MOLECULAR DOCKING IS AN IDEAL TOOL IN PHARMACEUTICAL DRUG DEVELOPMENT OF ANTI-TUBERCULOSIS MOLECULESHTML Full Text
MOLECULAR DOCKING IS AN IDEAL TOOL IN PHARMACEUTICAL DRUG DEVELOPMENT OF ANTI-TUBERCULOSIS MOLECULES
Fathima Asra, Naresh Dumala *, Fathima Ajra, Shaik Mallika and Kannan Vadivel
Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
ABSTRACT: Advancements in X-ray crystallography, Nuclear magnetic resonance (NMR) spectroscopy, and innovation projects like the human genome project left gigantic amounts of biological information accessible to the common public, academicians, and industries. Computer-Aided Drug Design (CADD), such as molecular docking, is an ideal method to exploit this biological information in rapid drug development and discovery. The basic principle of molecular docking is explained in the present review, with a brief introduction to bioinformatics and Mycobacterium tuberculosis. Methodology and types of molecular docking are also described along with various software and programs used viz. AutoDock, FRED, FlexX and GOLD. Further, two fundamental wings of molecular docking techniques i.e., searching algorithms and scoring functions, are briefed. Molecular docking tools are discussed as their applications in the anti-tuberculosis drug discovery process. Drug molecules targeting various cellular enzymes important for the viability of multi-drug resistant M. tuberculosis studied using in-silico docking methods are reviewed. In contrast to high-throughput screening, molecular docking methods speed up the drug discovery process. It has been concluded that molecular docking studies significantly take part in novel drug development by rapid virtual screening of existing drug databases and in the discovery of new drugs which are fundamental to pharmaceutical industries and medical science to overcome the emergencies situations like the emergence of drug resistance in medical pathogens and changing disease scenarios.
Keywords: Tuberculosis, Drug-resistance, Mycobacterium tuberculosis, Bioinformatics software, Molecular Docking, Docking tools, Genomics, Proteomics, AutoDock, Drug discovery
INTRODUCTION: Extensive research in biological science generated a gigantic amount of scientific information related to genomics, proteomics, metabolomics, drug-target interactions, etc. Managing such huge scientific information manually has become a challenging task. For instance, the DNA sequence obtained from the human genome project, if printed, may need approximately 100 volumes of telephone dictionary to demonstrate the whole genomic information 1.
Hence, exploiting this scientific information for human applications is only feasible by adopting automated techniques. Bioinformatics is a field of life science that uses the computer for the computation of biological information, and it is also referred to as computation biology.
It is an interdisciplinary arena that enables the exploration of a huge amount of biological data by storing, organizing, annotating, systematizing, mining, interpreting, and understanding the complex volumes of scientific data as shown in Fig. 1. The field utilizes modern, conventional computer science, mathematics, statistics, cloud computing, machine learning, folding algorithms / molecular modeling, iterative and simulation approaches 2.
FIG. 1: EMERGENCE OF BIOINFORMATICS FROM THE CONVERGENCE OF VARIOUS FIELDS OF SCIENCE 3
Important bioinformatics tools mainly include an internet facility and computer software programs, mostly available on public websites. The basic activity of bioinformatics includes analysis of DNA sequences and proteins with the help of the World Wide Web available software programs and databases3. The computer programs and software developed are used to handle much biological information on genome (DNA, RNA), metabolites, and proteins. These bioinformatics tools also have immense applications, as indicated in Fig 2.
FIG. 2: BIOINFORMATICS APPLICATIONS IN DIFFERENT FIELDS OF BIOLOGICAL SCIENCES 4
As far as applications for computational biology or bioinformatics in drug discovery and design is concerned, it is a key component that saves researchers time and reduces the cost of research 5. Since, drug isolation and characterization is dated back to ancient times, tremendous traditional and advanced research in this area accumulated a huge drug database. Today, the count of pure drugs with known structures has touched near tens of millions, and this massive data is fundamental for drug development and research. Scrutinizing such a huge drug depository employing traditional experimental models and pharmacology is enormously expensive and time-consuming.
