulated with and without the GSH linker. For the construction of the mutant (AgGSTE2mut), the nonmutant protein geometries (AgGSTE2) were used, and the residues in the PDB file were replaced manually in the two subunits.

The receptors used in the docking analyses were the crystallographic structure of AgGSTE2 and its mutant (AgGSTE2mut) and the structure of the model constructed for AgGSTE5. The ligands used were the insecticides DDT, carbaryl, cypermethrin, and malathion, being all these synthetic and commercially used organic insecticides normally used to control Culicidae vectors (**Table 1**). The atomic coordinates of the compounds were obtained from the ZINC database (http://zinc.docking.org/).

Molecular docking is a computational technique that aims to calculate atomic interactions between a small binding molecule and a macromolecule in search of the lower energy conformation. The AutoDock 4.2.2 program [18] was used to convert the files into PDB format for the form *pdbqt*, which is the file format used by AutoDock. The ligands were marked with *Gasteiger* load parameters and only the nonpolar hydrogens explicitly represented. The *Gasteiger* charge parameters provide charges properties of each atom, by the *SetPartialCharge* method, an algorithm that includes partial charges. In this algorithm, it


**Table 1.** Compounds used as ligands for the calculation of docking.

is admitted that all hydrogens are explicitly represented and based on electronegativity equilibration. The *Kollman* set parameters were used to assign the receptor molecules. This force field uses values for each amino acid that was derived from the corresponding electrostatic potential. The simulations were performed with the Lamarckian genetic algorithm (LGA). The box was set in the 126×126×126 dimensions centered on the ligand and the active site, and the LGA was subjected to calculations of 10,000 replicates with populations of 150 individuals to a maximum of 27,000 generations and crossover mutation rates of 0.02 and 0.08, respectively.

The binding energies between the three proteins and the five different compounds studied were calculated and are available in **Table 2**. The lower energy conformations of each complex were visually analyzed (VMD, visual molecule dynamics) and was listed all residues in radius of 4.0 Å of the ligand (**Figures 1**–**4**).

The lowest energy was observed in the AgGSTE2muT-DDT complex, indicating a greater affinity between this enzyme and this insecticide. The observed distance between DDT and GSH (<4 Å) and position shows that this conformer is a potential candidate to metabolize DDT. The binding energy of this complex was the smallest among all comparisons. In the docking with the DDT, we observed a few higher energies for AgGSTE2 and AgGSTE5 when compared with the AgGSTE2mut values, but the values in both were negative. The distances between DDT and GSH in these conformers shows a value which allows for interactions, with AgGSTE5 being the shortest distance (2.91 Å) observed in complexes simulated with DDT. In all three enzymes, an approximation was observed between the trichloromethyl group of DDT and GSH, evidencing the ability of these enzymes to bind to this insecticide.

For carbaryl, the enzyme with the lowest binding energy was AgGSTE5, followed by AgGSTE2mut and AgGSTE2. However, it was the AgGSTE2mut that showed the conformation with the smallest distance between the ligands. The proximity of carbaryl to glutathione suggests that the three systems can form GSH conjugated with this insecticide.

In simulated complexes with cypermethrin that were observed, the lowest energy values were used, except for the AgGSTE2mut whose lowest energy score was for the DDT simulation. In the conformations of AgGSTE2 and AgGSTE2mut, the binding distances between cypermethrin and GSH were 3.39 and 2.74 Å, respectively, showing a potential of these enzymes to metabolize cypermethrin. In AgGSTE5, the distance between the ligands was 4.81 Å, indicating that although the enzyme has insecticide-binding affinity, the likelihood of the glutathione conjugation reaction is low.

Malathion, despite having demonstrated negative values when complexed with enzymes, was the compound that showed the highest energy values for all three systems. In addition, no


**Figure 1.** Representation of the best conformation of the AgGSTE2-carbaryl (top), AgGSTE2mut-carbaril (middle), and AgGSTe5-carbaryl (bottom) complexes. Residues are represented in rods and spheres. The GSH is represented in sticks

Molecular Docking for Detoxifying Enzyme Studies http://dx.doi.org/10.5772/intechopen.73920 35

(purple). In green the carbaryl.

