MOLECULAR DOCKING: AN EXPLANATORY APPROACH IN STRUCTURE-BASED DRUG DESIGNING AND DISCOVERY
DOI:
https://doi.org/10.22159/ijpps.2021v13i6.40830Keywords:
Molecular Docking, Drug Discovery, Conformations, Ligand, Optimization, Protein FlexibilityAbstract
Molecular docking is a modeling tool of Bioinformatics which includes two or more molecules which interact to provide a stable product in the form of a complex. Molecular docking is helpful in predicting the 3-d structure of a complex which depends on the binding characteristics of Ligand and target. Also, it is a structure-based virtual screening (SBVS) utilized to keep the 3-d structures of small molecule which are generated by computers into a target structure in various types of conformations, positions and orientations. This molecular docking has come out to be a novel concept with various types of advantages. It behaves as a highly exploring domain due to its significant structure-based drug design (SBDD), Assessment of Biochemical pathways, Lead Optimization and in De Novo drug design. In spite of all potential approaches, there are certain challenges which are-scoring function (differentiate the true binding mode), ligand chemistry (tautomerism and ionization) and receptor flexibility (single conformation of rigid receptor). The area of computer-aided drug design and discovery (CADDD) has achieved large favorable outcomes in the past few years. CADD has been adopted by various big pharmaceutical companies for leading discoveries of drugs. Many researchers have worked in order to examine different docking algorithms and to predict molecules' active site. Hence, this Review article depicts the whole sole of Molecular Docking.
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References
Lengauer T, Rarey M. Computational methods for biomolecular docking. Curr Opin Struct Biol 1996;6:402-6.
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discovery 2004;3:935–49.
Agarwal S, Chadha D, Mehrotra R. Molecular modeling and spectroscopic studies of semustine binding with DNA and its comparison with lomustine–DNA adduct formation. J Biomol Struct Dyn 2015;33:1653-68.
Song CM, Lim SJ, Tong JC. Recent advances in computer-aided drug design. Brief Bioinform 2009;10:579–91.
Shekhar C. In silico pharmacology: computer-aided methods could transform drug development. Chem Biol 2008;15:413–4.
Jorgensen WL. The many roles of computation in drug discovery. Sci 2004;303:1813–8.
Xiang M, Cao Y, Fan W, Chen L, Mo Y. Computer-aided drug design: lead discovery and optimization. Comb Chem High Throughput Screen 2012;15:328–37.
Zhang S. Computer-aided drug discovery and development. Methods Mol Biol 2011;716:23–38.
Tan JJ, Cong XJ, Hu LM, Wang CX, Jia L, Liang XJ. Therapeutic strategies underpinning the development of novel techniques for the treatment of HIV infection. Drug Discovery 2010;15:186–97.
Salmaso V. Exploring protein flexibility during docking to investigate ligand-target recognition. Ph. D. thesis, University of Padova, Padova; 2018.
Pozzan A. Molecular descriptors and methods for ligand-based virtual high throughput screening in drug discovery. Curr Pharm Des 2006;12:2099-110.
Green DV. Virtual screening of virtual libraries. Progr Med Chem 2003;41:61-97.
Goodsell DS, Olson AJ. Automated docking of substrates to proteins by Simulated Annealing. Proteins 1990;8:195-202.
Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE. A geometric approach to macromolecule-ligand interactions. J Mol Biol 1982;161:269-88.
Salmaso V, Sturlese M, Cuzzolin A, Moro S. Combining self and cross-docking as benchmark tools: the performance of dock bench in the D3R grand challenge 2. J Comput Aided Mol Des 2018;32:251–64.
Abagyan R, Totrov M. High-throughput docking for lead generation. Curr Opin Chem Biol 2001;5:375–82.
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discovery 2004;3:935–49.
Huang SY, Zou X. Advances and challenges in protein-ligand docking. Int J Mol Sci 2010;11:3016–34.
Rarey M, Kramer B, Lengauer T. Multiple automatic base selection: protein-ligand docking based on incremental construction without manual intervention. J Comput Aided Mol Des 1997;11:369-84.
Schulz Gasch T, Stahl M. Binding site characteristics in structure-based virtual screening: evaluation of current docking tools. J Mol Model 2003;9:47-57.
Goldman BB, Wipke WT. QSD quadratic shape descriptors. 2. Molecular docking using quadratic shape descriptors (QSDock). Proteins 2000;38:79–94.
Meng EC, Shoichet BK, Kuntz ID. Automated docking with grid-based energy evaluation. J Comput Chem 2004;13:505–24.
Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ. Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 1998;19:1639–62.
Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004;47:1739-49.
Jones G, Wilett P, Glein RC, Leach AR, Taylor R. Development and validation of genetic algorithm and an empirical binding free energy function. J Mol Biol 1997;267:727-48.
