Abstract
Molegro Virtual Docker is a protein-ligand docking simulation program that allows us to carry out docking simulations in a fully integrated computational package. MVD has been successfully applied to hundreds of different proteins, with docking performance similar to other docking programs such as AutoDock4 and AutoDock Vina. The program MVD has four search algorithms and four native scoring functions. Considering that we may have water molecules or not in the docking simulations, we have a total of 32 docking protocols. The integration of the programs SAnDReS (https://github.com/azevedolab/sandres) and MVD opens the possibility to carry out a detailed statistical analysis of docking results, which adds to the native capabilities of the program MVD. In this chapter, we describe a tutorial to carry out docking simulations with MVD and how to perform a statistical analysis of the docking results with the program SAnDReS. To illustrate the integration of both programs, we describe the redocking simulation focused the cyclin-dependent kinase 2 in complex with a competitive inhibitor.
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References
Aarthy M, Singh SK (2018) Discovery of potent inhibitors for the inhibition of dengue envelope protein: an in silico approach. Curr Top Med Chem 18:1585–1602
Sehgal SA, Hammad MA, Tahir RA, Akram HN, Ahmad F (2018) Current therapeutic molecules and targets in neurodegenerative diseases based on in silico drug design. Curr Neuropharmacol 16:649–663
Zloh M, Kirton SB (2018) The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions. Future Med Chem 10:423–432
Ishiki HM, Filho JMB, da Silva MS, Scotti MT, Scotti L (2018) Computer-aided drug design applied to Parkinson targets. Curr Neuropharmacol 16:865–880
Baig MH, Ahmad K, Rabbani G, Danishuddin M, Choi I (2018) Computer aided drug design and its application to the development of potential drugs for neurodegenerative disorders. Curr Neuropharmacol 16:740–748
Crespo A, Rodriguez-Granillo A, Lim VT (2017) Quantum-mechanics methodologies in drug discovery: applications of docking and scoring in lead optimization. Curr Top Med Chem 17:2663–2680
Ramesh M, Dokurugu YM, Thompson MD, Soliman ME (2017) Therapeutic, molecular and computational aspects of novel monoamine oxidase (MAO) inhibitors. Comb Chem High Throughput Screen 20:492–509
Abdolmaleki A, Ghasemi F, Ghasemi JB (2017) Computer-aided drug design to explore cyclodextrin therapeutics and biomedical applications. Chem Biol Drug Des 89:257–268
Ganesan A, Barakat K (2017) Applications of computer-aided approaches in the development of hepatitis C antiviral agents. Expert Opin Drug Discovery 12:407–425
Leelananda SP, Lindert S (2016) Computational methods in drug discovery. Beilstein J Org Chem 12:2694–2718
Hung CL, Chen CC (2014) Computational approaches for drug discovery. Drug Dev Res 75:412–418
Tabeshpour J, Sahebkar A, Zirak MR, Zeinali M, Hashemzaei M, Rakhshani S et al (2018) Computer-aided drug design and drug pharmacokinetic prediction: a mini-review. Curr Pharm Des 24:3014–3019
Zhong F, Xing J, Li X, Liu X, Fu Z, Xiong Z et al (2018) Artificial intelligence in drug design. Sci China Life Sci 61:1191–1204
Suryanarayanan V, Panwar U, Chandra I, Singh SK (2018) De novo design of ligands using computational methods. Methods Mol Biol 1762:71–86
Park H, Jung HY, Mah S, Hong S (2018) Systematic computational design and identification of low picomolar inhibitors of Aurora kinase a. J Chem Inf Model 58:700–709
Abdolmaleki A, Ghasemi JB, Ghasemi F (2017) Computer aided drug design for multi-target drug design: SAR/QSAR, molecular docking and pharmacophore methods. Curr Drug Targets 18:556–575
Zheng X, Liu Z, Li D, Wang E, Wang J (2013) Rational drug design: the search for Ras protein hydrolysis intermediate conformation inhibitors with both affinity and specificity. Curr Pharm Des 19:2246–2258
Jayadeepa RM, Sharma S (2011) Computational models for 5αR inhibitors for treatment of prostate cancer: review of previous works and screening of natural inhibitors of 5αR2. Curr Comput Aided Drug Des 7:231–237