**Table 2.** Binding energies (kcal/mol) for the best conformations of each complex.

Molecular Docking for Detoxifying Enzyme Studies http://dx.doi.org/10.5772/intechopen.73920 35

is admitted that all hydrogens are explicitly represented and based on electronegativity equilibration. The *Kollman* set parameters were used to assign the receptor molecules. This force field uses values for each amino acid that was derived from the corresponding electrostatic potential. The simulations were performed with the Lamarckian genetic algorithm (LGA). The box was set in the 126×126×126 dimensions centered on the ligand and the active site, and the LGA was subjected to calculations of 10,000 replicates with populations of 150 individuals to a maximum of 27,000 generations and crossover mutation rates of 0.02

The binding energies between the three proteins and the five different compounds studied were calculated and are available in **Table 2**. The lower energy conformations of each complex were visually analyzed (VMD, visual molecule dynamics) and was listed all residues in

The lowest energy was observed in the AgGSTE2muT-DDT complex, indicating a greater affinity between this enzyme and this insecticide. The observed distance between DDT and GSH (<4 Å) and position shows that this conformer is a potential candidate to metabolize DDT. The binding energy of this complex was the smallest among all comparisons. In the docking with the DDT, we observed a few higher energies for AgGSTE2 and AgGSTE5 when compared with the AgGSTE2mut values, but the values in both were negative. The distances between DDT and GSH in these conformers shows a value which allows for interactions, with AgGSTE5 being the shortest distance (2.91 Å) observed in complexes simulated with DDT. In all three enzymes, an approximation was observed between the trichloromethyl group of

For carbaryl, the enzyme with the lowest binding energy was AgGSTE5, followed by AgGSTE2mut and AgGSTE2. However, it was the AgGSTE2mut that showed the conformation with the smallest distance between the ligands. The proximity of carbaryl to glutathione

In simulated complexes with cypermethrin that were observed, the lowest energy values were used, except for the AgGSTE2mut whose lowest energy score was for the DDT simulation. In the conformations of AgGSTE2 and AgGSTE2mut, the binding distances between cypermethrin and GSH were 3.39 and 2.74 Å, respectively, showing a potential of these enzymes to metabolize cypermethrin. In AgGSTE5, the distance between the ligands was 4.81 Å, indicating that although the enzyme has insecticide-binding affinity, the likelihood of the glutathi-

Malathion, despite having demonstrated negative values when complexed with enzymes, was the compound that showed the highest energy values for all three systems. In addition, no

**DDT Carbaryl Cypermethrin Malathion**

DDT and GSH, evidencing the ability of these enzymes to bind to this insecticide.

suggests that the three systems can form GSH conjugated with this insecticide.

AgGSTE2 −5.13 −5.85 −8.37 −3.37 AgGSTE2mut −9.16 −6.09 −8.81 −3.67 AgGSTE5 −7.68 −6.42 −8.64 −3.24

**Table 2.** Binding energies (kcal/mol) for the best conformations of each complex.

and 0.08, respectively.

34 Molecular Docking

radius of 4.0 Å of the ligand (**Figures 1**–**4**).

one conjugation reaction is low.

**Figure 1.** Representation of the best conformation of the AgGSTE2-carbaryl (top), AgGSTE2mut-carbaril (middle), and AgGSTe5-carbaryl (bottom) complexes. Residues are represented in rods and spheres. The GSH is represented in sticks (purple). In green the carbaryl.

**Figure 2.** Representation of the best conformation of the AgGSTE2-cypermethrin (top), AgGSTE2mut-cypermethrin (middle), and AgGSTe5-cypermethrin (bottom) complexes. Residues are represented in rods and spheres. The GSH is represented in sticks. In green the cypermethrin.

**Figure 3.** Representation of the best conformation of the AgGSTE2-DDT (top), AgGSTE2mut-DDT (middle), and AgGSTe5-DDT (bottom) complexes. Residues are represented in rods and spheres. The GSH is represented in sticks. In

Molecular Docking for Detoxifying Enzyme Studies http://dx.doi.org/10.5772/intechopen.73920 37

green the cypermethrin.

**Figure 3.** Representation of the best conformation of the AgGSTE2-DDT (top), AgGSTE2mut-DDT (middle), and AgGSTe5-DDT (bottom) complexes. Residues are represented in rods and spheres. The GSH is represented in sticks. In green the cypermethrin.

**Figure 2.** Representation of the best conformation of the AgGSTE2-cypermethrin (top), AgGSTE2mut-cypermethrin (middle), and AgGSTe5-cypermethrin (bottom) complexes. Residues are represented in rods and spheres. The GSH is

represented in sticks. In green the cypermethrin.

36 Molecular Docking

reasonable proximity of GSH (AgGSTE2 = 8.10 Å; AgGSTE2mut = 9.57 Å; AgGSTE5 = 5.26 Å) was observed in any of the conformers, which rule out the possibility that one of these

Molecular Docking for Detoxifying Enzyme Studies http://dx.doi.org/10.5772/intechopen.73920 39

The docking results showed that the three enzymes have affinity for compounds of different nature. In fact, this represents an in silico that these enzymes show a remarkable functional promiscuity, resulting from a multi-specificity to the substrate. Although the AgGSTE2mut presented the lowest values for five of the seven compounds submitted to the docking calculation, the values did not differ much. When comparing the two isoforms, it was observed that for six of the seven compounds tested, the mutant enzyme had slightly more favorable energies than the wild type. The most plausible explanation for this result lies in the fact that AgGSTE2mut has a higher catalytic site resulting from the mutations in this enzyme, which