Venkatachalam CM, Jiang X, Oldfield T, Waldman M. Ligand fit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J Mol Graph Model 2003;27:289-307.
Abagyan RA, Totrov MM, Kuznetsov DA. ICM: a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J Comput Chem 1994;15:488-506.
Brooijmans N, Kuntz ID. Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 2003;32:335–73.
Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 1998;19:1639-62.
Goodsell DS, Olson AJ. Automated docking of substrates to proteins by simulated annealing. Proteins 1990;8:195-202.
Morris GM, Goodsell DS, Huey R, Olson AJ. Distributed automated docking of flexible ligands to proteins: parallel applications of AutoDock 2.4. J Comput Aided Mol Des 1996;10:293-304.
Huang SY, Zou X. Advances and challenges in protein-ligand docking. Int J Mol Sci 2010;11:3016–34.
Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking method using an incremental construction algorithm. J Mol Biol 1996;261:470–89.
Bohm HJ. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des 1994;8:243–56.
Bohm HJ. Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J Compt Aided Mol Des 1998;12:309–23.
Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004;47:1739-49.
Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 2004;47:1750-9.
Jorgensen WL, Tirado Rives J. The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc 1988;110:1657-66.
Jorgensen WL, Maxwell DS, Tirado Rives J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 1996;118:11225-36.
Thomas PD, Dill KA. An iterative method for extracting energy-like quantities from protein structures. PNAS USA 1996;93:11628–33.
Muegge I, Martin YC. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem 1999;42:791–804.
Muegge I. PMF scoring revisited. J Med Chem 2006;49:5895–902.
Huang SY, Zou X. An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials. J Comput Chem 2006a;27:1866–75.
Huang SY, Zou X. An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function. J Comput Chem 2006b;27:1876–82.
Grinter SZ, Yan C, Huang SY, Jiang L, Zou X. Automated large-scale file preparation, docking, and scoring: evaluation of its core and STScore using the 2012 community structure-activity resource benchmark. J Chem Inform Model 2013;53:1905–14.
Grinter SZ, Zou X. A bayesian statistical approach of improving knowledge-based scoring functions for protein–ligand interactions. J Comput Chem 2014a;35:932–43.
Grinter SZ, Zou X. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules 2014b;19:10150–76.
Yan C, Grinter SZ, Merideth BR, Ma Z, Zou X. Iterative knowledge-based scoring functions derived from rigid and flexible decoy structures: evaluation with the 2013 and 2014 CSAR benchmarks. J Chem Inform Model 2016;56:1013–21.
Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE. A geometric approach to macromolecule-ligand interactions. J Molecul Biol 1982;161:269–88.
Halperin I, Ma B, Wolfson H, Nussinov R. Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 2002;47:409–43.
Taylor RD, Jewsbury PJ, Essex JW. A review of protein small molecule docking methods. J Comput Aided Mol Des 2002;16:151–66.
Mc Martin C, Bohacek RS. QXP: Powerful, rapid computer algorithms for structure-based drug design. J Comput Aided Mol Des 1997;11:333-44.
Schnecke V, Kuhn LA. Virtual screening with solvation and ligand-induced complementarity, perspect. Drug Discovery 2000;20:171-90.
Jain AN. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 2003;46:499-511.
Wandzik I. Current molecular docking tools and comparisons thereof. Comm Math Comput Chem 2006;55:271-8.
Kellenberger E, Rodrigo J, Muller P, Rognan D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 2004;57:225-42.
Jha N, Kumar P. Molecular docking studies for the comparative analysis of different biomolecules to target hypoxia inducible factor-1α. Int J Appl Pharm 2017;9:83-9.
Choudhury MD. In silico study for identification of drug like inhibitor from natural compounds against INHA reductase of Mycobacterium tuberculosis. Int J Pharm Pharm 2015;7:232-7.
Brindha DP, Kanakam CC, Nithya G. Docking studies for various antibacterial benzilate derivatives. Asian J Pharm Clin Res 2017;10:268-71.
Hamsa NS, Nair VP, Chandramohan V, Patel SJ. Pharmacophore elucidation and docking studies on anti-inflammatory compounds of medicinal plants for ulcerative coloitis. Asian J Pharm Clin Res 2013;6:56-61.
Ferreira LG, dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules 2015;20:13384-421.
Shoichet BK, McGovern SL, Wei B, Irwin JJ. Lead discovery using molecular docking. Curr Opin Chem Biol 2002;6:439-46.
Gschwend DA, Good AC, Kuntz ID. Molecular docking towards drug discovery. J Mol Recognit 1996;9:175-86.
Elokely KM, Doerksen RJ. Docking challenge: protein sampling and molecular docking performance. J Chem Inform Model 2013;53:1934-45.
Totrov M, Abagyan R. Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 2008;18:178-84.
Cho AE, Rinaldo D. Extension of QM/MM docking and its applications to metalloproteins. J Comput Chem 2009; 30:2609-16.
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