Michel J, Essex JW (2010) Prediction of protein-ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations. J Comput Aided Mol Des 24:639–658
Reddy MR, Erion MD (2005) Computer-aided drug design strategies used in the discovery of fructose 1, 6-bisphosphatase inhibitors. Curr Pharm Des 11:283–294
Irwin JJ, Shoichet BK (2005) ZINC--a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182
Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768
Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P et al (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34:668–672
Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D et al (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36:901–906
Freitas PG, Elias TC, Pinto IA, Costa LT, de Carvalho PVSD, Omote DQ et al (2018) Computational approach to the discovery of phytochemical molecules with therapeutic potential targets to the PKCZ protein. Lett Drug Des Discovery 15:488–499
Teles CB, Moreira-Dill LS, Silva Ade A, Facundo VA, de Azevedo WF Jr, da Silva LH et al (2015) A lupane-triterpene isolated from Combretum leprosum Mart. fruit extracts that interferes with the intracellular development of Leishmania (L.) amazonensis in vitro. BMC Complement Altern Med 15:165
Sá MS, de Menezes MN, Krettli AU, Ribeiro IM, Tomassini TC, Ribeiro dos Santos R et al (2011) Antimalarial activity of physalins B, D, F, and G. J Nat Prod 74:2269–2272
Wong CF, McCammon JA (2003) Protein flexibility and computer-aided drug design. Annu Rev Pharmacol Toxicol 43:31–45
Wishart DS (2008) Identifying putative drug targets and potential drug leads: starting points for virtual screening and docking. Methods Mol Biol 443:333–351
Śledź P, Caflisch A (2018) Protein structure-based drug design: from docking to molecular dynamics. Curr Opin Struct Biol 48:93–102
de Azevedo WF Jr (2011) Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem 18:1353–1366
Goodsell DS, Olson AJ (1990) Automated docking of substrates to proteins by simulated annealing. Proteins 8:195–202
Morris GM, Goodsell DS, Huey R, Olson AJ (1996) Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4. J Comput Aided Mol Des 10:293–304
Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK et al (1998) Automated docking using a lamarckian genetic algorithm and empirical binding free energy function. J Comput Chem 19:1639–1662
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS et al (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791
Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461
Yang JM, Chen CC (2004) GEMDOCK: a generic evolutionary method for molecular docking. Proteins 55:288–304
Yang JM, Shen TW (2005) A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators. Proteins 59:205–220
Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49:3315–3321
Heberlé G, de Azevedo WF Jr (2011) Bio-inspired algorithms applied to molecular docking simulations. Curr Med Chem 18:1339–1352
De Azevedo WF Jr (2010) MolDock applied to structure-based virtual screening. Curr Drug Targets 11:327–334
Bitencourt-Ferreira G, de Azevedo WF Jr (2018) Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys Chem 240:63–69
de Ávila MB, de Azevedo WF Jr (2018) Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem Biol Drug Des 92:1468–1474
Russo S, de Azevedo WF (2019) Advances in the understanding of the cannabinoid receptor 1—focusing on the inverse agonists interactions. Curr Med Chem. https://doi.org/10.2174/0929867325666180417165247
Amaral MEA, Nery LR, Leite CE, de Azevedo Junior WF, Campos MM (2018) Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest New Drugs 36:782–796
Pintro VO, Azevedo WF (2017) Optimized virtual screening workflow. towards target-based polynomial scoring functions for HIV-1 protease. Comb Chem High Throughput Screen 20:820–827
Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF (2017) Supervised machine learning methods applied to predict ligand-binding affinity. Curr Med Chem 24:2459–2470