The docking results showed that the three enzymes have affinity for compounds of different nature. In fact, this represents in silico evidence that these enzymes show remarkable functional promiscuity, resulting from multi-specificity to the substrate. Although the AgGSTE2mut presented the lowest values for five of the seven compounds submitted to the docking calculation, the values did not differ much. When comparing the two isoforms, it was observed that for six of the seven compounds tested, the mutant enzyme had slightly more favorable energies than the wild type. The most plausible explanation for this result is that AgGSTE2mut has a larger catalytic site volume, resulting from the mutations in this enzyme, which probably allows a

The multi-specificity presented by these enzymes, especially AgGSTE2mut, may represent an important aspect in the ability of *A. gambiae* to have populations resistant to chemical insecticides. This is a recent concept [19] and should be taken into account in future studies of the molecular evolution of enzyme superfamily. The use of chemical insecticides in this species needs to be rethought and reevaluated as a mode of control. A future perspective may be on the potential of development of specific inhibitors for these enzymes, in an attempt to decrease the response to the insecticides used, especially DDT. Another aspect that evidences the potential of the epsilon class GSTs as targets for inhibition is the fact that this class of enzymes is specific to arthropods, which enables the further development of inhibitory compounds that do not affect other species, such as mammals. Understanding the mechanisms of evolution and adaptation of these enzymes and details of their dynamics and functioning is indispensable when planning a rational and integrated control of a vector species. Another possible application is to use these enzymes as indicators of resistant populations and refractory to various insecticides and thus to choose the best type of compound to be used for each

**3. Molecular docking between a rice GST and chemical herbicides**

It is known that the superfamily of glutathione S-transferases (GSTs) gives rice (*Oryza sativa*) a catalytic action, protection against biotic and abiotic stress [20, 21]. The inactivation of the toxic effects of herbicides on plants has different defense systems [22]. Another study [23] has shown that the GST enzyme is associated with several crop herbicides' harmful effect tolerance,

enzymes could metabolize the malathion.

better accommodation of the compounds.

population.

probably allows a better accommodation of the compounds.

**Figure 4.** Representation of the best conformation of the AgGSTE2-malathion (top), AgGSTE2mut-malathion (middle), and AgGSTe5-malathion (bottom) complexes. Residues are represented in rods and spheres. The GSH is represented in sticks. In green the cypermethrin.

reasonable proximity of GSH (AgGSTE2 = 8.10 Å; AgGSTE2mut = 9.57 Å; AgGSTE5 = 5.26 Å) was observed in any of the conformers, which rule out the possibility that one of these enzymes could metabolize the malathion.

The docking results showed that the three enzymes have affinity for compounds of different nature. In fact, this represents an in silico that these enzymes show a remarkable functional promiscuity, resulting from a multi-specificity to the substrate. Although the AgGSTE2mut presented the lowest values for five of the seven compounds submitted to the docking calculation, the values did not differ much. When comparing the two isoforms, it was observed that for six of the seven compounds tested, the mutant enzyme had slightly more favorable energies than the wild type. The most plausible explanation for this result lies in the fact that AgGSTE2mut has a higher catalytic site resulting from the mutations in this enzyme, which probably allows a better accommodation of the compounds.

The docking results showed that the three enzymes have affinity for compounds of different nature. In fact, this represents in silico evidence that these enzymes show remarkable functional promiscuity, resulting from multi-specificity to the substrate. Although the AgGSTE2mut presented the lowest values for five of the seven compounds submitted to the docking calculation, the values did not differ much. When comparing the two isoforms, it was observed that for six of the seven compounds tested, the mutant enzyme had slightly more favorable energies than the wild type. The most plausible explanation for this result is that AgGSTE2mut has a larger catalytic site volume, resulting from the mutations in this enzyme, which probably allows a better accommodation of the compounds.

The multi-specificity presented by these enzymes, especially AgGSTE2mut, may represent an important aspect in the ability of *A. gambiae* to have populations resistant to chemical insecticides. This is a recent concept [19] and should be taken into account in future studies of the molecular evolution of enzyme superfamily. The use of chemical insecticides in this species needs to be rethought and reevaluated as a mode of control. A future perspective may be on the potential of development of specific inhibitors for these enzymes, in an attempt to decrease the response to the insecticides used, especially DDT. Another aspect that evidences the potential of the epsilon class GSTs as targets for inhibition is the fact that this class of enzymes is specific to arthropods, which enables the further development of inhibitory compounds that do not affect other species, such as mammals. Understanding the mechanisms of evolution and adaptation of these enzymes and details of their dynamics and functioning is indispensable when planning a rational and integrated control of a vector species. Another possible application is to use these enzymes as indicators of resistant populations and refractory to various insecticides and thus to choose the best type of compound to be used for each population.

