Molecular Docking in Drug Repositioning and Virtual Screening

### **Chapter 5**

## Development of Nucleic Acid Targeting Molecules: Molecular Docking Approaches and Recent Advances

*Mohit Umare, Fai A. Alkathiri and Rupesh Chikhale*

#### **Abstract**

Molecular docking is a widely used and effective structure-based computational strategy for predicting dynamics between ligands and receptors. Until now the docking software were developed for the protein-ligand interactions and very few docking tools were developed exclusively for the docking of small molecules on the nucleic acid structures like the DNA and RNA. The progress in algorithms and the need for deeper understanding of ligand-nucleic acid interactions more focused, and specialized tools are being developed to explore this hindered area of drug discovery. This chapter is focused on and discus in details about various tools available for docking with nucleic acids and how the rejuvenation of machine learning methods is making its impact on the development of these docking programs.

**Keywords:** nucleic acids, molecular docking, docking algorithms, machine learning, non-canonical DNA, RNA

#### **1. Introduction**

Computer-Aided Drug Design (CADD) has evolved as a cost-effective method of producing potential medications for the treatment of a wide range of diseases [1]. The use of the CADD technique in pharmaceutical research is becoming more common. Recently, there has been a trend in drug design to strategically create effective therapies with multi-targeting effects, better effectiveness, and tolerability, particularly in terms of toxic effects [2, 3]. To assist the exploration, a mix of modern computer approaches, biological research, and synthesizing molecules was developed, and this combinational methodology increased the scope of discoveries [4, 5].

CADD may be generally defined as encompassing both structure- and ligand-based drug design (SBDD and LBDD) [6]. SBDD approaches are based on evidence acquired from an understanding of a target's three-dimensional structure, and they allow rating databases of compounds based on the affinity of ligands to a specific target [7, 8]. LBDD provides a generic technique for understanding links between the structural and compositional features of molecules and their bioactivities. When threedimensional data for a protein of interest is lacking, this strategy is used [9]. The existing knowledge on molecules and their bioactivity are employed in this approach to produce new possible therapeutic molecules. In this regard, molecular docking is a widely used and effective structure-based computational based strategies for predicting dynamics between ligands and physiological receptors [10, 11].

The molecular docking procedure consists of two main stages: projection of a new molecular configuration including its pose inside the peptide-binding pocket, and evaluation of the pose quality using a scoring function [11, 12]. Around 1975, highthroughput protein isolation, [13] nuclear magnetic resonance spectroscopy, and Xray crystallography [14] have advanced, primarily leading to improved knowledge of the structural properties of ligand and molecule complex [15].

MD studies, along with many other *in silico* technologies, have grown more frequent and simpler to use in drug development; yet it is not wholly reliant on molecular libraries. Since its inception in the 1980s as among the most mostly utilized procedures, the experimental data collected by MD techniques has developed at an accelerating rate [16]. Nearly annually, programs configured using various methods for MD analysis are produced, considerably boosting pharmaceutical research. The scoring function calculates the binding affinities of produced poses, ranks them, and selects the most advantageous ligand and protein binding modes [17].

The scoring function of an optimum search algorithm should be capable of assessing the physical and chemical characteristics of compounds and the thermodynamics of interactions [18]. The earliest algorithms were created to deal with protein interactions [19]. Over the previous few decades, the progressive development of efficient and comprehensive algorithms with the inclusion of new variables has mirrored computing technical breakthroughs. Kuntz and colleagues at UCSF then utilized a shape pairing method algorithm to keep looking for alternative combinations based on the geometric length between the target and the ligand molecule [20].

The molecular docking technique has risen to prominence in the realm of drug development. Times over the past twenty years, molecular docking has developed as a vital tool for computational drug development, and it has been proved to be more systematic than conventional drug development approaches [16]. The enormous increase in computational capabilities and the rising access of molecule and protein libraries have considerably aided molecular docking. Several docking methodologies have been implemented over the last several years that may be used to dock proteins on peptides with diverse levels of accuracy. Molecular docking was initially intended to be done between a ligand and a target protein, but there is a significant focus on docking between proteins, and nucleic acid-protein-ligand docking, nucleic acidligand docking in the recent decade [21].

Methods for addressing the shortcomings of the docking approach are still being researched [22]. Results can be refined, for example, by employing consensus procedures, implementing more stringent scoring techniques to a portion of the filtered library, or employing filters that include interaction fingerprints [23]. Significant effort has also been undertaken to collect inputs from potential binding waters. Identified water molecules as critical for molecule recognition can be considered part of the binding pocket, and prediction can be enhanced by energy contribution by displacing water molecules [24].

*Development of Nucleic Acid Targeting Molecules: Molecular Docking Approaches and Recent… DOI: http://dx.doi.org/10.5772/intechopen.107349*

#### **2. Methods in molecular docking**

#### **2.1 Monte Carlo**

In molecular docking studies, the Monte Carlo technique is the use in creation of a randomized conformation of a molecule in a targets active site. The advantage is that this method uses equilibrium statistical method. Rather than attempting to mimic a system's dynamics, it develops states based on the suitable Boltzmann distribution [25]. It determines the initial configuration value. Further, it generates and evaluates a new configuration. Through using Metropolis criteria, it assesses whether the new configuration should be preserved [26]. The Metropolis criteria states that if a new strategy provides better conformation than the previous one, it is recognized immediately. If the combination is not innovative, a probability assessment based on Boltzmann's law is used. If the conclusion passes the likelihood function test, it is approved, and the other arrangement is discarded [27].

#### **2.2 Ligand fit**

Ligand fit denotes to a rapid and accurate approach for docking small molecules into targets active sites while considering form as a complementarity. The technique of cavity identification is used in the procedure to discover and produce cavity in the protein as probable binding site locations [28]. For producing ligand poses that are compatible with the receptor binding site shape, a shape similarity screening is paired along with a Monte Carlo parametric analysis. A grid-based technique for analyzing energies between protein and ligand is used to reduce candidate poses with respect to the active site. A non-linear interpolation approach drastically reduces errors caused by grid interpolation [27, 28].

#### **2.3 Point complimentary**

Here on grounds of the complementarity of the interatomic contacts, a technique for docking a drug into a binding pocket in an enzyme is disclosed. Docking is accomplished by increasing a complementarity function that is reliant on the atomic surface area of contact as well as the elemental composition of the interacting atoms [29]. Although the target and ligand molecules are viewed as inflexible entities, mobility of a restricted range of residues bordering the binding site can also be considered. These techniques of molecular docking are focused on comparing the shapes and/or chemical properties of different molecules [26].

#### **2.4 Fragment based**

Fragment-based drug discovery (FBDD) is a novel strategy that is increasingly being used to improve hit recognition for previously thought intractable biological targets. FBDD, in specifically, uncovers small ligands (300 Da) capable of binding to pharmacologically important macromolecules with micromolar affinity [30].

#### **2.5 Distance geometry**

Even though it is primarily known as a tool for predicting the solution conformation of compounds from NMR data, distance geometry is a basic and effective tool for generating approximation models of complicated chemical formations [31]. Distance geometry is a basic geometrical approach that builds structures directly to fulfill model requirements; this does not involve an initial conformational or force field variables. The approach simply handles flexible rings without any extra attention or adjustment. Distance geometry is also distinct in that it works well together with qualitative data: a significant number of estimated distance boundaries are more useful in creating a model than a limited handful of highly exact distances [12, 31].

#### **3. Nucleic acid docking**

Nucleic acids (NAs) are biological macromolecules which can be broken down into phosphoric acid, sugars, and mixture of organic bases like purines and pyrimidines [32]. These can occur in various forms and constitute the building blocks like the DNA and RNA. These are essential for various cellular process including cell division and protein synthesis [33, 34]. Due to their crucial role in cell division, DNA, RNA, and their alternate structures have become target of choice for drug discovery in case of cancer drug discovery, infectious diseases, and rare diseases [35–38]. The NA modulators act by interfering with DNA replication process which affect the cell proliferation, transcription and ultimately inhibition of gene expression [39]. These agents can modulate the functioning of the RNA resulting in altered transcription and translation processes [40]. These modulators could be small molecule ligands, peptide or macromolecules, these can interact with the NAs by various mechanisms like intercalation, molecular cross-linking, DNA or RNA strand cleavage, and interference at the site of NA-protein interactions (**Figure 1**) [40, 41].

#### **Figure 1.**

*The commonly known NA structures with and without bound ligands; (A) duplex DNA structure with a bound antitumour drug, distamycin, PDB: 2DND [42]; (B) duplex RNA structure with a bound aminoglycoside antibiotic, apramycin, PDB: 2OE5 [43]; (C) DNA G-quadruplex in complex with the di-substituted amino alkylamido acridine compound (G4), PDB: 1L1H [44]; (D) RNA G-quadruplex (G4) crystal structures of TO1 biotin complexes of mango-III, a structure-guided mutant mango-III (A10U), PDB: 6E8S [45]; (E) i-motif DNA, a fragment of the vertebrate telomere which folds intramolecularly, PDB: 1ELN [46]; (F) i-motif RNA, a oligodeoxynucleotides with stretches of cytidine residues associate into a four-stranded structure, PDB: 1I9K [47]; (G) DNA hairpin, solution structure of the PdG-containing hairpin PDB: 1LAE [48]; (H) RNA hairpin, solution structure of RNA hairpin loop, PDB: 1HS2 [49].*

#### *Development of Nucleic Acid Targeting Molecules: Molecular Docking Approaches and Recent… DOI: http://dx.doi.org/10.5772/intechopen.107349*

Recent advancement in crystallization techniques, oligonucleotide synthesis, methods for structure determination like the NMR, crystal diffraction and cryo-EM has allowed for enrichment of structural data for NAs [50, 51]. The protein data bank (PDB) is an open source repository where these structures are deposited and curated [52]. There are more than 730 DNA-ligand and 523 RNA-ligand co-crystallized structures in the PDB and these would keep increasing [53]. Structural data of NAs helps in the investigation of the possible binding of ligands into the target, a co-crystallized structure provides with a bound ligand which helps understand the binding or active site in the given NAs. These co-crystallized molecules offer an excellent opportunity to perform structure-based and ligand-based drug discovery experiments and apply various other computational methods for drug discovery of NAs therapeutics. The most widely used method in computational drug design is molecular docking studies. The algorithms available for performing molecular docking are basically made for ligand-protein docking. There are several similarities like the protein and NAs follow similar physicochemical binding principles. However, these algorithms often fail to lack of sufficient sampling of the conformation space in case of NA docking to reasons of non-specific scoring functions [54]. Most of the target protein molecules contain a hydrophobic binding site whereas, the NAs consist of a rather more solvent-exposed binding pocket with higher polarity and charge density [55]. These are the major differences between the proteins and NAs as targets in molecular docking. Most of these algorithms are focused on the protein target molecules and need to consider parameters that need to be included in the program for NAs docking. NAs particularly the RNAs are very flexible owing to their charge, intrinsic atomic arrangements, and movements due to the presence of ligands. This flexibility is not considered by most of the programs as they consider NAs as rigid bodies [56]. Some programs like MORDOR are available that allows for the flexibility of the NAs and the ligands [57]. It applies molecular mechanics minimisation restraints based on the data from the X-ray and NMR experimental data [58]. There are several shortfalls to these methods, they are marred by slow speed, minimisation stages are slow, and time consuming, and large library screening is not feasible. Other NA specific methods reported were ensemble docking based on structural information from the X-ray structures or NMR or structures from the normal-mode analysis of an MD simulation [59–61]. The presence of water molecules and metal ions add to the complications in NAs docking. The water molecules and metal ions are essential for the stability and functioning of the NAs, this makes their presence in any docking protocol imperative. The metal ions in case of NAs like the i-Motif and G-quadruplex are necessary for the formation and stability of the structure [62, 63]. Various algorithms that considers these challenges in NAs docking are discussed in the section scoring function.