The CADD (computer-aided drug design)tool of bioinformatics plays a substantial role in drug design, and development of drug leads through computer-mediated simulation, prediction, and determination of ligand (drugs) and target (receptor) interactions as a majority of biochemical processed in living organisms rely on the interactions of ligand and proteins. This computational technique being an important tool in drug research and development, critically enhances the rate of successful drug screening, avoids blindness research, and is more economical and less time-consuming 6, 7. Molecular docking is a computational method used for studying ligand and target protein fitness at the atomic level. The method is one of the significant tools in drug discovery, which enables the prediction of the small molecule behavior at the active site of targeted proteins and the understanding of the basis of biochemical reactions.
Mycobacterium tuberculosis is a bacterium that causes a communicable disease, Tuberculosis (TB) by mostly attacking the lungs, followed by other parts of the body in low-income nations. Among the top ten diseases, tuberculosis is one of the leading causes of death over the globe. World Health Organization (WHO) estimates about 1.4 million deaths due to TB, and infection is even more severe in HIV (Human Immunodeficiency Virus) patients 8, 9. TB is being managed successfully with current medications, but it still poses several threats to the healthcare system. Amidst these, a few are drug resistance TB like XDR-TB (exclusively drug-resistant-TB), MDR-TB (multi-drug resistance-TB), TDR-TB (totally drug-resistant-TB), the risk of diabetes mellitus development in TB patients, and co-morbidities associated with AIDS (Acquired Immunodeficiency Syndrome) patients 10, 11, 12. Despite intensive anti-TB research in the last 40 years, the existing drugs for TB are found to be less effective and cytotoxic against drug-resistant bacteria 13. The recent approach to developing the drug candidate against TDR, MDR, and XDR-TB has resulted in the discovery of a limited number of therapeutic agents, such as telacebac or Q203 and TBA-7371, which are potential drugs for the treatment of XDR and MDR-TB 14.
Thus, there is an emergency in developing a potential multi-targeting drug that can bind to different biological targets. Since the approach targets different protein targets, modern techniques like molecular docking methods intensify the processes by advancing the prediction of these possible drug-binding targets in contrast to traditional methods. Present review focus on the detailed discussion of molecular docking techniques for drug discovery with special emphasis on drug development using bioinformatics tools concerning anti-TB molecules.
Molecular Docking and its Principle: The interaction of protein-protein and protein to small molecules are fundamental to the existence of life through the production of energy and biomass. Biochemical processes anticipating anabolic and catabolic pathways form a metabolic network mediated through the interaction of enzymes (protein target) and substrate (ligand). During the disease, these interactions are often dysregulated. The protein-protein interaction presents both inside and outside the cells is an important target for therapeutic agents 15. Hence, protein-to-protein interactions are important regulatory events in physiology and pathology that are critical targets for small molecules in the drug development process 16. However, proper orientation of protein(s) and ligands is essential for interactions to take place, which can be achieved by using advanced molecular docking techniques. Hence, molecular docking is a technique to determine the appropriate orientation of ligands and target proteins using the computational method to determine the proper ligand fit with the receptor 17. Accurate orientation of ligand-protein is accomplished by scoring functions that indicate the proper fit of ligand into the target protein’s site both geometrically and energetically 18. Molecular docking involves two interrelated steps: ligand sampling conformation in the protein target site, i.e., active site, and grading ligands conformation based on scoring function 19. On the other hand, the ligand binding conformations are predicted using search algorithms and the binding energies (7 - 10 kJ/mol) between protein and ligand docking are predicted through scoring functions 20. The basic principle of molecular docking can be explained better through Fisher's Lock-Key model introduced in 1894, shown in Fig. 3. The model proposes that the protein and ligand, via energy matching and geometric matching, could identify each other21. In this model, both ligand and protein are considered rigid structures where no change in the structure of receptor proteins is expected. This limitation of the Lock-Key model was instigated in the proposal of Induced Fit Theory, which suggests that during docking studies, the structure of ligands and proteins are considered flexible 22. The theory was validated by applying it to the drug-protein interactions resulting in more accurate results.
FIG. 3: COMPARISON OF PROTEIN CONFORMATION IN LOCK AND KEY MODEL (A) AND INDUCED-FIT THEORY (B) 23
The various types of interaction forces or energy that mediate ligand and proteins interaction include Electrodynamics forces or Van der Waals interaction energy, electrostatic forces or Electrostatic interaction energy, steric forces or solvation change the energy and hydrophilic and hydrophobic interactions, and covalent bond 24, 25, 26.