## **3. Molecular docking between a rice GST and chemical herbicides**

**Figure 4.** Representation of the best conformation of the AgGSTE2-malathion (top), AgGSTE2mut-malathion (middle), and AgGSTe5-malathion (bottom) complexes. Residues are represented in rods and spheres. The GSH is represented in

sticks. In green the cypermethrin.

38 Molecular Docking

It is known that the superfamily of glutathione S-transferases (GSTs) gives rice (*Oryza sativa*) a catalytic action, protection against biotic and abiotic stress [20, 21]. The inactivation of the toxic effects of herbicides on plants has different defense systems [22]. Another study [23] has shown that the GST enzyme is associated with several crop herbicides' harmful effect tolerance, promoting the resistance of grasses to its chemicals substances. In plants GST is also responsible through the metabolism of a huge name of commercial important herbicides [24] reducing damage that could occur through the toxically herbicides' action [25]. The reaction consists of the conjugation of the tripeptide glutathione to a hydrophobic compound, making it more soluble and less toxic [26], maintaining the cellular homeostasis. For this study two herbicides were selected, metsulfuron and bentazon sodium.

The herbicide metsulfuron-methyl belongs to the group of sulfonylureas and acts on the enzyme acetolactate synthase (ALS), consequently inhibiting the synthesis of the amino acids leucine, valine, and isoleucine, interfering in the protein synthesis and inducing the death of the plant by interfering in the cellular division. Among its properties, it is reported that metsulfuron-methyl has a systemic action and is rapidly absorbed by the whole plant, besides presenting selectivity to the crops for which its use is recommended. In susceptible plants, the absorption of this herbicide results initially in growth stoppage; due to the rapid translocation of this group of molecules to the meristems, apices, and later, death is inevitable, considering the impossibility of the essential amino acid biosynthesis to the plant. This mechanism inhibition of ALS was elucidated due to works done and published [27, 28].

Bentazon is a herbicide from the benzothiazinone class, which, after being absorbed, interferes in the photosynthesis process and is therefore a photosystem II photosynthesis inhibitor, affecting the carbohydrate synthesis in leaf areas that have received treatment, occasionally and may occasionally lead the plants to death, especially when they are in the early development stage. The photosynthesis inhibitors mechanism action is the removal or the inactivation of intermediary charge carriers from the electron transport process, and are considered to be inhibitors of electron transport [28]. The inhibitory mechanism of photosynthesis results in the blockade of the electron transport of the compound QB component of the photosynthetic system and, thus, makes impossible the occurrence of electron transport to plastoquinone B [29]. The aforementioned blockade occurs through the binding of the herbicides to the active site of QB in the D1 protein belonging to photosystem II, located on the membranes of the thylakoids of the chloroplasts. This process interrupts the fixation of CO<sup>2</sup> and interferes in the production of essential elements to the plant growth, such as ATP and NADPH2 ; however, plant death usually occurs due to other factors. The interruption of the electron flow in photosystem II promotes a significant increase in the energy status of the chlorophyll, resulting in a state called "triplet," which causes an energy overload derived from the attenuation effect of the carotenoid pigments, and this characterizes the peroxidation process. In other study [30], lipid peroxidation due to excess triplet chlorophyll may occur through two mechanisms: direct formation of lipid radicals in unsaturated molecules of fatty acids constituting membranes and production of singlet oxygen through the reaction of chlorophyll triplet with oxygen. In both cases, the peroxidation process will corroborate with damage to cell membranes.

The *.mol2* files were converted to *.pdbqt* in AutoDock 1.5.6 (https://www.chpc.utah.edu/ documentation/software/autodock.php) and had the polar hydrogens removed, and their molecules were flagged with the Gasteiger parameters [31]. The structure of OsGSTU4 was obtained from a *.pdb* file modeled using homology which was converted to *.pdbqt* file in AutoDock and added hydrogens and Kollman load parameters [32, 33]. For this step, glutathione was treated as a cofactor. The docking calculations were run in AutoDock 1.5.6 program, and the simulations were performed using the Lamarckian genetic algorithm (LGA). In this work, the LGA was used in conjunction with the Goodford method, allowing simultaneous sampling of the ligand configurational space and calculating the receptor and ligand atomic interaction energy [34, 35]. The grid parameters are established in 126×126×126 Å by the program Autogrid (http://autodock.scripps.edu/wiki/AutoGrid) and receiver-centered (GST). The parameters used for simulations were as follows: 10,000 replicates, energy analyzes per 1,500,000 and 27,000 generations, population size of 150, and mutation rates and crossing over of 0.02 and 0.08, respectively. Ten conformations were generated that were ranked based on the lowest energy and analyzed in the VMD (http://www.ks.uiuc.edu/Research/vmd/).