Coracini JD, de Azevedo WF Jr (2014) Shikimate kinase, a protein target for drug design. Curr Med Chem 21:592–604
Moraes FP, de Azevedo WF Jr (2012) Targeting imidazoline site on monoamine oxidase B through molecular docking simulations. J Mol Model 18:3877–3886
Soares MB, Silva CV, Bastos TM, Guimarães ET, Figueira CP, Smirlis D et al (2012) Anti-Trypanosoma cruzi activity of nicotinamide. Acta Trop 12:224–229
Vianna CP, de Azevedo WF Jr (2012) Identification of new potential Mycobacterium tuberculosis shikimate kinase inhibitors through molecular docking simulations. J Mol Model 18:755–764
Morgan DO (1995) Principles of CDK regulation. Nature 374:131–134
Murray AW (1994) Cyclin-dependent kinases: regulators of the cell cycle and more. Chem Biol 1:191–195
Volkart PA, Bitencourt-Ferreira G, Souto AA, de Azevedo WF (2019) Cyclin-dependent kinase 2 in cellular senescence and cancer. A structural and functional review. Curr Drug Targets 20(7):716–726. https://doi.org/10.2174/1389450120666181204165344
Kim SH, Schulze-Gahmen U, Brandsen J, de Azevedo Júnior WF (1996) Structural basis for chemical inhibition of CDK2. Prog Cell Cycle Res 2:137–145
De Azevedo WF Jr, Mueller-Dieckmann HJ, Schulze-Gahmen U, Worland PJ, Sausville E, Kim SH (1996) Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. Proc Natl Acad Sci U S A 93:2735–2740
Canduri F, de Azevedo WF Jr (2005) Structural basis for interaction of inhibitors with cyclin-dependent kinase 2. Curr Comput Aided Drug Des 1:53–64
Krystof V, Cankar P, Frysová I, Slouka J, Kontopidis G, Dzubák P (2006) 4-arylazo-3,5-diamino-1H-pyrazole CDK inhibitors: SAR study, crystal structure in complex with CDK2, selectivity, and cellular effects. J Med Chem 49:6500–6509
Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo WF Jr (2018) Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem 235:1–8
de Ávila MB, Xavier MM, Pintro VO, de Azevedo WF (2017) Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem Biophys Res Commun 494:305–310
Levin NM, Pintro VO, de Ávila MB, de Mattos BB, De Azevedo WF Jr (2017) Understanding the structural basis for inhibition of cyclin-dependent kinases. New pieces in the molecular puzzle. Curr Drug Targets 18:1104–1111
De Bondt HL, Rosenblatt J, Jancarik J, Jones HD, Morgan DO, Kim SH (1993) Crystal structure of cyclin-dependent kinase 2. Nature 363:595–602
Schulze-Gahmen U, De Bondt HL, Kim SH (1996) High-resolution crystal structures of human cyclin-dependent kinase 2 with and without ATP: bound waters and natural ligand as guides for inhibitor design. J Med Chem 39:4540–4546
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359
Nelder JA, Mead RA (1965) Simplex method for function minimization. Comput J 7:308–313
Korb O, Stutzle T, Exner TE (2009) Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model 49:84–96
De Azevedo WF, Leclerc S, Meijer L, Havlicek L, Strnad M, Kim SH (1997) Inhibition of cyclin-dependent kinases by purine analogues: crystal structure of human CDK2 complexed with roscovitine. Eur J Biochem 243:518–526
Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL et al (2016) SAnDReS a computational tool for statistical analysis of docking results and development of scoring functions. Comb Chem High Throughput Screen 19:801–812
Acknowledgments
This work was supported by grants from CNPq (Brazil) (308883/2014-4). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior—Brasil (CAPES)—Finance Code 001. GB-F acknowledges support from PUCRS/BPA fellowship. WFA is a researcher for CNPq (Brazil) (Process Numbers: 308883/2014-4 and 309029/2018-0).
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Bitencourt-Ferreira, G., de Azevedo, W.F. (2019). Molegro Virtual Docker for Docking. In: de Azevedo Jr., W. (eds) Docking Screens for Drug Discovery. Methods in Molecular Biology, vol 2053. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9752-7_10
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DOI: https://doi.org/10.1007/978-1-4939-9752-7_10
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