Workflow of the Molecular Docking: The main objective of molecular docking is to analyze ligands for proper orientation or conformation (also called “pose”) so they can bind to a protein target. Therefore docking protocol consists of two steps; sampling conformation of ligand that can bind to the active site of the protein target using searching algorithms and assigning as the core for ligand conformations via scoring functions 27. Searching algorithms generate several conformations, including the experimentally binding mode assigned to the highest score by scoring functions. Some search algorithms include Monte Carlo, Fragment-based, Point complementary, distance geometry, systematic searches, etc 28, 29. The primary requirement of molecular docking studies is to retrieve 3D structures of both ligand and target macromolecules viz., protein, DNA, or RNA, from online available data banks. The 3D structures of macromolecules are obtained from PDB (Protein Data Bank) 30, 31. Meanwhile, the structures of ligands are retrieved from various resources like Pub Chem and ZINC, and they can also be obtained by drawing the Chem Sketch tool. Therefore, the steps mentioned below are the major ones involved in docking.
Preparation of Target Protein: as described, the 3D structure of proteins obtained from PDB is pre-processed based on the parameters available to form the stable ligand-protein complex. These protein preparations include optimization of hydrogen bonds, addition/removal of hydrogen bonds, elimination of atomic clashes, water removal from protein cavity, the addition of side chain, missing residue fillings and charges stabilization 32.
Prediction of Protein (Target) Active Site: active site of the protein is directly anticipated in the binding with ligand to from ligand-protein complex. Hence, before docking, this active site is predicted by its modification, like removing heteroatoms and water molecules. The receptor might possess many active sites in which the appropriate one can be chosen 33.
Preparation of Ligand: ligands obtained from different databases must be prepared to get conformation that can bind to the receptor. Ligand’s pre-preparation should be according to the “Lipinsky’s Rule of 5” in order 34.
The rule proposes that the drug possesses more than 5 hydrogen bond donors and 10 hydrogen bond acceptors with CLog P >5.0 (calculated Log P), and a molecular mass of more than 500 is more likely to get adsorb and permeate. The rule is used to identify the difference between drug-like and non-drug-like molecules.
Molecular Docking: The ligand with different conformations is docked with the protein target's active site, and their interaction is analyzed. The scoring function assigns the score for the best ligand-protein complex docked and refers to Fig. 4.
FIG. 4: MOLECULAR DOCKING PROCEDURES FOR DOCKING SCORE CALCULATION TO OPT FOR BEST LIGAND-PROTEIN COMPLEX 35
Though, if such a situation arises where the 3D structure is not available for few protein, the 3D structure of these proteins can be obtained using computational prediction methods viz., ab initio, prediction and comparative modelling 27. Similarly, the 3D structure of the ligand is not found after the virtual screening, it can be obtained from the 2D structure with the help of software Concord, Avogadro, Chem Draw, Chem Sketch, etc 36, 37.
Software Available for Molecular Docking: Two important sections of molecular docking are search algorithms and scoring functions accountable for the prediction of the ligand conformation and assigning scores for the ideal ligand-target complex, respectively, using computational techniques, and together these functions accomplish the molecular docking process 38. The detailed discussion of search algorithms, scoring functions, and their corresponding available software are explained below.
Search Algorithms: these algorithms predict the ligand conformation or orientation, also known as posing 39. Search algorithms should be able to generate the optimum number of ligand orientations that is useful in practically determining binding modes. Considering the internal and roto-translational degree of freedom, these algorithms analyze and generate the ligand conformation (pose) at the protein target site 30. Several search algorithms used in molecular docking studies are listed in Table 1. Search approaches are further categorized as deterministic, stochastic, and systematic search algorithms. Deterministic search utilizes the previous state in the determination of conformation and orientation of ligand in each iteration and compares it to the previous one; the new state has a lower or equal energy value. But, the cost of computation is higher in this type of algorithm. Sometimes it results in the trapping of ligand conformation unenviably to a local energy minimum 40, 41.
Simulations of molecular dynamics and methods of energy minimization are a few examples of this kind of algorithm. Through the Stochastic search algorithm, the degree of freedom of the ligand can be changed randomly without promising convergence to the ideal solution, and it can be improved by conducting an iterative process. Evolutionary algorithms, Monte Carlo, Swarm Optimization, and Tabu Search are some commonly implemented stochastic search algorithms 41. The systematic search algorithm uses the degree of freedom of each ligand incrementally, and with increased free rotational bonds, there will be an increased number of evaluations experiencing combinatorial explosion. These search algorithms are further sub-categorized into the combinatorial ensemble, incremental and exhaustive construction 40, 41, 42.
Scoring Functions: Soon after the thousands of ligand conformation are predicted, they are raked using scoring functions. The scoring is based on the free energy, qualitative numerical measures of binding energy, and interactions energies 43. Scoring functions are classified into different types based on classical force-field, empirical, and knowledge.
Classical Force-flied-based Scoring Functions: this scoring function measures binding energy by determining the sum of non-bonded interactions, including vander Waals and electrostatic forces. For the calculation of binding energy, in a few algorithms, parameters like hydrogen bonds, salvations, and entropy contributions are taken into consideration 44. Coulombic formulation and Lennard-Jones potential function are used to calculate the electrostatic and vander Waals terms, respectively. Such calculation provides information about protein environment modeling based on charge-charge interaction and acceptability of the close contact of protein-ligand 45, 46. This scoring function can be further refined in docking studies using techniques like free-energy perturbation methods (FEP) and linear interaction energy 47, 48. These functions are low-speed computational methods that also decline the precision of the long-range bonding effect. The effect of entropies and solvents is neglected in this type of function 40. The example for this kind of function employs the DockThor program to predict a pose.
Empirical Scoring Functions: these functions are idealized by Hansh and Fujita and are derivative of the quantitative structure-activity relationship 40. The main objective is to predict high-precision binding affinity with the help of well-understood investigational data on binding affinity 41. These functions have simple energy terms to calculate. Some of the empirical scoring functions include Glide score and Chemscore. In these functions, various energy components like hydrophobic effect, ionic interactions, and hydrogen bind together to contribute the binding energy. Different software is used to treat each term differently in these functions, and even in different algorithms, the number of terms included is also different 49.
Knowledge-based Scoring Functions: this function is computationally simple and mainly used to screen large molecule databases. The atom pair’s interactions frequency noticed in practically determining 3D ligand-target complexes is fundamental to this type of scoring function. Some examples of this category of functions include PMF and FlexX program DrugScore 40, 41. Calculating the score is done by considering penalizing revolting interactions between protein and each ligand atom and preferring selected contacts within recommended cutoff. Consensus scoring is an advanced method in docking analysis that predicts the docking conformation by combining different scores. The acceptance criteria of a potential binder or ligand pose are only possible when it attains a good score under several scoring strategies 50.
Types of Docking Methods: Docking methodologies are categorized into several types based on the structure of ligand and proteins target (receptors).
Rigid Molecular Docking (Both Ligand and Protein are Rigid Structure): in this type of docking, protein and ligand are considered fixed in their spatial orientation and only the posture and spatial position of two molecules will change51. The search space in this kind of docking is restricted taking account of three rotational and translational degrees of freedom.
Through this docking, a huge number of ligand conformations are created with suitable surface complementarity and re-ranked with the help of free energy of approximation. The docking technique is simple in calculation amount as well as calculation difficulty; hence is more useful in macromolecule docking methods like protein-nucleic acid and protein-protein complexes. The docking method was successfully applied in maltose-protein docking simulation by binary docking technique 52. The docking tool MEGADOCK, identical to ZDOCK is used to produce docking conformations that generate docking conformations using Fast Fourier Transform (FFT) in a grid-based 3D space. However, calculations in contrast to ZDOCK are 8.8 times greater as the score functions are simpler where only electrostatic and shape complementarities are taken into account 53.
TABLE 1: BIOINFORMATICS SOFTWARE USED IN MOLECULAR DOCKING STUDIES
|Software||Features & Applications||Search Algorithm||Scoring function||Designed company|
|AutoDock 54||Rigid-Flexible docking; Used in the analysis of ligand covalent-bound||Lamarckian Genetic Algorithm (LGA) Genetic algorithm (GA)||Force-field methods||D. S. Good sell and A. J. Olson The Scripps Research Institute|
|FlexX 28, 55||Rigid-Flexible docking; easy operation, high efficiency, rapid speed docking, suitable for small molecule virtual screening||Fragmentation algorithm||PLP, Drug Score Screen Score, Flex X Score,||M. Rarey Bio Solve IT and T. Lengauer|
|DOCK56, 57||Flexible ligand-receptor docking; Step-by-step geometric matching strategy||Fragmentation algorithm||GB/SA solvation scoring, Chem Score, other||I. Kuntz University of California, San Francisco|
|Flexible docking; utilized for virtual screening of database, evaluated for reliability & accuracy in the docking simulation||Genetic algorithm (GA)||Chem Score, Gold Score||Crystallographic Data Centre, Cambridge|
|Rigid body Docking; In protein active site possible conformations are examined by a Nonstochastic approach||Shape fitting (Gaussian)||PLP, Screen Score, Gaussian shape score, user define||Open Eye Scientific Software|
|Glide 59||Flexible docking;
High throughput virtual filter, standard precision, extra precision search algorithms are fundamental to the Docking program
|Exhaustive systematic search||Glide Comp, Glide Score||Schrödinger Inc.|
|Shape-directed rapid docking; Good hit rates are generated using Lig Score||Monte Carlo Sampling||PMF, PLP, Lig Score||Accelrys Inc.|
Note: GOLD; Genetic Optimization for Ligand Docking, FRED; Fast Rigid Exhaustive Docking
Rigid Protein and Flexible Ligand Docking (Semi-flexible Docking): To overcome the difficulty of docking where both ligand and target are considered flexible which is costly and energy perfect-fit complex is minimum semi-flexible docking approach is used to balance between computational time and accuracy. This methodology is adopted by the majority of the programs like Dock, FlexX, and AutoDock 61, 62. During the calculation process of semi-flexible docking, receptor protein conformation is maintained, and non-critical parts like bond angle and length are changed. Due to the model's prediction and calculation ability, the method is extensively used in macromolecules (enzymes, nucleic acid, proteins) and small-molecule docking simulation 42. AMBER force field viz., desolvation, conformational entropy, hydrogen bonds, vander Waals, and electrostatic interactions based scoring function is used in this docking method. The empirical scaling factor acquired from the investigational database is used to weigh each of these terms. Using AutoDock 4.0 the protein-protein interaction docking interactions can be evaluated, and the latest version for virtual screening and molecular docking is AutoDock Vina 63.
Flexible Molecular Docking (Both Ligand and Receptor are Flexible): The orientation of ligands and receptors freely changed during the calculation of flexible docking. It is a highly accurate docking simulation nearest to the real docking condition, and this method can accurately investigate the recognition between molecules. But, due to variables’ geometric growth in terms of the number of atoms, the method is time-consuming, computationally intensive, and needs high-level computer hardware and software system. The most popular software for molecular docking of this kind is FlexX 64.
Docking Tools Available in TB Drug Design: As described above, a continued search for drug molecules in a limited time is essential due to drug resistance and aggressiveness of the TB disease. Isolation of a broad-spectrum potential drug active against TB through experimental trials is very difficult and time-consuming, and the results are less promising. Hence, molecular docking enables rapid screening of molecules from a drug database in isolation of a broad spectrum of potent anti-TB drug molecules. Some of the molecular docking studies involved in the virtual screening of drugs against tuberculosis reported in the literature are discussed here.
Accelrys Discovery Studio 4.0 was used in the molecular docking of indolizines into the target protein’s active site, as shown in Fig. 5. The mycobacterial enzymes trpD (anthranilate phosphoribosyltransferase) and InhA (enyol-ACP-reductase) X-ray crystal structures with their inhibitor were recovered from PDB (Protein Data Bank). The clean protocol tool is used in preparing crystal complexes. Parameters like atoms names, removal of water molecules, bond order and correct connectivity, ionisable residues protonation at pH 7.4 and addition of missing residues in protein were standardized. The docking protocol was validated by co-crystallization of ligand and de-docking into enzyme’s active site to ensure proper binding site definition and evaluate docking algorithm accuracy in generating co-crystallized ligand pose. Ligand conformations are generated by docking the ligand in rigid receptors.
In targeting the active binding site, the optimal ligand pose was ensured by using extra scoring functions PMF, Jain, PLP1 and PLP2. Their negative score indicatesthe strongest ligand-receptor binding affinity. Docking of indolizines in respective receptor targets and binding energy calculation was conducted using the C-Docker protocol and procedure of in-situ ligand minimization. In-silico docking indicated indolizines is potential drugs for trpD and InhA targets with no toxicity. Hence, indolizines are promising inhibitors of InhA activity against multidrug-resistant TB strain 65.
FIG. 5: PREDICTION OF INDOLIZINES–INHA BINDING DOMAIN INTERACTIONS. INHA – PDB 5G0S, CYAN: RECEPTOR, YELLOW: NAD (NICOTINE DIAMINE DINUCLEOTIDE), AND SOLMON: LIGAND, GREEN DOTS: HYDROGEN BONDS CONTACT
Twenty Benzimidazole derivatives' efficiency was initially evaluated for their anti-tuberculosis activity using the AutoDock Vina algorithm, further enhanced by Glide algorithm-mediated redocking. Molecular Docking of Benzimidazole ligands indicated hydrogen bond formation and strong binding affinity to the active site of PrpR, an M. tuberculosis protein in which amino acids residues like GLY189, LEU190, ARG308, VAL312, and LEU403 are anticipated binding of benzimidazole ligands. The results revealed the derivatives of benzimidazole are potent anti-tubercular molecules in contrast to the standard drug isoniazid 66.
A molecular docking study of 357 structural analogs of Azole drugs inhibits the CYP121 proteins of M. tuberculosis using CDOCKER (Discovery Studio, 2.0) and Ligscore 2, PLP1 scoring functions indicated 53 molecules better score than Azole drugs, and 5 of them ranked among the top 12 molecules. Since computational and gene-knockout studies indicated CYP121-based viability of M. tuberculosis, these proteins are potential targets for novel drug development. Azole drugs are fungal-based drugs that are extensively reported to inhibit CYP121 orthologs 67.
A molecular docking study evaluated the 1, 3, 4-thiadiazole derivatives revealing significant anti-TB activity with potent minimum inhibitory concentration (MIC) value to rationalize their biological outcome further. Docking was performed using Glide version 5.7 by selecting the complex of the crystal structure of InhA and inhibitor 1-cyclohexyl-N-(3,5-dichlorophenyl)-5-oxopyr-rolidine-3-carboxamide (PDB:2H7M) with 1.62Å resolution indicated −8.02 kcal/mol Glide score. The InhA inhibitor compound 11, 15 and 19 indicating potent MIC value against M. tuberculosis were used for docking against InhA indicating an excellent score of docking in the range of -7.12 to -7.83 kcal/mol in contrast to the less active compounds 6 and 17 docking score of −5.57 and −6.20 kcal/mol 68.
The potential leads such as ZINC000034268676, ZINC000000001392, ZINC000000157405 and ZINC000003958185 selected by virtual screening of natural compounds were characterized for binding interactions with OmpATb an outer membrane protein A of M. tuberculosis. The molecular dynamics simulation indicated the anticipation of PHE151, VAL146, SER145, ARG86, PHE142, ALA115, LEU114, LEU113, and LEU110, amino acids of OmpATb in the formation of stable lead-protein complex. These amino acid residues donate lower binding energy to molecules and OmpATb interactions. The poses of molecules are predicted by Induced Fit Docking using AutoDock Vina. ZINC000034268676 was found to be a potential lead in designing an inhibitor of OmpATb to enhance hydrophobic drug uptake that decreases the duration of TB treatment 69.
DISCUSSION: Adoption of docking techniques for rapid drug discovery is inevitable due to several threats to the healthcare industry, the emergence of drug resistance in medical pathogens, and increased viral diseases, as exemplified by recent coronavirus epidemics persisting all over the world. Moreover, in such an emergency, high throughput screening (HTS) of drug molecules may be fruitless in a limited time and expense. Even the outcome may not always result in isolating a novel bioactive molecule. It may be already isolated, a less potent molecule, or obtaining a novel, highly infrequent potent molecule.
A similar obstacle is also associated with screening existing drug databases for various biological activities. On the other hand, the numbers of newly FDA-approved drugs are drastically declining; for instance, merely 19 new molecular entities were approved in 2007 by USFDA, which is the least count since 1983 70. The astonishing present and future situation is that no new drug molecules are expected to enter the market. Only existing miracle drugs need to be modified to obtain a new lead, further complicating the problem as pharmaceutical industries are driven by innovative and miracle drugs 71.
Since, in the novel drug discovery process time and cost are two crucial factors, computer-aided drug design (CADD) like In-silico Molecular docking methods are increasingly becoming more popular, attractive, and unavoidable computational tools due to their applications in the rapid drug development process in limited budget. Moreover, most software, programs, genomics, proteomics, and metabolomics required for molecular docking are freely available on various public websites. The advancement in Nuclear Magnetic Resonance Spectroscopy and 3D X-ray further enlarged the depository of PDB. For instance, at the end of 2008, in PDB total number of X-ray structure reported were 46,541, which was further grown to the astonishing count of 1,31,993 by the end of 2018 72. Henceforth, virtual screening has become a perfect computational method alternative to HTS in screening such a huge database in a few days, and its speed helps in identifying new leads 73.
One widely applied VS method is molecular docking exploited as a powerful tool in drug discovery and optimization. Over three decades, based on the scoring functions and search algorithms, several docking programs are developed that include AutoDock tools like AutoDock, AutoDock Vina, BDT, WinDock, AUDocker, VSDocker, DockoMatic, DOVIS, PyMOL AutoDock plugin, etc. 74. Along with AutoDock programs, some of the extensively used docking routines are GOLD, FRED, and FlexX. Amidst docking programs, AutoDock Vina generates a 70% perfect pose as the method can bind beep inside the binding pocket of 5Å. However, FRED and FlexX predicta good pose of 45% and 65%, respectively and no significant pose is generated by FlexX. As far asthe average time needed for docking is concerned, the FlexX algorithm is more time taking and FRED is the fastest taking 1.4 seconds for single ligand docking followed by 1.66 and 2-3 seconds required by GOLD and AutoDock Vina 18.
The applications of molecular docking in drug discovery are witnessed by drugs in clinical use. Inhibition of HIV1 Integrase, a target for drug molecules used to treat AIDS was discovered with the help of AutoDock 75. The virtual screening of 14,064 marine drugs is carried out to study the main protein (Mpro) of SARS-CoV-2 Mpro (Severe Acquired Respiratory Syndrome Coronavirus) using a hyphenated pharmacophore model. Further docking of 180 molecules using AutoDock Vina, molecular dynamics simulations, and AutoDock 4 resulted in 17 phloroglucinol oligomers isolated from brown alga Sargassum spinuligerum potentially inhibited the revealed SARS-CoV-2 Mpro with the highest docking score in comparison to the existing treatment of COVID-19 76. Molecular docking techniques also played a substantial role in anti-TB drug discovery. Drug design for anti-TB activity is mainly aimed at inhibition of various cellular targets of M. tuberculosis namely enzymes and cellular proteins having critical functions in cells and essential for bacterial survival example arabinosyltransferase C (cell wall synthesis), protein kinase A (cell shape and cell mechanics), and glutamine synthetase (inhibits host defense mechanism), etc.
The study of interactions of two drugs, Isoniazid and Ethambutol, with Arabinosyltransferase C using the molecular docking tool AutoDock 4 indicated successful inhibition of Arabinosyltransferase C 77. Virtual screening of 3176 FDA drugs using molecular docking against the protein kinase A revealed vitamin B2-based compounds inhibition of protein kinase A. The study suggested that riboflavin and vitamin B2 substances may help treat TB by inhibiting the protein kinase A 78. Similarly, the glutamine synthetase of M. tuberculosis is inhibited by trisubstituted Imidazoles, as revealed by docking studies 78. Thus, the need CADD in the TB drug development process is huge and plays a major role in designing new leads to combat the TB pathogen, including multi-resistant ones.
CONCLUSION: The changing disease scenario, aggressive response of medical pathogens towards antibiotics like the emergence of antibiotic resistance, exhausting antibiotics, and lack of discovery of novel antibiotics are constantly threatening the health care system unless the approaches for innovative drug discovery are changed.
As discussed, the HTS technique is time-consuming and costly; to cope with the above situations, the CADD using computational techniques in the advanced drug development process is essential. Molecular docking offers several software programs for virtual screening for massive biological information for designing new leads or new molecule discoveries. Hence, molecular docking tools play a significant role in discovering new drugs at a limited time and expense.
ACKNOWLEDGEMENT: The Authors would like to thank the Principal, K. L. College of Pharmacy, for encouraging and supporting the manuscript's preparation.
CONFLICTS OF INTEREST: The Authors declared no conflict of interest.
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How to cite this article:
Asra F, Dumala N, Ajra F, Mallika S and Vadivel K: Molecular docking as an ideal tool in pharmaceutical drug development of anti-tuberculosis molecules. Int J Pharm Sci & Res 2023; 14(4): 1534-46. doi: 10.13040/IJPSR.0975-8232.14(4).1534-46.
All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Fathima Asra, Naresh Dumala *, Fathima Ajra, Shaik Mallika and Kannan Vadivel
Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
13 July 2022
01 September 2022
20 October 2022
01 April 2023