**Herbicide name Molecular formula 2D structure Access code** Bentazon-sodium C10H12N2O3S ZINC05442053

Molecular Docking for Detoxifying Enzyme Studies http://dx.doi.org/10.5772/intechopen.73920 41

Metsulfuron-methyl C14H15N5O6S ZINC01532069

The docking result for the herbicide metsulfuron-methyl, performed in the AutoDock program, ranked ten possible complexes; **Table 4** shows the best possible complex. This procedure is based on intermolecular energy, binding energy, and hydrogen bond scores, showing the atoms (and residues) of the protein and the ligand that present favorable interactions for the model.

**4. Results**

Source: ZINC database (http://zinc.docking.org/).

**Table 3.** Compounds used as ligands for the calculation of docking.

#### **3.1. Molecular docking of rice GST and herbicides**

The atomic coordinates of the compounds were obtained from the ZINC database (http://zinc. docking.org/) on *.mol2* file extension (**Table 3**).

**Table 3.** Compounds used as ligands for the calculation of docking.

The *.mol2* files were converted to *.pdbqt* in AutoDock 1.5.6 (https://www.chpc.utah.edu/ documentation/software/autodock.php) and had the polar hydrogens removed, and their molecules were flagged with the Gasteiger parameters [31]. The structure of OsGSTU4 was obtained from a *.pdb* file modeled using homology which was converted to *.pdbqt* file in AutoDock and added hydrogens and Kollman load parameters [32, 33]. For this step, glutathione was treated as a cofactor. The docking calculations were run in AutoDock 1.5.6 program, and the simulations were performed using the Lamarckian genetic algorithm (LGA). In this work, the LGA was used in conjunction with the Goodford method, allowing simultaneous sampling of the ligand configurational space and calculating the receptor and ligand atomic interaction energy [34, 35]. The grid parameters are established in 126×126×126 Å by the program Autogrid (http://autodock.scripps.edu/wiki/AutoGrid) and receiver-centered (GST). The parameters used for simulations were as follows: 10,000 replicates, energy analyzes per 1,500,000 and 27,000 generations, population size of 150, and mutation rates and crossing over of 0.02 and 0.08, respectively. Ten conformations were generated that were ranked based on the lowest energy and analyzed in the VMD (http://www.ks.uiuc.edu/Research/vmd/).

## **4. Results**

promoting the resistance of grasses to its chemicals substances. In plants GST is also responsible through the metabolism of a huge name of commercial important herbicides [24] reducing damage that could occur through the toxically herbicides' action [25]. The reaction consists of the conjugation of the tripeptide glutathione to a hydrophobic compound, making it more soluble and less toxic [26], maintaining the cellular homeostasis. For this study two herbicides

The herbicide metsulfuron-methyl belongs to the group of sulfonylureas and acts on the enzyme acetolactate synthase (ALS), consequently inhibiting the synthesis of the amino acids leucine, valine, and isoleucine, interfering in the protein synthesis and inducing the death of the plant by interfering in the cellular division. Among its properties, it is reported that metsulfuron-methyl has a systemic action and is rapidly absorbed by the whole plant, besides presenting selectivity to the crops for which its use is recommended. In susceptible plants, the absorption of this herbicide results initially in growth stoppage; due to the rapid translocation of this group of molecules to the meristems, apices, and later, death is inevitable, considering the impossibility of the essential amino acid biosynthesis to the plant. This mechanism inhibi-

Bentazon is a herbicide from the benzothiazinone class, which, after being absorbed, interferes in the photosynthesis process and is therefore a photosystem II photosynthesis inhibitor, affecting the carbohydrate synthesis in leaf areas that have received treatment, occasionally and may occasionally lead the plants to death, especially when they are in the early development stage. The photosynthesis inhibitors mechanism action is the removal or the inactivation of intermediary charge carriers from the electron transport process, and are considered to be inhibitors of electron transport [28]. The inhibitory mechanism of photosynthesis results in the blockade of the electron transport of the compound QB component of the photosynthetic system and, thus, makes impossible the occurrence of electron transport to plastoquinone B [29]. The aforementioned blockade occurs through the binding of the herbicides to the active site of QB in the D1 protein belonging to photosystem II, located on the membranes of the

and interferes in the

; however,

were selected, metsulfuron and bentazon sodium.