#### **4. Recent developments in docking tools for nucleic acid**

There are several types of small molecules that interact with the NAs and its alternate forms. These can be subdivided into double stranded DNA/RNA (ds-DNA and ds-RNA) binding, G-quadruplex DNA/RNA (G4-DNA and G4-RNA) binding, i-Motif DNA/RNA (iM-DNA and iM-RNA) binding ligands and ligands interacting with other DNA structures like hairpins [62, 63]. These ligands can also be classified based on their mechanism of binding to the DNA, for example covalent binding and intercalators. Several review articles have discussed these ligands in more details in the past [64]. The lab-based experiments and further crystallization experiments are

costly and time consuming and hence to assist with these efforts molecular modeling and docking tools are used widely to find the most suitable ligand. Most of the available molecular docking tools have been developed for protein-ligand docking. These tools have been used for NA-ligand docking irrespective of the fact that these tools do not consider the NAs as flexible moieties and thus do not consider the most important feature of NAs. The other type of docking interaction that NA undergo is with the proteins, Protein-NAs docking [65]. There are several algorithms that are used to perform NA-protein docking as mentioned in the table number 1. Earlier reports in NA-ligand docking dealt with finding correct docking conformations based on RMSD to the native co-crystallized ligand. Autodock and Surflex were used to dock several ligands like pentamidine, daunorubicin, distamycin and ellipticine in the minor groove of the ds-DNA. It was observed that Surflex performed better over Autodock in speed of operation and results with lower reference RMSD [66]. Several algorithms have been published and are available for NAs-ligand docking like, GRAMM, FTDock, 3D-DOCK, HEX, Dot and DoT2, HADDOCK, PatchDock, SymmDock, ParaDock, GOLD, Glide [67], NPDcok and HDOCK (**Table 1**). The most recent NA-ligand docking tools are NLDock, LigandRNA and DOCK 6.

The DOCK algorithm developed by the Kuntz lab has been traditionally a proteinligand docking program. However, the most recent development of the series is


*Development of Nucleic Acid Targeting Molecules: Molecular Docking Approaches and Recent… DOI: http://dx.doi.org/10.5772/intechopen.107349*


#### **Table 1.**

*List of NA-ligand docking tools with their names and principle of working and algorithms.*

DOCK6 which has the special feature to dock small molecules on the NAs. DOCK6 have significant progress in ligand orientation and conformational sampling which has led to significant improvement in the accuracy of docking for the large and flexible molecules over the NAs. It uses a sampling algorithm 'anchor-and-grow' which allows a cluster-based pruning with controlled cut-off of 25 kcal/mol. This flexibility in the upper limit allows for ranked orientation and improves prediction near the binding site. DOCK 6 uses the MD parameters like the AMBER GB/SA and PB/SA for predicting and ranking the poses and the effect of presence of metal ions and the water molecules in the binding site. The NLDock developed by the Huang lab uses ITScoreNL which is an iterative knowledge-based scoring function. The ITScoreNL uses a statistical mechanics based interactive algorithm. It uses the information from a training set of experimentally determined structures in the protein data bank (PDB). This scoring function consist of atomic, distance dependent pair potential, stacking interaction, and electrostatic effects. Results from ITScoreNL significantly improve the performance in binding and affinity prediction for the NAs-ligand complex. Recent advances and enrichment of the RNA structures in the PDB let to the development of LigandRNA. It uses the 3D information from the available RNA structures. A potential is obtained using the inverse Boltzmann scheme which considers the ligand poses that are favorable and exhibit interactions fitting the maxima of the statistical distribution of RNA-ligand atom contacts derived from the RNA-ligand co-crystal structures. This method is dedicated to scoring and ranking ligand poses in their RNA three-dimensional structure with correct intramolecular interactions while maintaining high accuracy and precision. These recent tools have given larger momentum to screening of ligands for NAs with better accuracy and speed.

#### **5. Scoring functions**

Molecular docking is quickly becoming a valuable technique in drug development and molecular modeling fields. The precision of the selected scoring function, that can lead and identify ligand positions when hundreds of potential ligand positions are created, determines the effectiveness of molecular docking [11, 91, 92]. The scoring function can also be used to forecast binding affinity and discover possible drug candidates for a specific protein of interest, as well as to define the binding mode and location of a molecule [93]. In lead optimization, scoring functions serve three main purposes: first, they recognize the best location of a ligand's binding to a protein based on the scoring function; second, they estimate the absolute binding affinity between the protein and ligand; and third, they perform virtual screening, which can identify possible drug leads for a given target protein by finding a sizable molecule database [93].

The most recent scoring functions for protein-ligand interactions using a new categorization that divides the scoring functions into force-field-based, empirical, and knowledge-based SFs. Ongoing study has drastically enhanced the research for scoring functions, particularly in protein-ligand interactions.

#### **5.1 Physics-based scoring functions**

Direct computation of the associations between both the atoms of a protein and a ligand is possible using physics-based SFs. Owing to the consideration of solvation, enthalpy, and entropy, physics-based SFs are suited to calculate binding free energy

*Development of Nucleic Acid Targeting Molecules: Molecular Docking Approaches and Recent… DOI: http://dx.doi.org/10.5772/intechopen.107349*

among proteins and ligands with significantly improved prediction performance than other forms of SFs [94]. These are founded on solvation models, force fields, and quantum mechanics techniques. The van der Waals and electrostatic interactions between the protein and ligand atom pairs are added up in the conventional force field-based SF, which considers the energy-contributing role of enthalpy, to estimate the binding energy [95].

Pairwise atomic interactions between the ligand and protein are the focus of the fundamental equation in the classical method. R is the distance between atomic centres, q is the fractional charge on every atom, and e is the dielectric constant. The A and B parameters are determined for every pair of various atom type combinations [96].

$$\Delta \mathbf{G}\_{bind} = \sum\_{i=1}^{ligand} \sum\_{j=1}^{protein} \left[ \frac{\mathbf{A}\_{ij}}{R\_{ij}^{12}} - \frac{\mathbf{B}\_{ij}}{R\_{ij}^{6}} + \frac{\mathbf{q}\_{i} \mathbf{q}\_{j}}{\varepsilon R\_{ij}} \right]$$

#### **5.2 Empirical scoring functions**

Empirical SFs calculate a complex's binding energy by adding up the essential energy components for binding affinity, such as hydrophobic effects, hydrogen bonds, steric conflicts, and so on. There are two study paths in empirical SFs. One approach is to use a usually high labeled training data to optimize protein complexes; the other is to pick appropriate energy terms using progressive parameters and methodical selection of the target molecule [92, 97].

#### **5.3 Knowledge-based scoring functions**

Predicated on the reverse Boltzmann statistic concept, knowledge-based SFs compute the appropriate pairwise potential in terms of 3D structures of a wide range of complexes. The rate of distinct atom pairs at different distances is thought to be connected to the interactions between two atoms, which translates the rate through the distance-dependent potential of mean force [18]. When tried to compare to physics and empirical SFs, knowledge-based SFs have the largest benefit in terms of processing cost and prediction accuracy. Unfortunately, knowledge-based SFs have a tough time locating the reference state [98].

#### **5.4 DrugScoreRNA**

Interactions of protein with protein, DNA, and ligand have all been studied using knowledge-based techniques. DrugScoreRNA is the first knowledge-based technique to scoring RNA-ligand complexes. Because of the small percentage of experimental measurements of RNA-ligand combinations, it was thought that obtaining statistically meaningful potentials was improbable [80].

The fact that the binding (free) energy landscape derived by such prospects is more focused than in the context of all other knowledge-based SFs or AutoDock may be taken into consideration as one of the factors contributing to DrugScoreRNA's effectiveness in docking [18]. This is anticipated to result in a quicker docking converging to a global solution, or, put another way, a lower probability that the configurational search would get stale in a local minimum. Reasonable correlation exists between experimental binding free energies and binding scores estimated by DrugScoreRNA [99].

#### **5.5 RiboDock**

The growing understanding of the significance of RNA in fundamental biological processes has lately made them more appealing as prospective therapeutic targets. To find small compounds that may selectively bind to identified locations in RNA molecules and inhibit or otherwise modify their function, a greater number of scientifically confirmed RNA three- dimensional structures were available. This allowed for structure-based searches for these molecules [100]. The access to high resolution structures of RNA-ligand complexes substantially facilitates the investigation of the atomic intricacies of RNA-ligand contacts. Furthermore, it is difficult to determine the physical structure of RNA and its interactions, and it is now unable to do so in a highthroughput way. This is what inspired the creation of source code for simulating the configurations of RNA-ligand complexes based on the known structures of RNA targets. Many of these advancements were motivated by comparable strategies used earlier for protein-ligand complex modeling [89, 100].

One of the first to develop a scoring function specifically for RNA-ligand complexes was done in 2004 by Morley and Afshar. They added the empirical regressionbased tool RiboDock (or rDock) to their own high-throughput docking tool to handle RNA-ligand structures [101]. This technique was, unfortunately, parameterized and tested on a small sample size of just 10 RNA molecules. Ligand intramolecular, intermolecular, site intramolecular, and external constraint factors are weighted together to form the rDock master score function. The major terminology of importance is Sintra, which stands for the RNA-ligand interaction score. According on the provided ligand configuration, Sintra provides the ligand's energy transfer. Similar to Ssite, this term denotes the comparative energy of the active site's variable regions [100, 101].