40 Molecular Docking

tion of ALS was elucidated due to works done and published [27, 28].

thylakoids of the chloroplasts. This process interrupts the fixation of CO<sup>2</sup>

**3.1. Molecular docking of rice GST and herbicides**

docking.org/) on *.mol2* file extension (**Table 3**).

production of essential elements to the plant growth, such as ATP and NADPH2

plant death usually occurs due to other factors. The interruption of the electron flow in photosystem II promotes a significant increase in the energy status of the chlorophyll, resulting in a state called "triplet," which causes an energy overload derived from the attenuation effect of the carotenoid pigments, and this characterizes the peroxidation process. In other study [30], lipid peroxidation due to excess triplet chlorophyll may occur through two mechanisms: direct formation of lipid radicals in unsaturated molecules of fatty acids constituting membranes and production of singlet oxygen through the reaction of chlorophyll triplet with oxygen. In both cases, the peroxidation process will corroborate with damage to cell membranes.

The atomic coordinates of the compounds were obtained from the ZINC database (http://zinc.

The docking result for the herbicide metsulfuron-methyl, performed in the AutoDock program, ranked ten possible complexes; **Table 4** shows the best possible complex. This procedure is based on intermolecular energy, binding energy, and hydrogen bond scores, showing the atoms (and residues) of the protein and the ligand that present favorable interactions for the model.


**Table 4.** Results of AutoDock-ranked complexes in the metsulfuron-methyl docking.

In metsulfuron-methyl, binding energies were lower than those of bentazon. The results revealed by the metsulfuron-methyl docking show that some residuals (LYS 111, LYS 56, GTX1226) were extremely favorable, being these possibly anchor residues for the binding, in combination with results evidenced by previous studies. The identification of the GTX1226 molecule as an anchor residue (**Table 5**) is evidence of a possible conjugation process [36] between metsulfuron-methyl and glutathione, evidencing the possibility of detoxification of metsulfuron-methyl by OsGSTU4. The best complex result ranked by the AutoDock for metsulfuron-methyl can be visualized in **Figure 5**. The image shows a zoom in a pocket where probably conjugation occurs by a hydrogen bond between bentazon and glutathione. The complex generated suggests that the OsGSTU4 displays a relevant role on the resistance for this herbicide (**Figure 5**).

The result of the docking performed for the herbicide bentazon sodium, also executed in the AutoDock program, is presented in **Table 6**. This procedure is the same used for metsulfuron-methyl and is also based on intermolecular energy, binding energy, and hydrogen bond scores, showing the atoms (and residues) of the protein and the ligand that present favorable interactions for the mode (**Figure 6**).

The results of **Table 6** also show the identified repeated residue (GLN 75) that presents the lowest binding energy, possibly showing as an anchor residue for the herbicide bentazon sodium, corroborating with the results obtained on previous studies [37].


**Table 5.** Representation of the atoms of near residues belonging to metsulfuron-methyl, atoms used as corresponding in the ligand and their respective distances in angstroms in the output.

**Figure 6.** Deep view of catalytic site. In red, the chain A; in blue, the chain B. In green, the bentazon. Glutathione (purple)

**Figure 5.** Deep view of catalytic site. In red, the chain A; in blue, the chain B. In green, the metsulfuron. Glutathione

Molecular Docking for Detoxifying Enzyme Studies http://dx.doi.org/10.5772/intechopen.73920 43

(purple) and residues (white) from H-binding-site, an interchain region. Source: Research data.

**Table 6.** Results of AutoDock-ranked complexes in the bentazon sodium docking.

Source: Research data.

**Binding energy (kcal/mol) Intermolecular energy (kcal/mol) Hydrogen bond −0.86** −1.16 B: GLN 75 HE21-O3

and residues (white) from H-binding-site, an interchain region. Source: Research data.

**Figure 5.** Deep view of catalytic site. In red, the chain A; in blue, the chain B. In green, the metsulfuron. Glutathione (purple) and residues (white) from H-binding-site, an interchain region. Source: Research data.


**Table 6.** Results of AutoDock-ranked complexes in the bentazon sodium docking.

In metsulfuron-methyl, binding energies were lower than those of bentazon. The results revealed by the metsulfuron-methyl docking show that some residuals (LYS 111, LYS 56, GTX1226) were extremely favorable, being these possibly anchor residues for the binding, in combination with results evidenced by previous studies. The identification of the GTX1226 molecule as an anchor residue (**Table 5**) is evidence of a possible conjugation process [36] between metsulfuron-methyl and glutathione, evidencing the possibility of detoxification of metsulfuron-methyl by OsGSTU4. The best complex result ranked by the AutoDock for metsulfuron-methyl can be visualized in **Figure 5**. The image shows a zoom in a pocket where probably conjugation occurs by a hydrogen bond between bentazon and glutathione. The complex generated suggests that the OsGSTU4 displays a relevant role on the resistance for

C: GTX1226 H11-N3 B: LYS 111 HZ2-O6 C: LYS 56 HZ1-O2

**Binding energy (kcal/mol) Intermolecular energy (kcal/mol) Hydrogen bond −3.74** −5.53 B: LYS 111 HZ1-O2

**Table 4.** Results of AutoDock-ranked complexes in the metsulfuron-methyl docking.

The result of the docking performed for the herbicide bentazon sodium, also executed in the AutoDock program, is presented in **Table 6**. This procedure is the same used for metsulfuron-methyl and is also based on intermolecular energy, binding energy, and hydrogen bond scores, showing the atoms (and residues) of the protein and the ligand that present favorable

The results of **Table 6** also show the identified repeated residue (GLN 75) that presents the lowest binding energy, possibly showing as an anchor residue for the herbicide bentazon

**Table 5.** Representation of the atoms of near residues belonging to metsulfuron-methyl, atoms used as corresponding in

sodium, corroborating with the results obtained on previous studies [37].

ASP110: O <0>0:C14 3.43 GLU69:OE2 <0>0:C5 2.95 LYS56:HZ1 <0>0:O2 1.91 LYS111:HZ1 <0>0:O2 1.91 GLN134:OE <0>0:C10 2.87 HIS54:HE2 <0>0:C5 3.91

the ligand and their respective distances in angstroms in the output.

**Near residue atoms Reference atoms (ligand) Respective distance (Å)**

this herbicide (**Figure 5**).

**Source:** Research data.

Source: Research data.

42 Molecular Docking

interactions for the mode (**Figure 6**).

**Figure 6.** Deep view of catalytic site. In red, the chain A; in blue, the chain B. In green, the bentazon. Glutathione (purple) and residues (white) from H-binding-site, an interchain region. Source: Research data.


Since herbicide and insecticide resistance is one of the major constraints of agriculture and mosquito control, the information from this study may be extremely useful for the development of specific inhibitors for these GSTs, thereby reducing the amount of herbicides and insecticides

Molecular Docking for Detoxifying Enzyme Studies http://dx.doi.org/10.5772/intechopen.73920 45

New strategies of control can be applied too. The results point these enzymes as very promi-

The molecular docking is a powerful approach for understanding the interactions of molecules, and it is useful to elucidate biochemical processes. In the field of molecular modeling, this tool is an option of rapid, with low computational, requirements, to perform molecular simulations of many systems. Many software, including the commercial ones, have been developed, and new algorithms are quickly incorporated to the packages. In the fields of computational biology and bioinformatics, it has become one of the most popular tools, with a wide range of applications. The diffusion of this amazing technique is a great strategy on the

We thank the Biomaterial Modeling group for the computational resources granted for

to be used and consequently reducing the environmental impact and other side effects.

advance of molecular studies and must be applied in many fields of knowledge.

\* and Vinícius Costa Amador2

1 Federal University of Campina Grande (UFCG), Sumé, Paraíba State, Brazil

2 Federal Rural University of Pernambuco (UFRPE), Recife, Pernambuco State, Brazil

[1] Hayes JD, Flanagan JU, Jowsey IR. Glutathione transferases. Annual Review of Pharmacology and Toxicology. **45**:51-88. DOI: 10.1146/annurev.pharmtox.45.120403.095857

[2] Robinson A et al. Modelling and bioinformatics studies of the human Kappa class glutathione transferase predict a novel third transferase family with homology to prokaryotic 2-hydroxychromene-2-carboxylate isomerases. Biochemistry Journal. **379**(Pt 3):541-552.

[3] Sheehan D. Structure, function and evolution of glutathione transferases: Implications for classification of 54 non-mammalian members of an ancient enzyme superfamily.

Biochemistry Journal. **360**(Pt 1):1-16. DOI: 10.1590/S1984-29612008000200007

\*Address all correspondence to: rafael.rafatrin@gmail.com

sor targets for iRNA technique.

**Acknowledgements**

research.

**Author details**

**References**

DOI: 10.1042/BJ20031656

Rafael Trindade Maia1

**Table 7.** Representation of the atoms of near residues belonging to bentazon sodium, atoms used as corresponding in the ligand and their respective distances in angstroms in the output.

**Figure 6** depicts a catalytic cavity where a conjugation with metsulfuron may occur. In the image, the complex with lower binding energy was chosen. The interaction with glutathione is made by a hydrogen bond. This is evidence that OsGSTU4 is able to bind to metsulfuron in order to promote the conjugation reaction. Theoretically, this enzyme plays an important role in the resistance to this herbicide.

Complementing the information in the figure information, **Table 7** shows the atoms of surrounding amino acid residues at distances less than 4 Å and their respective distances to atoms of the ligand.

## **5. Conclusions**

Molecular docking has proved to be an extremely useful technique for studying GSTs, especially in the context of resistance to chemical insecticides and herbicides. The methodology applied in these studies may be excused for other GSTs and other compounds. The complexes obtained provide a better understanding of the detoxification process performed by these enzymes.