#### **5.6 LigandRNA**

As discussed in the above section, the importance of RNA in fundamental biological processes has grown the scientific community interest in the research area of Nucleic Acid-Ligand docking. Another Scoring function developed for the similar function was LigandRNA [89].

The RNA-ligand complexes were computationally solved using the LigandRNA approach, which uses a grid-based algorithm and a knowledge-based SFs obtained from ligand-binding domains. LigandRNA requires two files as inputs: an RNA receptor file and a ligand poses file. It produces a list of poses ranked by their score as an output [100]. The potential is calculated using the inverse Boltzmann method, which assumes that only ligand poses with interactions that meet the maximum of the statistical distribution of RNA-ligand atom contacts generated from empirically established structures of RNA-ligand complexes are advantageous. Thus, according to their value, the supplied ligand poses are sorted, and this score would be used to assess the relative effectiveness of binding [89].

#### **5.7 MM/PBSA and MM/GBSA**

The molecular mechanics energies combined with the Poisson–Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) are the popular techniques for estimating the free energy of the binding of ligand

*Development of Nucleic Acid Targeting Molecules: Molecular Docking Approaches and Recent… DOI: http://dx.doi.org/10.5772/intechopen.107349*

molecules to the target protein. In MM/PBSA, the free energy of a state, that is, P, L or PL in the following equation, is estimated from the following sum [102].

*G* = *E*bnd + *E*el + *E*vdW + *G*pol *+ G*np - *TS. E*bnd: Bonded (bond, angle and dihedral) energy. *E*el: Electrostatic Energy. *E*vdW: van der Waals interactions. *G*pol: polar contribution to the solvation free energy. *G*np: non-polar contribution to the solvation free energy.

To calculate the MM/GBSA free energy, the system of relevance is first modeled either using Metropolis Monte Carlo or molecular dynamics (MD), with pose is being obtained at set intervals and for each pose the free energy is calculated by the above equation. The continuum-solvation technique, the dielectric constant, the charges, the sample selection, and the entropies have a significant impact on the outcomes. The approaches frequently exaggerate the differences between different ligand groups [103]. In actual use, it frequently produces outcomes of middling quality, frequently outperforming docking, and scoring. However, because of the findings'substantial reliance on the continuum solvation used, either the absolute affinities or the methodology is invalid [103, 104].

#### **5.8 Molecular recognition with a driven dynamics optimizer (MORDOR)**

The fixed nature of the protein target is drawback in most of the docking tools. To overcome this and to explore the dynamic nature of the target Molecular Recognition with a Driven dynamics Optimizer (MORDOR) tool was developed. MORDOR allows induced-fit type of docking algorithm. A new RNA stabilizing loop can be formed by the ligand, which could move bases [105].

MORDOR uses a unique conformational field search technique to achieve this goal, enabling a productive thorough search while docking. Utilizing a driving force to move the ligand, this method combines molecular minimization technique. By applying an extra RMSD kind of force, the ligand explores the receptor surface after beginning from any pose in and around the receptor. It is crucial to research induced fit with MORDOR when docking proteins, especially RNA. Drugs do not often bind a conventional form of nucleic acid, according to the architectures of nucleic acid-drug complexes. Also, more control over the docking process is provided by the allowance of an infinite number of restraints. Contrarily, it seems from known drug-nucleic acid binding structures that the small molecule ligands frequently replace bases, leading to a local restructuring of the nucleic acid. A drug development process will have a far better chance of being successful if flexible docking for RNA is used [61, 105].

#### **5.9 Dock-RNA**

Numerous biological activities, including the production and control of gene activity, depend on nucleic acid-ligand interactions. As a result, nucleic acid molecules like RNAs have grown in importance as pharmacological targets and knowing the structural characteristics of RNA-ligand complexes is essential to deriving treatment strategies. The nucleic acid-ligand docking method is divided into two stages: The model chooses a preliminary set of potential poses during the first stage using a different computer algorithm for the Born radiuses in the electrical charges; with in second stage, a stringent scoring function is utilized to arrange the poses to identify the top molecules [106].

The scoring function of the molecular docking program is dependent on the shift in free energy caused by RNA-ligand binding. It aggregates comparable ligand poses into clusters based on geometrical similarity and ranks the grouped poses based on the binding affinity. Because it separates itself from other models by sampling all potential interaction site and poses globally, the findings above highlight the relevance poses. Unfortunately, the RLDOCK approach is difficult to apply to big target and ligand sets. The time-consuming selection of the complex formation produces prohibitively small processing effectiveness of the approach in complexes with a big RNA such as ribosomal RNA or ligands with the more than 12 rotatable bonds [107, 108].

#### **6. Role of machine learning and artificial intelligence**

Machine learning (ML) specially the Deep learning methods (DL) and Artificial intelligence (AI) has rapidly developed and is being used in drug discovery. ML in drug discovery is used to improve the existing scoring functions or to develop a new scoring function for virtual screening studies. The existing scoring functions can be improved by refining their empirical function's weights. Most of the ML based scoring function improvements has been seen in the protein-ligand docking and their virtual screening domain. The ML methods being used are Random Forest methods [109], Gradient boosting trees method [110], Support vector machine methods [111], Multilayer perceptron methods [112], Convolutional neural network methods [113], and Graph neural network [114]. The scoring functions for NAs-ligand interactions can be classified into force-field based, empirical, knowledge-based and machine learning based. The machine learning based scoring functions can capture intrinsic nonlinearities in the training set without imposing a predetermined functional form. The most important feature that separates the ML methods from others is that ML maps the ligands to a potential energy landscape, it is inherently flexible, and the mapping relationship works without the addition of extensive physicochemical knowledge. However, the use of ML in NAs binding ligands discovery comes with certain challenges as well. First, the mapping relationships generated by ML are not always interpretable and the second, ML models for NAs could find difficult to make accurate predictions for complexes out of the training sets.

For the NA-ligand complex interactions two ML based scoring functions were recently developed, RNAPoser [115] and AnnapuRNA [116]. The RNAPoser uses a set of 80 RNA-ligand experimental structures as dataset and investigates the 'nativeness' of the RNA-ligands poses. This program uses machine learning methods to train a set of pose classifiers that would estimate the position of the ligands in the experimental structures. These poses are defined as fingerprints which are encoded as local RNA environment surrounding the ligand. This method uses the leave-one-out training and testing approach where about 80% of the native poses were recovered within 2.5 Å. The classification is done based on ranking of ligands and scoring from machine learning classifiers, which were able to recover the native like poses. The validation set for the method returned recovery of native poses for more than 60% of the cases. These were found to be better than the poses with higher docking scores. Another recent development in the NA-ligand docking improvement is AnnapuRNA. It is a machine learning-based statistical scoring function which can evaluate the quality of RNA-Ligand complex structure predicted by a computational docking program and thus help in validation of the docking results. It uses the information like the initial ligand conformation, the docking program and the scoring function used by the

*Development of Nucleic Acid Targeting Molecules: Molecular Docking Approaches and Recent… DOI: http://dx.doi.org/10.5772/intechopen.107349*

docking program. The training set is derived from the experimental data available on the PDB and it uses the *k*NN (*k*-Nearest Neighbors) and Deep Learning (multi-layer feedforward artificial neural network) as ML algorithms. This program supports a various docking program like the AutoDock, AutoDock Vina, Dock6, rDock, iDock, LigandRNA, and several other NAs specific programs.

#### **7. Conclusion**

In this chapter we have overviewed various important aspects in development of small molecule inhibitors for NAs and various docking software specific and nonspecific for NAs-ligand docking. We have also reviewed various docking programs, algorithms and scoring functions, their advantages and lacune and challenges in the discovery of novel NAs binding ligands. Until recently most of the algorithms were focused on protein-ligand docking but now slowly programs specific for NAs are appearing in the molecular docking space. The progress in ML and AI has led to an advantage for development of NA specific algorithms. However, there is lot of scope for development of NA-docking specific programs, structural variations of NA also pose a challenge for the new programs. However, it is possible to convert these challenges into opportunities as the need for better NA targeting ligands are high in demand specifically due to the resurgence of viral infections and other infectious disease.

#### **Acknowledgements**

FA acknowledges the support provided by Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.

#### **Author details**

Mohit Umare<sup>1</sup> , Fai A. Alkathiri<sup>2</sup> and Rupesh Chikhale<sup>3</sup> \*

1 Tata Consultancy Services Limited, Pune, India

2 Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia

3 UCL School of Pharmacy, London, UK

\*Address all correspondence to: rupeshchikhale7@gmail.com

© 2022 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.

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[115] Chhabra S, Xie J, Frank AT. RNAPosers: Machine learning classifiers for ribonucleic acid-ligand poses. Journal of Physical Chemistry B. 2020;**124**(22): 4436-4445 [Internet]. [cited 2022 Jul 27]. Available from: https://pubs.acs.org/ doi/full/10.1021/acs.jpcb.0c02322

[116] Stefaniak F, Bujnicki JM. AnnapuRNA: A scoring function for predicting RNA-small molecule binding poses. PLoS Computational Biology. 2021;**17**(2):e1008309 [Internet]. [cited 2022 Jul 29]. Available from: https:// journals.plos.org/ploscompbiol/article? id=10.1371/journal.pcbi.1008309

### **Chapter 6**

## Repurposing Drugs as Potential Therapeutics for the SARS-Cov-2 Viral Infection: Automatizing a Blind Molecular Docking High-throughput Pipeline

*Aldo Herrera-Rodulfo, Mariana Andrade-Medina and Mauricio Carrillo-Tripp*

#### **Abstract**

In the context of the COVID-19 pandemic, scientists worldwide have been looking for ways to stop it using different approaches. One strategy is to look among drugs that have already proved safe for use in humans and tested for other illnesses. Several components from the virus and the infected cell are the potential therapeutic targets from a molecular perspective. We explain how we implemented a cavity-guided blind molecular docking algorithm into a high-throughput computational pipeline to automatically screen and analyze a large set of drugs over a group of SARS-CoV-2 and cell proteins involved in the infection process. We discuss the need to significantly extend the conformational space sampling to find an accurate target-ligand complex. Our results identify nine drugs with potential multi-target activity against COVID-19 at different stages of the infection and immune system evasion. These results are relevant in understanding the SARS-CoV-2 drug's molecular mechanisms and further clinical treatment development. The code developed is available on GitHub [https://github.com/tripplab/HTVS].

**Keywords:** SARS-CoV-2, COVID-19, drug repurposing, cavity-guided blind molecular docking, high-throughput virtual screening

#### **1. Introduction**

The coronavirus disease-2019 (COVID-19) is the third documented viral outbreak caused by a member of the *Coronaviridae* family. From 2002 to 2004, the severe acute respiratory syndrome coronavirus (SARS-CoV) spread to 29 countries, causing 8422 confirmed cases and 916 deaths, and is considered the first emerging epidemic of the twenty-first century [1, 2]. Later in 2012, the middle-east respiratory syndrome coronavirus (MERS-CoV) caused 2585 confirmed cases and 890 deaths to date [3]. In less than two decades since the appearance of SARS-CoV, the severe acute respiratory

syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 and has spread worldwide ever since by human-to-human transmission. As of April 29, 2022, there are more than 510 million confirmed cases and 6.2 million deaths related to SARS-CoV-2 infection, and it continues to increase at present [4]. The *Coronaviridae* family comprises a group of enveloped crown-shaped single-stranded positive-sensed RNA viruses (ssRNA+) with multiple domestic and wild animal reservoirs [5]. Lessons from previous and current outbreaks have shown the severity of cross-species transmission, which has led to concerns about health emergencies, such as COVID-19. The transmission of this disease occurs through an infected person's respiratory droplets carrying the SARS-CoV-2, and the severity ranges from asymptomatic cases, mild and moderate flu-like symptoms, to critical illness requiring intensive care with mechanical ventilation, and death [6]. Global contributions and efforts following the COVID-19 outbreak have unraveled a considerable amount of information about viral infection, transmission, infection cycles, and immune evasion. Currently, the threedimensional proteome structures of the SARS-CoV-2 are available on the RCSB protein data bank [7]. Therefore, it is feasible to evaluate drug-like small molecules against relevant targets in the viral infection cycle through a structure-based molecular docking approach. Blind molecular docking, unlike traditional molecular docking, does not require prior knowledge of target binding sites, which simplifies the automatizing of the process since it only needs the structural information of the target. In the past, this process was considered less accurate than the traditional. However, methods, such as CB-dock, have overcome this limitation by reducing the nonrelevant conformation sampling by directing the molecular docking on putative sites instead of the whole protein structure [8]. The integration of this tool into our customized high-throughput virtual screening pipeline allows the screening of N sorted-by-size cavities. The cavitybased search is an exciting scenario because protein-ligand interactions usually occur in large protein cavities or pockets that frequently contain the active site [9]. Moreover, the exploration of cavities in the vicinity of protein-protein interfaces (PPI) is also an attractive approach to searching for effective inhibitors since it plays an essential role in nearly all biological processes, including SARS-CoV-2 infection [10, 11]. In this context, screening already-known drugs with described pharmacology, dose, toxicity, formulation, and proven to be safe for use in humans represents a low-risk and cost-effective strategy to considerably shorten the time required for drug approval [12, 13]. We present an in-house customizable pipeline that integrates a cavity-guided blind molecular docking algorithm to extend the conformation space sampling on putative sites significantly. We also report the methodology to follow and results of the virtual screening of 47 drugs for potential repurposing against 16 structures of 10 viral and cell targets that are key in the SARS-CoV-2 infection cycle.

#### **2. Overview of the SARS-CoV-2 infection cycle**

The initial stage of the infection cycle starts with the recognition and anchoring of the SARS-CoV-2 spike protein complex into the host angiotensin-converting enzyme 2 (ACE2) through the receptor-binding domain (RBD) located at each one of the 3S proteins [14]. Then, the activation of the spike occurs at the surface or endosome level by transmembrane serine protease 2 (TMPRSS2) or cathepsin B/L proteases, respectively, to allow viral entry [15]. Once the virus membrane merges with the cell membrane, the genomic material enters the cell. The cell's ribosomes then translate the viral RNA into pp1a/ab polyproteins, which will be later processed by cleavage

*Repurposing Drugs as Potential Therapeutics for the SARS-Cov-2 Viral Infection… DOI: http://dx.doi.org/10.5772/intechopen.105792*

through the enzymatic activity of the main protease (Mpro) and the papain-like protease (PLpro) [16]. This process will release 16 non-structural proteins (NSPs), including the RNA-dependent RNA polymerase (NSP12) and co-factors NSP7, and NSP8 of the RNA-replication machinery (Rdrp). After replication, expression of the structural proteins occurs, the genomic material is packaged, and the virion is assembled on a lipid membrane and matured for subsequent exocytosis. In addition, evidence suggests that the SARS-CoV-2 proteases and some of their cleavage products, besides their critical function for the proper infection process, interplay with the host's innate immune response through different mechanisms [17]. In particular, PLpro-ISG15 interaction allows the virus to evade the innate immune response through deubiquitination and deISGylation activities of the protease [18, 19]. Interestingly, the process occurs at the same binding cavity as the PLpro known inhibitor, GRL0617 [20].

#### **3. Methods**

The code for the cavity-detection guided blind docking (CB-Dock) [8] stand-alone version is freely available at Yang Cao's Lab webpage [http://clab.labshare.cn/cb-doc k/php/manual.php#download].

The customized high-throughput virtual screening pipeline we developed can be accessed at GitHub [https://github.com/tripplab/HTVS].

#### **3.1 Drug selection and modeling**

We conducted an extensive scientific literature search for drugs reported as potentially able to prevent SARS-CoV-2 infection. The search included *in silico*, *in vitro*, and *in vivo* studies, covering different stages of the viral cycle. We grouped the reported ligands into five sets: the fusion and viral entry into the host cell (RPA), the polyprotein processing by viral proteases (RPB and RPD), the RNA replication machinery (RPC), and other drugs with alternative or unknown mechanisms (EXT).

We performed the molecular *in silico* modeling of each ligand's configuration using the PubChem compound identifier (CID) or, in its absence, using UCSF chimera 1.15 from scratch [21]. The solvent, ions, and other small molecules were removed in all cases, while charges and hydrogens were fixed at neutral pH. Then, ligands were subjected to energy minimization by 10,000 steepest descent steps and 1000 conjugate gradient steps to ensure the proper molecular conformation, saving the final structure in MOL2 format. The next step was to generate the files in PDBqt format using AutoDock Tools, considering the torsional degrees of freedom [22]. We used the PDBqt and MOL2 files as input for the high-throughput virtual screening pipeline. A list of all the ligands studied in this work is shown in **Table 1**.

#### **3.2 Target selection and modeling**

We included viral and cellular targets involved in the SARS-CoV-2 infection cycle, covering the entry, polyprotein processing, and replication. The targets' threedimensional structures were obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB) in PDB format [65]. The complete structure of the spike homotrimer complex (PDB: 6VXX-1-1-1) was retrieved from the CHARMM-GUI Archive-COVID-19 proteins library [66]. We took special consideration to the spike complex given its large size and quaternary structure. We focused


*Repurposing Drugs as Potential Therapeutics for the SARS-Cov-2 Viral Infection… DOI: http://dx.doi.org/10.5772/intechopen.105792*


#### **Table 1.**

*Ligand information, CID number, and reference of 47 drugs with potential activity against the SARS-CoV-2 viral cycle.*

on four independent spike-based structures to extend the cavity sampling: the fulllength spike's homotrimer complex, the homotrimer head (S1), 1 S protein monomer, and one isolated receptor-binding domain (RBD).

Water, ions, glycosylations, and co-crystallized ligands were removed from all targets. Charges and hydrogens were fixed at neutral pH using chimera 1.15, their structure optimized, and the final configuration saved in PDB format [21]. In total, 16 structures of 10 targets were curated, as summarized in **Table 2**.

#### **3.3 Extended conformational space sampling maximizes the prediction accuracy of the target-ligand complex**

Molecular docking is a computational method that allows to sample the conformational space and rank the ligand poses through an energy scoring function. It attempts to generate an optimized target-ligand complex conformation with the lowest binding free-energy change estimate, predicting the interaction of the two molecules in the energy minimum. This task is a cyclic process performed by systematic or stochastic search methods. However, the latter is the choice of preference since it increases the probability of finding an energetic global minimum conformation because the search initiates from different random points [78]. For this reason, the results of two or more molecular docking cycles are not necessarily the same due to the random nature of the conformational search method. Therefore, performing as many cycles as necessary to get as close as possible to the energetic global minimum conformation is essential.

Easy customization of this parameter in the developed high-throughput virtual screening code offers the user the possibility of an exhaustive sampling of the conformational space that maximizes the accuracy of target-ligand complex prediction.

#### **3.4 High-throughput virtual screening pipeline**

We have developed in-house bash scripts that integrate the CB-Dock's cavity-guided blind molecular docking method, which automatically identifies binding sites by calculating putative cavities through a curvature-based detection approach. Molecular


#### **Table 2.**

*Structural information and PDB entries of viral (V) and host (H) targets included.*

docking analysis is conducted in these putative cavities to sample and rank ligand poses and estimate the best target-ligand complex binding energy scores per cycle.

The pipeline has three phases comprised of nested loops, schematized in **Figure 1** as a flowchart. First, each target *T* is subject to a cavity detection step based on a spatial geometry measure of curvature distribution on the protein surface [79]. Cavity identification is achieved by clustering the resulting surface points by density and curvature factor [80]. All cavities are then sorted by size, considering their solvent-accessible surface area. Second, the algorithm automatically configures a docking box for each cavity by defining its center and size, considering the cavity space location and the ligand *L* size. Finally, in the third step, the blind molecular docking is performed by the AutoDock VINA algorithm [8, 81] for the user-defined top *N* cavities for each ligand *L* and each target *T*. This protocol will be repeated for *K*-independent rounds.

In our study, we found that the optimal number of independent rounds is *K* ¼ 30 since it is at this point that the conformational search converges to the lowest energy binding pose; that is, more rounds do not improve the prediction. The calculations were performed on the top *N* ¼ 10 cavities for each *T* � *L* pair to significantly extend the cavity and conformational space sampling. The value of these parameters is easily customizable at the top section of the bash script.

#### **3.5 Target-ligand co-crystallization complex prediction**

The method we used for automatizing the virtual high-throughput screening process is blind; that is, it does not require any information on the binding site. Hence, we *Repurposing Drugs as Potential Therapeutics for the SARS-Cov-2 Viral Infection… DOI: http://dx.doi.org/10.5772/intechopen.105792*

**Figure 1.**

*Flowchart of the customized high-throughput virtual screening pipeline implemented in this work. Four phases are involved, i) target and ligand molecular modeling (blue), ii) target cavity detection (green), iii) docking box optimization (orange), and iv) target-ligand docking (red).*

validated its predictions by reproducing the enzymatic targets' experimental binding complexes. We gathered a set of ligands with available complex co-crystallized data. Eight known enzymatic inhibitors were modeled, optimized, and evaluated under the same methodology conditions as the rest of the ligands included in this study. The ligands in the control set are listed in **Table 3**.

Furthermore, at this time, a small drug-like co-crystallized molecule in complex with the spike homotrimer does not yet exist. We included amantadine (INV05) in our set as a negative control since it inhibits the SARS-CoV-2 infection but does not prevent spike-ACE2 interaction [83].

#### **3.6 Data analysis and selection criteria**

We inspected the top 10 size-ranked putative cavity sites screened for each target. We selected those that either had the active site (targets ACE2, TMPRSS2, cathepsin B/L, Mpro, and NSP12), or were inside a quaternary interface (targets spike, PLpro, and Rdrp). We selected the *T* � *L* complex conformation with the best affinity estimation, that is, the conformation with the lowest energy scores after *K* ¼ 30 independent rounds for each target-ligand pair. We organized the data in matrix form and analyzed it with the statistical R package function *heatmap.2*. Rows (ligands) or columns (targets) were scaled to have average = 0 and standard deviation = 1 and generated a Z-score heatmap representation. Finally, we identified potential drugs for repurposing as those ligands with the best energy score estimate at least one standard deviation away from the mean toward more negative values. The data matrix of the VINA scores of the conformation with the lowest scores after *K* ¼ 30 independent


#### **Table 3.**

*Modeled ligands to validate that the method is capable of reproducing the co-crystallized complex conformations and previous* in vitro *findings (negative control).*

cycles of each target-ligand pair for known inhibitors and the set of ligands evaluated are provided in the appendix section as **Tables A-1** and **A-2**.

#### **4. Results**

#### **4.1 Blind docking correctly reproduces co-crystallized known-inhibitor binding**

We found that the *T* � *L* complex conformation with the lowest energy for the known co-crystallized inhibitors in our control set successfully reproduces the ligand binding at the active site with an RMSD below 1 Å in most cases, as shown in **Figure 2**. These findings strongly suggest that the implemented high-throughput blind docking cavity-guided protocol can accurately predict the binding mode of the *T* � *L* data in the experimental set.

#### **4.2 Statistical analysis of the data: Sorting results by target**

After doing all the blind docking calculations with an extended conformation sampling, we analyzed the most negative energy scores. We performed a Z-score transformation of the data for each independent column in the matrix (targets *T*). The graphical representation of the results is shown as a heatmap in **Figure 3** using a six-color code based on the Z-score value.

Since each column gathers the results for a different target, it is thus possible to identify which ligands had the best scores for each target (in green). It is worth noting that cathepsin L (H05) and PLpro (V08) co-crystallized inhibitors give a good binding free-energy estimate. Most of the co-crystallized inhibitors remained near the mean

*Repurposing Drugs as Potential Therapeutics for the SARS-Cov-2 Viral Infection… DOI: http://dx.doi.org/10.5772/intechopen.105792*

**Figure 2.**

*Target-ligand complex superimposition of native co-crystallized inhibitors (yellow) and the best-predicted ligand conformation after K* ¼ 30 *independent blind docking pipeline rounds (green). The molecular targets (orange) are ACE2 labeled as H00,TMPRSS2 (H03), cathepsin B (H04), cathepsin L (H05), Mpro (V03), NSP12 (V04), and PLpro (V08), created with the visual molecular dynamics (VMD) [84].*

(in black, with respect to the experimental drug set), except for amantadine (INV05), which presents a positive Z-score value for the spike's RBD (in red). The latter is concomitant to previous works, where amantadine fails to prevent the spike-ACE2 quaternary interaction [83].

#### **4.3 Nine ligands showed potential for drug repurposing against targets involved in SARS-CoV-2 infection**

Out of the 47 drugs screened, nine showed potential inhibition against viral or host targets of the SARS-CoV-2 infection cycle. Saquinavir, simeprevir, nilotinib, an isatinderivative, telmisartan, tegobuvir, qingdainone, rac5c, and nafamostat achieved the selection criteria. Interestingly, all but rac5c and nafamostat showed the best scores against more than one target. The schematic representation of these results is summarized in **Table 4**.

#### **4.4 Sorting results by ligand: The screened ligands showed a preference for ACE2, Spike, and PLpro targets**

Also, we inspected the results by ligand, performing a Z-score analysis by row (ligands). Since the rows gather data from the *L* � *T* complex, it is thus possible to identify which ligand had the best scores on any particular target, that is, which target *T* might be a potential pharmacological target for the ligand *L*. The results are presented in **Figure 4** with a heatmap, using the same color-based code as previously described (see Section 4.2). The targets having most of the ligands in the experimental set with a negative Z-score are ACE2 H00, H01, and H02, and the spike protein structures V01, V01H, and V02 (see **Table 2**). The results suggest a greater acceptance of those two targets for the ligands as drug-like molecules, at least in the cavities

#### **Figure 3.**

*Target (columns) and ligand (rows) complex docking results. Heatmap of binding free-energy change estimates, using a color-based code according to the Z-score value through column analysis. Targets are grouped as host proteins (blue) and virus proteins (pink). Ligands are grouped by control set (green), potential repurposing drugs (orange), and others (brown). IDs correspond to those defined in Tables 1–3. Black shades represent ligands around the set's mean. In green shades, ligands with at least one negative standard deviation from the mean. Red shades represent ligands with at least one positive standard deviation from the mean.*

evaluated. These findings are not a minor fact because those targets are directly involved in the first step of the viral infection.

The ligands such as arbidol, colchicine, qingdainone, nafamostat, and carvacrol exhibit a binding preference to PLpro (V08). It is important to highlight the essential function of the protease PLpro for processing the viral proteome and evading the host's innate immune system. In the latter case, PLpro cleaves off post-translational modifications, such as ubiquitin and ubiquitin-like proteins from cell proteins, disrupting the inflammatory signaling pathway necessary for an appropriate immune response [18, 85]. Noteworthy, the potential PLpro inhibitors we have identified in the present work as repurposed drugs form a *T* � *L* complex in the cavity where GRL0617 binds, located in the USP domain [20]. The inhibition of this site means blocking the interaction with the ubiquitin-like protein ISG15, evading the immune mechanisms and compromising its canonical enzymatic activity due to the proximity of the assessed site to the active site.


*Repurposing Drugs as Potential Therapeutics for the SARS-Cov-2 Viral Infection… DOI: http://dx.doi.org/10.5772/intechopen.105792*

#### **Table 4.**

*Potential drugs for repurposing with the most negative free-energy change score found and their corresponding Zscore value grouped by the target.*

#### **5. Scientific evidence to support our findings**

#### **5.1 Saquinavir and simeprevir targeting viral entry and Rdrp quaternary complex formation**

Saquinavir is a peptide-mimetic HIV inhibitor. However, some reports suggest potential inhibitory activity against SARS-CoV-2 proteases [86–88] and other targets involved in the viral infection, such as the Rdrp replication complex [55, 89] and the

#### **Figure 4.**

*Target (columns) and ligand (rows) complex docking results. Heatmap of binding free-energy change estimates, using a color-based code according to the Z-score value through row analysis. Targets are grouped as host proteins (blue) and virus proteins (pink). Ligands are grouped by control set (green), potential repurposing drugs (orange), and others (brown). IDs correspond to those defined in Tables 1–3. Black shades represent ligands around the set's mean. In green shades, ligands with at least one negative standard deviation from the mean. Red shades represent ligands with at least one positive standard deviation from the mean.*

spike-ACE2 PPI [90]. In our study, saquinavir showed the best energy scores against TMPRSS2, ACE2, and the NSP8-NSP12 interface of the Rdrp complex, as shown in **Figure 5**. The transmembrane serine protease 2 (TMPRRS2) is essential in several viral infections. Previous reports have shown that the inhibition of this target significantly reduces SARS-CoV-2 entry in lung cells at nM concentrations and therefore the viral infection [91]. Saquinavir also presented the best energy scores against the ACE2 active site, a critical host target needed to initiate entry through the formation of the spike-ACE2 quaternary complex. In this scenario, conformational changes upon ligand binding into the catalytic cavity may shift the relative positions of the receptor's interface residues that bind to the spike protein and prevent the anchoring of the spike on host cells [92]. However, because saquinavir targets the catalytic site of ACE2, the main activity of this enzyme in the renin-angiotensin system requires further investigation of its biological effect as a competitive inhibitor [93]. In addition, our results show that this drug targets the Rdrp replication complex, which is consistent with the previous results reported in the literature [55, 94]. Interestingly, saquinavir appears to *Repurposing Drugs as Potential Therapeutics for the SARS-Cov-2 Viral Infection… DOI: http://dx.doi.org/10.5772/intechopen.105792*

#### **Figure 5.**

*Target-ligand complex conformations of potential drugs for repurposing. Molecular docking against viral and host targets relevant in the SARS-CoV-2 infection cycle. A. Superposition of ACE2 target (H00, H01, and H02) docked with saquinavir (cyan), isatin-derivative (red), nilotinib (pink), rac5c (brown), and simeprevir (yellow). B. TMPRSS2 docked with saquinavir (cyan) and nilotinib (pink). C. Spike docked with telmisartan (purple) and isatin-derivative (red). D. Cathepsin B docked with nilotinib and simeprevir (yellow). E. Mpro docked with nilotinib (pink) and qingdainone (orange). F. PLpro docked with nafamostat (green), nilotinib (pink), qingdainone (orange), and tegobuvir (dark orange). G. Superposition of NSP12 and NSP7 and NSP8 cofactors docked with simeprevir (yellow), tegobuvir (dark orange), nilotinib (pink), telmisartan (purple), qingdainone (orange), and saquinavir (cyan), created with the visual molecular dynamics (VMD) [84].*

target two essential steps, compromising the entry and viral replication of the SARS-CoV-2.

On the other hand, simeprevir also showed the best energy scores on targets relevant to viral entry and replication, including the active cavities of ACE2, cathepsin B, NSP12, and the Rdrp complex interface. We show a molecular visualization of these results in panels A, C, and G of **Figure 5**. This drug is a protease inhibitor that has presented potent *in vitro* suppression of SARS-CoV-2 replication at *μ* M range in Vero E6 cell lines [95]. It is a macrocyclic drug that forms a non-covalent bond within the active site of the hepatitis C virus (HCV) NS3/4A protease, which has a similar threedimensional arrangement to SARS-CoV-2 Mpro catalytic residues [96]. Simeprevir binds to ACE2 in a quaternary complex inhibition mechanism, analogous to saquinavir, concomitant with the reported disruption of spike-ACE2 PPI [90]. However, the binding does not occur directly at the active site as saquinavir but in the same but larger cavity. Additionally, this drug targets the peptidase activity of cathepsin B, which is a crucial step in spike activation and viral entry. ACE2 was previously proposed as a strategic target to limit viral infection by targeting the cathepsinmediated entry pathway, decreasing the viral infection efficiency [15, 97]. Simeprevir also showed the best results for the Rdrp complex. Consistent with our results, biochemical assays show low Rdrp replication efficiency after treatment with this drug [95].

#### **5.2 Nilotinib targeting viral entry, polyprotein processing, and Rdrp quaternary complex**

Nilotinib is used to treat chronic myelogenous leukemia as a Bcr-Abl tyrosine kinase antagonist. Our results suggest the potential inhibition of six targets involved in the SARS-CoV-2 infection process, including the catalytic cavities of enzyme targets ACE2, TMPRSS2, cathepsin B, Mpro, and the PLpro-ISG15 and Rdrp's NSP8-NSP12 interfaces. We show a molecular visualization of these results in **Figure 5**. Reports suggest that nilotinib can inhibit the SARS-CoV and SARS-CoV-2 infection processes, but not MERS-CoV. Interestingly, the latter does not use ACE2 as a cell receptor [98, 99]. This observation is particularly interesting since other reports suggest that nilotinib can destabilize the SARS-CoV-2 spike-ACE2 complex [54]. According to our results, nilotinib might prevent the spike priming and activation since it showed the best energy scores against TMPRSS2 and cathepsin B at the active site cavities. These findings represent a potential inhibition of two independent priming pathways. Moreover, nilotinib potentially inhibits the Mpro and Rdrp complex and is consistent with previous *in vitro* and *in silico* results [55, 100].

Interestingly, nilotinib also had the best energy scores against PLpro. In addition to PLpro's essential protease activity in the processing of pp1a polyprotein, it is also implicated in host immune innate response evasion mechanisms as described in Section 4.4. The inhibition of PLpro decreases the exacerbated immune response, as described by other members of the Bcr-Abl inhibitors family, for example, ponatinib, which protects against cytokine storm in mouse models [101, 102].

#### **5.3 An isatin-derivative and telmisartan targeting SARS-CoV-2 entry**

Isatin-derivatives have shown potential antiviral properties, some of them with promising results against HCV, SARS-CoV [103, 104], and SARS-CoV-2 [46]. In particular, the compound 1-(naphthalen-2-ylmethyl)-2,3-dioxoindoline-5 carboxamide inhibits Mpro from SARS-CoV-2. Therefore, we decided to evaluate it against our whole set of targets. It presented the best score against the ACE2 active site, which might disrupt the spike-ACE2 interaction as discussed previously (see Section 5.1). Moreover, it also showed the best energy scores against the spike protein, precisely in the quaternary interface region of the homotrimer complex, and thus a plausible termination of the viral cycle at an early stage in the replication process.

*Repurposing Drugs as Potential Therapeutics for the SARS-Cov-2 Viral Infection… DOI: http://dx.doi.org/10.5772/intechopen.105792*

Telmisartan is an anti-antihypertensive. There is evidence of a morbidity and mortality reduction in hospitalized patients infected with SARS-CoV-2 treated with this drug [105]. Telmisartan showed the best energy scores against a spike's cavity in the homotrimer quaternary interface. Therefore, the isatin-derivative could inhibit two targets involved in the viral entry (spike and ACE2), while telmisartan might prevent the spike homotrimer formation and the Rdrp complex. We show the molecular visualization of these results in panels A, C, and G of **Figure 5**.

#### **5.4 Tegobuvir, qingdainone, and nafamostat targeting quaternary interface regions**

Tegobuvir is a non-nucleoside inhibitor of the NS5B polymerase of HCV. Our results suggest that this drug may prevent the formation of the Rdrp quaternary complex. Previously, *in silico* results reported tegobuvir as a potential inhibitor of Rdrp active site [106]. According to our data, tegobuvir did not achieve the selection criteria at the Rdrp active site. However, it shows a negative Z-score value at the NSP7-NSP12 interface region, which may compromise the RNA synthesis efficiency of the complex since its importance along with NSP8 for the Rdrp enzymatic activity [107]. Moreover, tegobuvir, nafamostat, and qingdainone presented the best binding free-energy change estimates on a cavity of PLpro in the vicinity of the interface of this target with ISG15, compromising an adequate immune response. In this manner, these drugs could avoid the formation of PLpro-ISG15 and the Rdrp quaternary complexes.

In addition, qingdainone also showed the potential inhibitory activity on Mpro active site, suggesting that this drug might completely disrupt the polyprotein processing stage by targeting both proteases, Mpro and PLpro. We show a molecular visualization of these results in **Figure 5**.

#### **5.5 Nafamostat and rac5c as potential inhibitors of PLpro and ACE2**

We included nafamostat and rac5c in our ligand sets due to evidence suggesting their inhibitory capacity against TMPRSS2 [108] and PLpro [19], respectively. Neither ligand achieved the selection criteria for their expected targets despite being on the borderline with scores of �8.3 and � 9.3 kcal/mol, which indicates the selection criteria's exhaustiveness. However, nafamostat does achieve the best scores against PLpro's USP domain, while rac5c presented the best score on the ACE2 active site. We show these results in panels A and F of **Figure 5**.

#### **6. Conclusions**

We have theoretically identified nine drugs or compounds for potential drug repurposing against SARS-CoV-2 through a cavity-based blind molecular docking protocol (**Figure 6**). Interestingly, seven of them present potential inhibitory activity on multiple targets at different stages of the viral infection cycle, including innate immune evasion. We have implemented an in-house high-throughput virtual screening pipeline that successfully reproduces experimental data and findings from previous works. After the target's cavity detection and ranking by surface area, we used the pipeline to perform the numerous independent blind molecular docking rounds to achieve a sufficiently extensive conformational target-ligand complex search.

#### **Figure 6.**

*Repurposing drugs (left) with corresponding potential inhibitory activity on multiple viral or host targets (right).*

Experimental design is a critical step in every scientific study, for example, method validation by including a control group. Nonetheless, one has to be wary of the limitations of the methodology employed. In this case, molecular docking can be a good estimator for the most energetically favorable *T* � *L* complex. However, the method does not explicitly consider solvent or thermodynamic parameters. Hence, molecular docking results should be taken as the input of other methodologies to further the study, for example, molecular dynamics.

We analyzed the molecular binding predictions through rigorous visualization and Z-score-based statistical algorithms to identify the potential drugs for repurposing. In this context, our findings suggest that:


*Repurposing Drugs as Potential Therapeutics for the SARS-Cov-2 Viral Infection… DOI: http://dx.doi.org/10.5772/intechopen.105792*

• Nafamostat and rac5c could be potential inhibitors of PLpro and ACE2.

These results are relevant in understanding the SARS-CoV-2 drug's molecular mechanisms and further clinical treatment development, either at a single or multitarget activity.

#### **Acknowledgements**

The authors acknowledge funding from the Consejo Nacional de Ciencia y Tecnología México [grant number 132376] and Fondo Sectorial de Investigación para la Educación [grant number A1-S-17041] to MCT. All computations and analyses reported here were performed at the bmdhpc computing resources of the Biomolecular Diversity Lab (tripplab.com) at CINVESTAV Unidad Monterrey, México.

#### **Conflict of interest**

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Abbreviations**



**Table A-1.** *VINA scores of the conformation with the lowest scores after 30 independent cycles of each target–ligand pair for the enzymatic known*

 *inhibitors included.*

**Appendix**

 **A**




*Molecular Docking - Recent Advances*

### **Author details**

Aldo Herrera-Rodulfo, Mariana Andrade-Medina and Mauricio Carrillo-Tripp\* Biomolecular Diversity Laboratory, Centro de Investigación y de Estudios Avanzados del IPN Unidad Monterrey, Apodaca, Nuevo León, México

\*Address all correspondence to: mauricio.carrillo@cinvestav.mx

© 2022 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.

*Repurposing Drugs as Potential Therapeutics for the SARS-Cov-2 Viral Infection… DOI: http://dx.doi.org/10.5772/intechopen.105792*

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### **Chapter 7**

N1-(3-(Trifluoromethyl)Phenyl) Isophthalamide Derivatives as Promising Inhibitors of Vascular Endothelial Growth Factor Receptor: Pharmacophore-Based Design, Docking, and MM-PBSA/ MM-GBSA Binding Energy Estimation

*Aliaksandr Faryna and Elena Kalinichenko*

### **Abstract**

Targeting protein kinases is a common approach for cancer treatment. In this study, a series of novel terephthalic and isophthalic derivatives were constructed as potential type 2 protein kinase inhibitors adapting pharmacophore features of approved anticancer drugs of this class. Inhibitory activity of designed structures was studied in silico against various cancer-related protein kinases and compared with that of known inhibitors. Obtained docking scores, MM-PBSA/MM-GBSA binding energy, and RF-Score-VS affinities suggest that N1-(3-(trifluoromethyl) phenyl) isophthalamide could be considered as promising scaffold for the development of novel protein kinase inhibitors which are able to target the inactive conformation of vascular endothelial growth factor receptor.

**Keywords:** terephthalic and isophthalic derivatives, anticancer activity, VEGFR, virtual screening, MM-PBSA/MM-GBSA, docking

### **1. Introduction**

Since its approval in 2001, imatinib has revolutionized drug therapy of chronic myeloid leukemia (CML) [1, 2]. Imatinib is a selective inhibitor of a specific protein – BCR-ABL tyrosine kinase, which biosynthesis is encoded by the Philadelphia chromosome, which is characteristic for all CML cells [3, 4]. High and uncontrolled activity of this protein leads to disruption of cell signaling causing a rapid growth of the tumor tissue. Imatinib has secured more than 80% 8-year overall survival rate in patients with CML, almost double compared to the previous drugs generation [5, 6].

The clinical success of imatinib has fueled an explosion in the protein kinase inhibitor research. The strategy of blocking signaling pathways mediated by an overexpression or deregulation of certain protein kinases has proven to be effective in treating many other cancers as well as some non-cancer diseases. More than seventy drugs of this class have now been registered, targeting dozens of various kinase targets, which constitutes about 10% of the total number of kinases encoded by the human genome [7, 8]. Besides BCR-ABL, another large group of drugs targets various growth factors receptors (epidermal, platelet, vascular endothelial, etc.) [9, 10].

The use of protein kinase inhibitors for cancer treatment has some limitations. First of all, an important problem is drug resistance in patients. Resistance can occur initially (primary resistance) or over time (secondary resistance) [11–13]. One of the key mechanisms of secondary resistance is the emergence of the mutants of the primary target, which appears with the disease progression. Binding affinity of an inhibitor to the mutant target is significantly lower. In some cases, such mutations completely block binding [14–19].

The second key consideration is inhibitor selectivity. Since all protein kinases accept ATP as a substrate, there is a high structural similarity between the active sites of different protein kinases. An inhibitor usually does not act exclusively on its main target but can suppress, to some degree, the activity of some or many other kinase targets. So, such multitargetness can be a positive (e.g. when cancer cells express several types of kinases) or a negative factor – side inhibition can be the cause of adverse effects [20, 21]. Selectivity modulation becomes even more problematic with the disease progression as it is accompanied by further genetic degradation of cancer cells [22, 23]. For example, in the case of CML, the optimal choice for a second-line therapy inhibitor between dasatinib, bosutinib, and nilotinib can be made based on a personalized assessment of the actual kinase overexpression profile [24].

Since the efficacy of treatment with protein kinase inhibitors depends significantly on the time of treatment initiation, the most important property of a drug is its actual inhibitory activity, including that toward mutant targets. For example, nilotinib, a second-generation structural analog of imatinib, has been initially considered as a second-line therapy option [25]. Further investigations have showed that this drug could be more effective than imatinib as a first-line therapy being a more potent inhibitor of BCR-ABL and its mutants [26, 27].

Therefore, the search for the novel highly effective inhibitors of therapeutically relevant protein kinases with a given selectivity and the ability to suppress mutant targets is still an important scientific challenge.

In this context, the recent advances in the development of molecular modeling techniques for the search of biologically active compounds cannot be overlooked. The literature describes cases of successful application of pharmacophore screening [28, 29], molecular docking, and molecular dynamics [30–32] to identify new chemical structures with anti-kinase activity. In addition, the improvements in technical and theoretical background of machine learning algorithms have made it possible to adapt them, inter alia, for the modeling of protein-ligand interactions [33–36].

The present work continues our previous studies on the design of novel potential protein kinase inhibitors using directed pharmacophore design and molecular modeling [37, 38]. In this case, the object of such studies is new derivatives of terephthalic and isophthalic acids, which are designed in a manner to give the structures significant pharmacophore similarity to known type 2 protein kinase inhibitors.

The potential anti-kinase activity of the designed terephthalic and isophthalic acids derivatives has been investigated by molecular docking, molecular dynamics, as well as by using machine learning model for virtual screening RF-Score-VS [39].

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

#### **2.1 Design of target structures**

X-ray diffraction data have revealed a number of common patterns in terms of binding of known protein kinase inhibitors to their targets. Two large groups of inhibitors can be distinguished. Type 1 inhibitors are direct ATP competitors and bind to the active center of the biologically active conformation of a protein kinase. Most of the approved inhibitors are type 1 inhibitors. However, in the case of imatinib, the binding is of a slightly different nature. The loop that links the two main lobes of BCR-ABL tyrosine kinase is flexible and in a certain position opens up an additional allosteric pocket adjacent immediately to the ATP binding site, thus extending the active center of the enzyme [40]. At the same time, the structure of the ATP pocket changes significantly, so it is unable to accept the natural substrate. Such inactive conformations can be seen for many others protein kinases. Inhibitors that bind to this inactive conformation of a protein kinase target are classified as type 2 inhibitors [41]. The described classification to this most common inhibitor classes is not perfectly strict, since there are stable intermediate kinase conformations with different volumes of allosteric pocket available and it is hard to classify ligand binding as type 1 or type 2 unambiguously [42].

In the structure of type 2 inhibitors, a number of key structural and pharmacophore features can be distinguished. Firstly, there is a benzamide fragment, most often with the 3-trifluoromethyl substituent in the benzene ring, which facilitates the formation of the necessary interactions, including hydrogen bonds, in the allosteric pocket of the active center. Secondly, the structure of type 2 inhibitors contains a heteroaromatic system, which in some sense imitates adenine but can form hydrogen bonds in the modified ATP pocket, which has been subjected to the structural changes upon the transition of a kinase to the inactive conformation. The relative orientation of these structural fragments is managed by the linker, which is usually represented by a benzene ring containing substituents in different positions [43–46].

In our previous studies, we have used the 4-methylbenzamide linker as a framework for constructing novel type 2 protein kinase inhibitors and that are allowed us to identify novel bioactive compounds with actual inhibitory activity against protein kinases [37, 38].

In this study, we have proposed that isophthalic and terephthalic acids transform into appropriate amides as a promising linkers for developing potential protein kinase inhibitors (**Figure 1**). In our opinion, the use of such linkers may be favorable for several reasons. For instance, these structures contain an amide bond, which is necessary for the formation of hydrogen bonds in the allosteric pocket of a kinase binding pocket. In addition, the overall size of linkers corresponds to those in the structures of known inhibitors. Moreover, the presence of a second carboxylic group may lead to the formation of hydrogen bonds in the ATP pocket. If compared to 4-methylbenzamide this linkers are more rigid, which may have a positive effect on kinase binding affinity. It is also important to note that we have used both isophthalic and terephthalic fragments to more fully study the conformational space of the linker

#### **Figure 1.**

*Pharmacophore features of approved type 2 protein kinase inhibitors and proposed structures. Structural fragments that bind to different regions of binding site are highlighted with red (ATP pocket), blue (allosteric pocket), and orange (linker). Interactions were obtained by PLIP [47].*

region. By varying the mutual arrangement of carbonyl groups, it could be possible to determine which linker is more suitable to be placed in a kinase's binding site.

On the basis of selected linkers, we have generated a library of novel chemical structures by introducing different amines into the carbonyl groups of phthalic acids *N1-(3-(Trifluoromethyl)Phenyl) Isophthalamide Derivatives as Promising Inhibitors… DOI: http://dx.doi.org/10.5772/intechopen.107236*

**Figure 2.**

*The generation scheme of studied phthalic acids derivatives. Letters a-h represent amine substituents. Letters I, T, and IT represent what type of linker was used for a structure: Isophthalic, terephthalic, or both.*

to study their potential anti-kinase activity by molecular modeling and molecular docking. A total of 28 unique chemical structures are generated (**Figure 2**). As substituents at carbonyl groups of phthalic linkers, we have used structural fragments of known inhibitors: 3-trifluoroaniline (nilotinib, ponatinib, and sorafenib), 4-(4-aminophenoxy)-N-methylpyridine-2-carboxamide (sorafenib), and other amines convenient in terms of commercial availability and possibility of further derivatization.

#### **2.2 Docking**

For molecular docking experiments, 3D structures of studied phthalic acid derivatives are generated using the Cactus service [48]. For docking studies, we have used open-source software AutoDock Vina [49] as Qvina 2.1 [50] modification.

The 3D structures of 33 cancer-relevant protein kinases are used as docking receptors. Their structures are obtained from the database of experimental X-ray data The Protein Data Bank (PDB) [51]. Most of the receptors are protein kinases of different families. Two receptors are poly (ADP-ribose)-polymerases as this protein class is also used for targeted cancer therapy [52] (**Table 1**).

Docking of the constructed ligands and receptors is performed using "each to each" scheme. Coordinates of active centers for Qvina are generated based on a visual assessment of the location of native ligands from PDB complexes with an increase of approximately 10–30% in each dimension. The Qvina search exhaustiveness parameter is set to 24. The preparation of receptors and ligands for the docking has been performed using Chimera 1.13.1 [54].


*N1-(3-(Trifluoromethyl)Phenyl) Isophthalamide Derivatives as Promising Inhibitors… DOI: http://dx.doi.org/10.5772/intechopen.107236*


*\*\*A ligand is a covalent inhibitor – it binds to the receptor by forming a chemical bond [53].*

#### **Table 1.**

*Receptors used for the docking studies.*

#### **2.3 Molecular dynamics**

After the docking step, the most promising protein-ligand complexes have been subjected to molecular dynamics simulation for more accurate binding affinity estimation. The complexes for the simulation are selected based on the obtained docking scores. The open-source GROMACS 2019.1 [55] software is used to conduct molecular dynamics experiments. The standard molecular dynamics protocol includes a minimization step, two 200 ps equilibration steps, and a final 2 ns simulation. The resulting molecular dynamics trajectory is used to estimate the binding energy, which is performed in three ways. All ligands are parameterized by Acpype [56]. Complete md-protocol is described in previous work [37].

The first two calculation methods include the implementations of the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanicsgeneralized Born surface area (MM-GBSA) [57]. These methods are widely used to estimate inhibitory activity for protein-ligand complexes. Their main advantage is the relatively high accuracy of obtained results along with a simpler system setup procedure if compared to the thermodynamic integration or free energy perturbation methods [58]. A relatively short simulation time is chosen based on the published evidence that the accuracy of the MM-PBSA/MM-GBSA protocols is in many cases are independent of simulation time, and in some experiments a short simulation time is preferable [59].

In our case, the MM-PBSA/MM-GBSA binding energy calculation has been carried out using two kinds of softwares: g\_mmpbsa [60] and gmx\_MMPBSA [61]. The main difference between these programs, apart from the technical implementation, is that g\_mmpbsa only calculates the Poisson-Boltzmann surface area (PBSA) variant, whereas gmx\_MMPBSA allows to also using the generalized Born surface area (GBSA) and also provides entropy change estimation.

The third approach used provides the estimation of the binding affinity of the studied phthalic derivatives applying the RF-Score-VS (Random Forest-based scoring function for Virtual Screening) machine learning algorithm [39]. This algorithm uses a "set of decision trees" model trained on a large set of active and inactive docking poses. The main purpose of RF-Score-VS is to refine the estimation of docking results. In training procedure for this model, a set of deliberately inactive ligands are used aimed to increase the probability of distinguish real "hits" between the structures with the highest scores. This is what makes RF-Score-VS different from many other rescoring protocols, including RF-Score v3 [62] from the same authors, which are focused on more accurate numerical estimation of binding energy for known ligands. According to the published data [39], the RF-Score-VS model is significantly superior

to the AutoDock Vina scoring function in terms of the probability of finding a real inhibitor. In our study, we have extended the scope of RF-Score-VS uses by applying it not to the obtained docking pose, but to the frames of the resulting molecular dynamics trajectory. In our opinion, this approach can be more accurate as it takes into account time-dependent changes of the protein-ligand complex reflected by the simulation. At the same time, the computing expenses remain acceptable.

In all three methods, we did not use the full 2-ns-long trajectory of the complex, but every 20th frame skipping first 200 ps of the production run.

#### **3. Results and discussion**

After docking stage, we obtained 924 complexes of the studied structures along with the corresponding docking scores representing binding energy estimation. In order to study any binding patterns, the resulting docking poses were filtered based on their binding energy. Docking scores better or equal to −11.5 kcal/mol were used as a threshold for filtering. This threshold was chosen based on our previous experience. After filtering, we obtained 133 docking poses out of 924 that showed such a high binding energy. We investigated then the distribution of filtered docking poses by linker type (isophthalic or terephthalic), by the most frequent amine fragments and by receptor type.

Out of 133 poses with high docking scores, 101 poses corresponded to the structures containing an isophthalic linker; therefore, 22 poses belonged to the structures having terephthalic linker. This ratio remained virtually unchanged when the filtration threshold was increased: 63/12 for the threshold of 12.0 kcal/mol and better, 30/8 at 12.5 kcal/mol, and 20/5 at 13.0 kcal/mol.

The distribution of amine substituents in high-scoring docking poses is shown in **Figure 3**. Amines containing 3-trifluoromethylaniline are the most frequent.

The most frequent receptors in protein-ligand complexes with a score of −11.5 kcal/ mol and better are trkc kinase (PDB: 6kzd), abl family (PDB: 3cs9, 2hyy), and vegfr family (PDB: 3hng, 3wze, 4asd), as shown in **Figure 4**. It is important to note that all of these receptors are essentially protein kinases being in inactive conformation accepting type 2 ligands, which indirectly confirms the correctness of the chosen approach to the design of studied phthalic derivatives.

The obtained docking results indicate that the isophthalic linker, together with the attached 3-trifluoromethylaniline, might be a promising structural fragment in terms of its ability to bind to protein kinases as type 2 inhibitor.

At the second stage, we selected 25 complexes of the studied structures that were obtained during the docking step to refine ligand binding energies using molecular dynamics methods. The complexes for molecular dynamics simulation were chosen based on their docking score and to get a certain degree of diversity in chosen linkers and receptors. Out of 25 complexes, seven had terephthalic linker and 18 contained isophthalic linker.

After conducting a 2-ns simulation for each complex, we calculated the binding energy via processing the obtained trajectory frames using three methods: MM-PBSA (g\_mmpbsa), MM-GBSA (gmx\_mmpbsa), and rescoring with the RF-Score-VS scoring function. The last is based on a machine learning model (**Table 2**).

It was of particular interest for us to compare the results obtained by three methods of binding energy estimation. In our case, the values of electrostatic and van der Waals interactions obtained by g\_mmpbsa (MM-PBSA) and gmx\_mmpbsa *N1-(3-(Trifluoromethyl)Phenyl) Isophthalamide Derivatives as Promising Inhibitors… DOI: http://dx.doi.org/10.5772/intechopen.107236*

#### **Figure 3.**

*Frequency of different amine fragments appearing in docking poses with a score of −11.5 kcal/Mol and better.*

**Figure 4.** *Distribution of docking poses with a score of −11.5 kcal/Mol and better by receptor type.*

(MM-GBSA) are in strict linear correlation with each other (**Figure 5**), which indicates that the methods for calculating the molecular-mechanical component of binding energy in these two tools are uniform.

When taking into account the solvation component, the correlation between this two methods decreases but remains high with the correlation coefficient R<sup>2</sup> = 0.76. The decrease in correlation can be naturally explained by the differences in the estimation of the solvation component of binding energy applying the Poisson-Boltzmann surface area and the generalized Born surface area. When the entropic component of gmx\_mmpbsa is added, the correlation coefficient decreases slightly more but remains high (R2 = 0.66). Thus, in general, both used programs show similar results for the same complexes.

We also compared the results obtained from MM-PBSA/MM-GBSA calculations with those of RF-Score-VS machine learning algorithm. The RF-Score-VS values moderately correlated both with the g\_mmpbsa (R2 = 0.50) and gmx\_mmgbsa (R2 = 0.51)



**Table 2.** *Calculated binding affinities of studied and reference structures to their receptors.*

### *N1-(3-(Trifluoromethyl)Phenyl) Isophthalamide Derivatives as Promising Inhibitors… DOI: http://dx.doi.org/10.5772/intechopen.107236*

#### **Figure 5.**

*Correlations between binding affinities obtained by different approaches.*

final scores. It is noteworthy that the correlation between RF-Score-VS values and the van der Waals component of MM-PBSA/MM-GBSA binding energy is quite high (R2 = 0.73) and extremely low for the electrostatic component (R<sup>2</sup> = 0.13).

All used methods for binding energy estimation are known to be more efficient for the relative ranking of potential inhibitors than for the precise calculation of absolute binding energy. Therefore, we have used known inhibitors as reference structures. In most cases, the studied phthalic derivatives showed worse binding energy scores

#### *N1-(3-(Trifluoromethyl)Phenyl) Isophthalamide Derivatives as Promising Inhibitors… DOI: http://dx.doi.org/10.5772/intechopen.107236*

compared to known inhibitors. The latter, in turn, were characterized by relatively high binding energy scores regardless the applied method for the calculation. Among the known inhibitors, the highest RF-Score-VS scores were observed for nilotinib (PDB id: 3cs9). Extremely high MM-PBSA/MM-GBSA energies were obtained for the native ligand of trkc kinase complex (PDB id: 6kzd). In the case of abl-protein kinase, nilotinib, being a second-generation inhibitor, showed higher estimated activity compared to the first-generation drug imatinib.

Among the studied phthalic acid derivatives, two structures can be distinguished which showed high binding energy scores calculated by all three methods. Both of these structures are isophthalic acid derivatives and contain a 5-imidazolyl-3-trifluoraniline fragment of nilotinib. The second carboxyl group in these structures is modified by 4-(4-aminophenoxy)-N-methylpicolinamide **a** (sorafenib fragment) and (2-fluorophenyl) (piperidin-1-yl) methanone **h**, respectively. If compared to known inhibitors, high *in silico* inhibitory activity of these structures was observed for vegfr receptors (pdb ids: 4asd, 4ag8, 3wze) and, to a slightly lesser extent, for abl (3cs9).

Several complexes of two aforementioned structures have been subjected to hydrogen bonds analysis. For the frames of the molecular dynamics trajectory, hydrogen bonds are searched using GROMACS hbond module. The frames with the highest number of hydrogen bonds have been visualized. Visualization shows that this structures bind to the active center similar to known type 2 inhibitors: the 3-trifluoromethylaniline fragment occupies the allosteric pocket and the isophthalic acid fragment plays a linker role. In both cases, the allosteric amide bond forms two hydrogen bonds with amino acid residues of asparagine and glutamine, which is typical for type 2 inhibitors (**Figure 6**). Regarding the ATP binding site, our analysis shows that the carbonyl group of phenyl (piperazin-1-yl) methanone may be involved in hydrogen bonding. In the case when 4-(4-aminophenoxy)-N-methylpicolinamide is located in this region, hydrogen bonds can be formed by oxygen atoms of phenolic and carbonyl groups. Hydrogen bonds of the non-allosteric amide bond of the phthalic linker have not been detected.

#### **Figure 6.**

*Structure of most promising structures and the visualization of their binding to receptors. The binding of 3-(4-(2-fluorobenzoyl)piperazine-1-carbonyl)-N-(3-(4-methyl-1H-imidazol-1-yl)- 5-(trifluoromethyl)phenyl) benzamide to vegfr is shown on the left (PDB id: 3wze, h-bonds: Cys-106, Asp-183, Glu-72, Arg-164. The binding of N1-(3-(4-methyl-1H-imidazol-1-yl)- 5-(trifluoromethyl)phenyl)-N3-(4-((2-(methylcarbamoyl)pyridin-4-yl)oxy)phenyl) isophthalamide to vegfr is shown on the right (PDB id: 4asd, h-bonds: Cys-151, Asn-155, Asp-228, Glu-117).*

#### **4. Conclusions**

In this study, 28 unique chemical structures of new derivatives of terephthalic and isophthalic acids have been studied. These structures are designed in such a way as to give the structures a significant pharmacophore similarity with known type 2 protein kinase inhibitors. Three-dimensional structures of 33 protein kinases associated with cancer have been used as docking receptors. At the same time, most of the receptors represent protein kinases of different families. The obtained docking parameters, the binding energy of MM-PBSA/MM-GBSA, and the affinity of RF-Score-VS suggest that the isophthalic linker together with the attached 3-trifluoromethylaniline may be a promising structural fragment in terms of its ability to bind to protein kinases as a type 2 inhibitor. In comparison with known inhibitors, high inhibitory activity of isophthalic structures in silico are observed for vegfr (pdb ids: 4asd, 4ag8, 3wze) receptors and to a somewhat lesser extent for abl (3cs9). If compared to known inhibitors, high in silico inhibitory activity of these structures was observed for vegfr receptors (pdb ids: 4asd, 4ag8, 3wze) and, to a slightly lesser extent, for abl (3cs9). At the same time, the use of terephthalic acid for this purpose is ineffective. The most promising structural fragment is 1-[3-(trifluoromethyl)phenyl]benzene-1,4-dicarboxamide. By introducing different substituents to the free amino group to this structure, the anti-kinase activity of the obtained chemical compounds can be expected.

#### **Acknowledgements**

This research was funded by the National Academy of Sciences of Belarus within the research project number 2.3.2.1 of State Program for Scientific Research "Chemical basis of life processes (bioorganic chemistry)."

#### **Author details**

Aliaksandr Faryna\* and Elena Kalinichenko Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, Minsk, Belarus

\*Address all correspondence to: farina@iboch.by

© 2022 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.

*N1-(3-(Trifluoromethyl)Phenyl) Isophthalamide Derivatives as Promising Inhibitors… DOI: http://dx.doi.org/10.5772/intechopen.107236*

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## *Edited by Erman Salih Istifli*

Molecular docking is a widely used bioinformatics method in biology, medicine, and biochemistry. This method, which can model interactions between different receptors and their various ligands at the molecular level, can represent intermolecular interactions at an unprecedented resolution that may not be achieved by classical experimental approaches. This book describes different aspects of this method that can reveal the intermolecular biochemical and biophysical interactions and the affinities of partner molecules to each other. It is designed for academics, students, and professionals interested in this technique.

### *Robert Koprowski, Biomedical Engineering Series Editor*

Published in London, UK © 2023 IntechOpen © blackdovfx / iStock

Molecular Docking - Recent Advances

IntechOpen Series

Biomedical Engineering, Volume 15

Molecular Docking

Recent Advances

*Edited by Erman Salih Istifli*