However, although we find strong evidence of metabolization of these compounds, experimental studies should be undertaken to validate the in silico experiments. Site-directed mutation studies can be extremely providential to complement the information obtained here.

Not surprisingly, we notified that the GSTs here studied showed an affinity for more than one compound. This corroborates with the fact that members of this enzyme family display a multi-specificity on their H-binding-site.

As promiscuous proteins, these GSTs may be involved in metabolization of a wide range of toxic compounds, including other insecticides and herbicides. Further studies must be performed to investigate this.

Once the herbicide and insecticide resistance are multigenic, multi-enzymatic, and multifactorial process, the molecular docking technique can help to elucidate other pathways. Other computational techniques, such as molecular dynamics, can also give more insights about these systems.

Since herbicide and insecticide resistance is one of the major constraints of agriculture and mosquito control, the information from this study may be extremely useful for the development of specific inhibitors for these GSTs, thereby reducing the amount of herbicides and insecticides to be used and consequently reducing the environmental impact and other side effects.

New strategies of control can be applied too. The results point these enzymes as very promisor targets for iRNA technique.

The molecular docking is a powerful approach for understanding the interactions of molecules, and it is useful to elucidate biochemical processes. In the field of molecular modeling, this tool is an option of rapid, with low computational, requirements, to perform molecular simulations of many systems. Many software, including the commercial ones, have been developed, and new algorithms are quickly incorporated to the packages. In the fields of computational biology and bioinformatics, it has become one of the most popular tools, with a wide range of applications. The diffusion of this amazing technique is a great strategy on the advance of molecular studies and must be applied in many fields of knowledge.

## **Acknowledgements**

**Figure 6** depicts a catalytic cavity where a conjugation with metsulfuron may occur. In the image, the complex with lower binding energy was chosen. The interaction with glutathione is made by a hydrogen bond. This is evidence that OsGSTU4 is able to bind to metsulfuron in order to promote the conjugation reaction. Theoretically, this enzyme plays an important role

**Table 7.** Representation of the atoms of near residues belonging to bentazon sodium, atoms used as corresponding in the

**Near residue atoms Reference atoms (ligand) Respective distance (Å)**

Val105:CG' <0>0:C8 3.38 ALA106:HN <0>0:C7 3.25 ARG102: O <0>0:C7 2.79 VAL105:CG' <0>0:C1 3.58 ALA106:HN <0>0:C1 3.45 ARG102:HE <0>0:O2 3.71 GLN75:2HE2 <0>0:N2 2.11 GLN75:1HE2 <0>0:O3 1.78

Complementing the information in the figure information, **Table 7** shows the atoms of surrounding amino acid residues at distances less than 4 Å and their respective distances to atoms of the ligand.

Molecular docking has proved to be an extremely useful technique for studying GSTs, especially in the context of resistance to chemical insecticides and herbicides. The methodology applied in these studies may be excused for other GSTs and other compounds. The complexes obtained provide a better understanding of the detoxification process performed by these enzymes.

However, although we find strong evidence of metabolization of these compounds, experimental studies should be undertaken to validate the in silico experiments. Site-directed mutation

Not surprisingly, we notified that the GSTs here studied showed an affinity for more than one compound. This corroborates with the fact that members of this enzyme family display a

As promiscuous proteins, these GSTs may be involved in metabolization of a wide range of toxic compounds, including other insecticides and herbicides. Further studies must be per-

Once the herbicide and insecticide resistance are multigenic, multi-enzymatic, and multifactorial process, the molecular docking technique can help to elucidate other pathways. Other computational techniques, such as molecular dynamics, can also give more insights about these systems.

studies can be extremely providential to complement the information obtained here.

in the resistance to this herbicide.

ligand and their respective distances in angstroms in the output.

multi-specificity on their H-binding-site.

formed to investigate this.

**5. Conclusions**

44 Molecular Docking

We thank the Biomaterial Modeling group for the computational resources granted for research.

## **Author details**

Rafael Trindade Maia1 \* and Vinícius Costa Amador2

\*Address all correspondence to: rafael.rafatrin@gmail.com


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**Section 3**

**Molecular Docking for Medicinal Chemistry**


**Molecular Docking for Medicinal Chemistry**

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48 Molecular Docking

**Chapter 4**

**A Click Chemistry Approach to Tetrazoles: Recent**

Provisional chapterA Click Chemistry Approach to Tetrazoles: Recent

DOI: 10.5772/intechopen.75720

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

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

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

> © 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.

fumes. These are burst vigorously on exposed to shock, fire, and heat on friction.

**Advances**

Abstract

1. Introduction

1.1. Chemistry of tetrazoles

Advances

Ravi Varala and Bollikolla Hari Babu

Ravi Varala and Bollikolla Hari Babu

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

good to excellent yields.

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter
