Abstract
Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.
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References
Abagyan R, Totrov M, Kuznetsov D (1994) ICM: A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J Comput Chem 15:488–506. doi:10.1002/jcc.540150503
Apostolakis J, Plückthun A, Caflisch A (1998) Docking small ligands in flexible binding sites. J Comput Chem 19:21–37. doi:10.1002/(SICI)1096-987X(19980115)19:1<21::AID-JCC2>3.0.CO;2-0
Aqvist J, Medina C, Samuelsson JE (1994) A new method for predicting binding affinity in computer-aided drug design. Protein Eng 7:385–391
Armen RS, Chen J, Brooks CL (2009) An Evaluation of Explicit Receptor Flexibility in Molecular Docking Using Molecular Dynamics and Torsion Angle Molecular Dynamics. J Chem Theory Comput 5:2909–2923. doi:10.1021/ct900262t
Asses Y, Venkatraman V, Leroux V et al (2012) Exploring c-Met kinase flexibility by sampling and clustering its conformational space. Proteins Struct Funct Bioinform 80:1227–1238. doi:10.1002/prot.24021
Ballester PJ, Mitchell JBO (2010) A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26:1169–1175. doi:10.1093/bioinformatics/btq112
Baxter CA, Murray CW, Clark DE et al (1998) Flexible docking using Tabu search and an empirical estimate of binding affinity. Proteins 33:367–382
Beier C, Zacharias M (2010) Tackling the challenges posed by target flexibility in drug design. Expert Opin Drug Discov 5:347–359. doi:10.1517/17460441003713462
Bissantz C, Folkers G, Rognan D (2000) Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J Med Chem 43:4759–4767
Böhm HJ (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6:61–78
Bolton EE, Wang Y, Thiessen PA, Bryant SH (2008) Chapter 12 in. Annual Reports in Computational Chemistry, vol 4. American Chemical Society, Washington, DC, pp 217–241
B-Rao C, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14:394–400. doi:10.1016/j.drudis.2009.01.003.
Brooijmans N, Humblet C (2010) Chemical space sampling by different scoring functions and crystal structures. J Comput Aided Mol Des 24:433–447. doi:10.1007/s10822-010-9356-2
Brooijmans N, Kuntz ID (2003) Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 32:335–373. doi:10.1146/annurev.biophys.32.110601.142532
Carlson HA (2002) Protein flexibility is an important component of structure-based drug discovery. Curr Pharm Des 8:1571–1578
Cavasotto C, Singh N (2008) Docking and High Throughput Docking: Successes and the Challenge of Protein Flexibility. Curr Comput Aided-Drug Des 4:221–234. doi:10.2174/157340908785747474
Chang MW, Ayeni C, Breuer S, Torbett BE (2010) Virtual screening for HIV protease inhibitors: a comparison of AutoDock 4 and Vina. PLoS ONE 5:e11955. doi:10.1371/journal.pone.0011955
Changeux J-P, Edelstein S (2011) Conformational selection or induced-fit? 50 years of debate resolved. Biol Rep. doi:10.3410/B3-19. F1000
Charifson PS, Corkery JJ, Murcko MA, Walters WP (1999) Consensus Scoring: A Method for Obtaining Improved Hit Rates from Docking Databases of Three-Dimensional Structures into Proteins. J Med Chem 42:5100–5109. doi:10.1021/jm990352k
Cheng T, Li X, Li Y et al (2009) Comparative Assessment of Scoring Functions on a Diverse Test Set. J Chem Inf Model 49:1079–1093. doi:10.1021/ci9000053
Cheng T, Li Q, Zhou Z et al (2012) Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 14:133–141. doi:10.1208/s12248-012-9322-0
Clark DE, Westhead DR (1996) Evolutionary algorithms in computer-aided molecular design. J Comput Aided Mol Des 10:337–358. doi:10.1007/BF00124503
Clark DE, Wiley InterScience (Online service) (2000) Evolutionary algorithms in molecular design. Wiley-VCH, Weinheim
Craig IR, Essex JW, Spiegel K (2010) Ensemble Docking into Multiple Crystallographically Derived Protein Structures: An Evaluation Based on the Statistical Analysis of Enrichments. J Chem Inf Model 50:511–524. doi:10.1021/ci900407c
Damm-Ganamet KL, Smith RD, Dunbar JB et al (2013) CSAR Benchmark Exercise 2011–2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series. J. Chem. Inf. Model 53:1853–1870
De Amorim HLN, Caceres RA, Netz PA (2008) Linear interaction energy (LIE) method in lead discovery and optimization. Curr Drug Targets 9:1100–1105
De Azevedo WF Jr, Dias R (2008) Computational methods for calculation of ligand-binding affinity. Curr Drug Targets 9:1031–1039
de Magalhães CS, Barbosa HJC, Dardenne LE (2004a) Selection-Insertion Schemes in Genetic Algorithms for the Flexible Ligand Docking Problem. In: Deb K (ed) Genetic Evolutionary Computation, GECCO 2004. Springer, Berlin, pp 368–379
de Magalhães CS, Barbosa HJC, Dardenne LE (2004b) A genetic algorithm for the ligand-protein docking problem. Genet Mol Biol 27:605–610. doi:10.1590/S1415-47572004000400022
Desmet J, Wilson IA, Joniau M et al (1997) Computation of the binding of fully flexible peptides to proteins with flexible side chains. FASEB J 11:164–172
Dietzen M, Zotenko E, Hildebrandt A, Lengauer T (2012) On the applicability of elastic network normal modes in small-molecule docking. J Chem Inf Model 52:844–856. doi:10.1021/ci2004847
Doman TN, McGovern SL, Witherbee BJ et al (2002) Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J Med Chem 45:2213–2221
Dunkel M (2006) SuperNatural: a searchable database of available natural compounds. Nucleic Acids Res 34:D678–D683. doi:10.1093/nar/gkj132
Eldridge MD, Murray CW, Auton TR et al (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425–445
Ewing TJ, Makino S, Skillman AG, Kuntz ID (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15:411–428
Feher M (2006) Consensus scoring for protein–ligand interactions. Drug Discov Today 11:421–428. doi:10.1016/j.drudis.2006.03.009
Ferrari AM, Wei BQ, Costantino L, Shoichet BK (2004) Soft docking and multiple receptor conformations in virtual screening. J Med Chem 47:5076–5084. doi:10.1021/jm049756p
Flick J, Tristram F, Wenzel W (2012) Modeling loop backbone flexibility in receptor-ligand docking simulations. J Comput Chem 33:2504–2515. doi:10.1002/jcc.23087
Forrey C, Douglas JF, Gilson MK (2012) The fundamental role of flexibility on the strength of molecular binding. Soft Matter 8:6385. doi:10.1039/c2sm25160d
Friesner RA, Banks JL, Murphy RB et al (2004) Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. J Med Chem 47:1739–1749. doi:10.1021/jm0306430
Friesner RA, Murphy RB, Repasky MP et al (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49:6177–6196. doi:10.1021/jm051256o
Frimurer TM, Peters GH, Iversen LF et al (2003) Ligand-induced conformational changes: improved predictions of ligand binding conformations and affinities. Biophys J 84:2273–2281. doi:10.1016/S0006-3495(03)75033-4
Gallicchio E, Lapelosa M, Levy RM (2010) Binding Energy Distribution Analysis Method (BEDAM) for Estimation of Protein − Ligand Binding Affinities. J Chem Theory Comput 6:2961–2977. doi:10.1021/ct1002913
García (1992) Large-amplitude nonlinear motions in proteins. Phys Rev Lett 68:2696–2699
Gaulton A, Bellis LJ, Bento AP et al (2011) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107. doi:10.1093/nar/gkr777
Goodsell DS, Olson AJ (1990) Automated docking of substrates to proteins by simulated annealing. Proteins 8:195–202. doi:10.1002/prot.340080302
Gutiérrez-de-Terán H, Aqvist J (2012) Linear interaction energy: method and applications in drug design. Methods Mol Biol (Clifton NJ) 819:305–323. doi:10.1007/978-1-61779-465-0_20
Haider MK, Bertrand H-O, Hubbard RE (2011) Predicting Fragment Binding Poses Using a Combined MCSS MM-GBSA Approach. J Chem Inf Model 51:1092–1105. doi: 10.1021/ci100469n
Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17:490–519. doi:10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P
Hao M, Li Y, Zhang S-W, Yang W (2011) Investigation on the binding mode of benzothiophene analogues as potent factor IXa (FIXa) inhibitors in thrombosis by CoMFA, docking and molecular dynamic studies. J Enzyme Inhib Med Chem 26:792–804. doi:10.3109/14756366.2011.554414
Hong L, Hartsuck JA, Foundling S et al (1998) Active-site mobility in human immunodeficiency virus, type 1, protease as demonstrated by crystal structure of A28S mutant. Protein Sci 7:300–305. doi:10.1002/pro.5560070209
Houston DR, Walkinshaw MD (2013) Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context. J Chem Inf Model 53:384–390. doi:10.1021/ci300399w.
Huang S-Y, Zou X (2010) Advances and Challenges in Protein-Ligand Docking. Int J Mol Sci 11:3016–3034. doi:10.3390/ijms11083016
Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking Sets for Molecular Docking. J Med Chem 49:6789–6801. doi:10.1021/jm0608356
Huang Z, Wong CF, Wheeler RA (2008) Flexible protein-flexible ligand docking with disrupted velocity simulated annealing. Proteins 71:440–454. doi:10.1002/prot.21781
Huang S-Y, Grinter SZ, Zou X (2010) Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys Chem Chem Phys 12:12899–12908. doi:10.1039/c0cp00151a
Irwin JJ, Sterling T, Mysinger MM et al (2012) ZINC: A Free Tool to Discover Chemistry for Biology. J Chem Inf Model 52:1757–1768. doi:10.1021/ci3001277
Jacobsson M, Lidén P, Stjernschantz E et al (2003) Improving Structure-Based Virtual Screening by Multivariate Analysis of Scoring Data. J Med Chem 46:5781–5789. doi:10.1021/jm030896t
Jain AN (2000) Morphological similarity: a 3D molecular similarity method correlated with protein-ligand recognition. J Comput Aided Mol Des 14:199–213
Jain AN, Nicholls A (2008) Recommendations for evaluation of computational methods. J Comput Aided Mol Des 22:133–139. doi:10.1007/s10822-008-9196-5
Jiang F, Kim SH (1991) “Soft docking”: matching of molecular surface cubes. J Mol Biol 219:79–102
Jones G, Willett P, Glen RC (1995) Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol 245:43–53
Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748. doi:10.1006/jmbi.1996.0897
Kalliokoski T, Salo HS, Lahtela-Kakkonen M, Poso A (2009) The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening. J Chem Inf Model 49:2742–2748. doi:10.1021/ci900364w
Kar G, Keskin O, Gursoy A, Nussinov R (2010) Allostery and population shift in drug discovery. Curr Opin Pharmacol 10:715–722. doi:10.1016/j.coph.2010.09.002
Keserû GM, Kolossváry I (2001) Fully flexible low-mode docking: application to induced fit in HIV integrase. J Am Chem Soc 123:12708–12709
Kim R, Skolnick J (2008) Assessment of programs for ligand binding affinity prediction. J Comput Chem 29:1316–1331. doi:10.1002/jcc.20893
Kinnings SL, Jackson RM (2009) LigMatch: a multiple structure-based ligand matching method for 3D virtual screening. J Chem Inf Model 49:2056–2066. doi:10.1021/ci900204y
Knegtel RM, Kuntz ID, Oshiro CM (1997) Molecular docking to ensembles of protein structures. J Mol Biol 266:424–440. doi:10.1006/jmbi.1996.0776
Kokh DB, Wade RC, Wenzel W (2011) Receptor flexibility in small-molecule docking calculations. Wiley Interdiscip Rev Comput Mol Sci 1:298–314. doi:10.1002/wcms.29
Kolb P, Irwin JJ (2009) Docking screens: right for the right reasons? Curr Top Med Chem 9:755–770
Kollman PA, Massova I, Reyes C et al (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33:889–897
Kolossváry I, Guida WC (1999) Low-mode conformational search elucidated: Application to C39H80 and flexible docking of 9-deazaguanine inhibitors into PNP. J Comput Chem 20:1671–1684. doi:10.1002/(SICI)1096-987X(19991130)20:15<1671::AID-JCC7>3.0.CO;2-Y
Kolossváry I, Keserû GM (2001) Hessian-free low-mode conformational search for large-scale protein loop optimization: application to c-jun N-terminal kinase JNK3. J Comput Chem 22:21–30. doi:10.1002/1096-987X(20010115)22:1<21::AID-JCC3>3.0.CO;2-I
Korb O, Stützle T, Exner TE (2006) PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design. In: Dorigo M, Gambardella LM, Birattari M et al (eds) Ant Colony Optimisation Swarm Intelligence. Springer, Berlin, pp 247–258
Korb O, Stützle T, Exner TE (2009) Empirical Scoring Functions for Advanced Protein − Ligand Docking with PLANTS. J Chem Inf Model 49:84–96. doi:10.1021/ci800298z
Korb O, McCabe P, Cole J (2011) The Ensemble Performance Index: An Improved Measure for Assessing Ensemble Pose Prediction Performance. J Chem Inf Model 51:2915–2919. doi:10.1021/ci2002796
Korb O, Olsson TSG, Bowden SJ et al (2012) Potential and Limitations of Ensemble Docking. J Chem Inf Model 52:1262–1274. doi:10.1021/ci2005934.
Krüger DM, Evers A (2010) Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. Chem Med Chem 5:148–158. doi:10.1002/cmdc.200900314
Kuhl FS, Crippen GM, Friesen DK (1984) A combinatorial algorithm for calculating ligand binding. J Comput Chem 5:24–34. doi:10.1002/jcc.540050105
Kukol A (2011) Consensus virtual screening approaches to predict protein ligands. Eur J Med Chem 46:4661–4664. doi:10.1016/j.ejmech.2011.05.026
Lagorce D, Sperandio O, Galons H et al (2008) FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects. BMC Bioinform 9:396. doi:10.1186/1471-2105-9-396
Lang PT, Brozell SR, Mukherjee S et al (2009) DOCK 6: combining techniques to model RNA-small molecule complexes. RNA 15:1219–1230. doi:10.1261/rna.1563609
Leach AR (1994) Ligand docking to proteins with discrete side-chain flexibility. J Mol Biol 235:345–356
Li Y, Liu Z, Wang R (2010) Test MM-PB/SA on True Conformational Ensembles of Protein − Ligand Complexes. J Chem Inf Model 50:1682–1692. doi:10.1021/ci100036a
Li L, Khanna M, Jo I et al (2011a) Target-Specific Support Vector Machine Scoring in Structure-Based Virtual Screening: Computational Validation, In Vitro Testing in Kinases, and Effects on Lung Cancer Cell Proliferation. J Chem Inf Model 51:755–759. doi:10.1021/ci100490w
Li Y, Kim DJ, Ma W et al (2011b) Discovery of Novel Checkpoint Kinase 1 Inhibitors by Virtual Screening Based on Multiple Crystal Structures. J Chem Inf Model 51:2904–2914. doi:10.1021/ci200257b
Li G-B, Yang L-L, Wang W-J et al (2013) ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein–Ligand Interactions. J Chem Inf Model 53:592–600. doi:10.1021/ci300493w
Lill MA (2011) Efficient Incorporation of Protein Flexibility and Dynamics into Molecular Docking Simulations. Biochemistry (Mosc) 50:6157–6169. doi:10.1021/bi2004558
Liu T, Lin Y, Wen X et al (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35:D198–D201. doi:10.1093/nar/gkl999
Logean A, Sette A, Rognan D (2001) Customized versus universal scoring functions: application to class I MHC-peptide binding free energy predictions. Bioorg Med Chem Lett 11:675–679
Lu I-L, Wang H (2012) Protein-specific Scoring Method for Ligand Discovery. J Comput Biol 19:1215–1226. doi:10.1089/cmb.2012.0188
Luty BA, Wasserman ZR, Stouten PFW et al (1995) A molecular mechanics/grid method for evaluation of ligand-receptor interactions. J Comput Chem 16:454–464. doi:10.1002/jcc.540160409
Ma B, Kumar S, Tsai CJ, Nussinov R (1999) Folding funnels and binding mechanisms. Protein Eng 12:713–720
Madhavi Sastry G, Adzhigirey M, Day T et al (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27:221–234. doi:10.1007/s10822-013-9644-8
Maghsoudi AH, Khodagholi F, Hadi-Alijanvand H et al (2011) Homology modeling, docking, molecular dynamics simulation, and structural analyses of coxsakievirus B3 2A protease: an enzyme involved in the pathogenesis of inflammatory myocarditis. Int J Biol Macromol 49:487–492. doi:10.1016/j.ijbiomac.2011.05.023
Mangoni M, Roccatano D, Di Nola A (1999) Docking of flexible ligands to flexible receptors in solution by molecular dynamics simulation. Proteins 35:153–162
Marsh JA, Teichmann SA, Forman-Kay JD (2012) Probing the diverse landscape of protein flexibility and binding. Curr Opin Struct Biol 22:643–650. doi:10.1016/j.sbi.2012.08.008
Martin YC (2009) Let’s not forget tautomers. J Comput Aided Mol Des 23:693–704. doi:10.1007/s10822-009-9303-2
Meiler J, Baker D (2006) ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility. Proteins 65:538–548. doi:10.1002/prot.21086
Miller DW, Dill KA (1997) Ligand binding to proteins: The binding landscape model. Protein Sci 6:2166–2179. doi:10.1002/pro.5560061011
Miller MD, Kearsley SK, Underwood DJ, Sheridan RP (1994) FLOG: a system to select “quasi-flexible” ligands complementary to a receptor of known three-dimensional structure. J Comput Aided Mol Des 8:153–174
Mizutani MY, Takamatsu Y, Ichinose T et al (2006) Effective handling of induced-fit motion in flexible docking. Proteins 63:878–891. doi:10.1002/prot.20931
Mobley DL, Dill KA (2009) Binding of Small-Molecule Ligands to Proteins: “What You See” Is Not Always “What You Get. Structure 17:489–498
Moitessier N, Englebienne P, Lee D et al (2009) Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go: Docking/scoring methods-a review. Br J Pharmacol 153:S7–S26. doi:10.1038/sj.bjp.0707515
Morris GM, Goodsell DS, Halliday RS et al (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662. doi:10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B
Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. doi:10.1002/jcc.21256
Muegge I (2006) PMF scoring revisited. J Med Chem 49:5895–5902. doi:10.1021/jm050038s
Nabuurs SB, Wagener M, de Vlieg J (2007) A Flexible Approach to Induced Fit Docking. J Med Chem 50:6507–6518. doi:10.1021/jm070593p
Neves MAC, Totrov M, Abagyan R (2012) Docking and scoring with ICM: the benchmarking results and strategies for improvement. J Comput Aided Mol Des 26:675–686. doi:10.1007/s10822-012-9547-0
Nichols SE, Baron R, Ivetac A, McCammon JA (2011) Predictive Power of Molecular Dynamics Receptor Structures in Virtual Screening. J Chem Inf Model 51:1439–1446. doi:10.1021/ci200117n
Nishibata Y, Itai A (1991) Automatic creation of drug candidate structures based on receptor structure. Starting point for artificial lead generation. Tetrahedron 47:8985–8990. doi:10.1016/S0040-4020(01)86503-0
Nishibata Y, Itai A (1993) Confirmation of usefulness of a structure construction program based on three-dimensional receptor structure for rational lead generation. J Med Chem 36:2921–2928
Novoa EM, de Pouplana LR, Barril X, Orozco M (2010) Ensemble Docking from Homology Models. J Chem Theory Comput 6:2547–2557. doi:10.1021/ct100246y
Nowosielski M, Hoffmann M, Kuron A et al (2013) The MM2QM tool for combining docking, molecular dynamics, molecular mechanics, and quantum mechanics†. J Comput Chem 34:750–756. doi:10.1002/jcc.23192
Oda A, Tsuchida K, Takakura T et al (2006) Comparison of Consensus Scoring Strategies for Evaluating Computational Models of Protein − Ligand Complexes. J Chem Inf Model 46:380–391. doi:10.1021/ci050283k
Park S-J, Kufareva I, Abagyan R (2010) Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles. J Comput Aided Mol Des 24:459–471. doi:10.1007/s10822-010-9362-4
Paulsen MD, Ornstein RL (1993) Substrate mobility in thiocamphor-bound cytochrome P450cam: an explanation of the conflict between the observed product profile and the X-ray structure. Protein Eng 6:359–365
Pearce BC, Langley DR, Kang J et al (2009) E-novo: an automated workflow for efficient structure-based lead optimization. J Chem Inf Model 49:1797–1809. doi:10.1021/ci900073k
Pei J, Wang Q, Liu Z et al (2006) PSI-DOCK: towards highly efficient and accurate flexible ligand docking. Proteins 62:934–946. doi:10.1002/prot.20790
Petukh M, Stefl S, Alexov E (2013) The role of protonation states in ligand-receptor recognition and binding. Curr Pharm Des 19:4182–4190
Pierce AC, Sandretto KL, Bemis GW (2002) Kinase inhibitors and the case for CH…O hydrogen bonds in protein-ligand binding. Proteins 49:567–576. doi:10.1002/prot.10259
Plewczynski D, Łaźniewski M, Augustyniak R, Ginalski K (2011) Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J Comput Chem 32:742–755. doi:10.1002/jcc.21643
Rarey M, Kramer B, Lengauer T (1995) Time-efficient docking of flexible ligands into active sites of proteins. Proc Int Conf Intell Syst Mol Biol 3:300–308
Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489. doi:10.1006/jmbi.1996.0477
Rarey M, Kramer B, Lengauer T (1997) Multiple automatic base selection: protein-ligand docking based on incremental construction without manual intervention. J Comput Aided Mol Des 11:369–384
Rejto PA, Verkhivker GM (1996) Unraveling principles of lead discovery: from unfrustrated energy landscapes to novel molecular anchors. Proc Natl Acad Sci USA 93:8945–8950
Ripphausen P, Nisius B, Bajorath J (2011) State-of-the-art in ligand-based virtual screening. Drug Discov Today 16:372–376. doi:10.1016/j.drudis.2011.02.011
Rueda M, Bottegoni G, Abagyan R (2009) Consistent Improvement of Cross-Docking Results Using Binding Site Ensembles Generated with Elastic Network Normal Modes. J Chem Inf Model 49:716–725. doi:10.1021/ci8003732
Rueda M, Bottegoni G, Abagyan R (2010) Recipes for the Selection of Experimental Protein Conformations for Virtual Screening. J Chem Inf Model 50:186–193. doi:10.1021/ci9003943
Schaffer L, Verkhivker GM (1998) Predicting structural effects in HIV-1 protease mutant complexes with flexible ligand docking and protein side-chain optimization. Proteins 33:295–310
Schlosser J, Rarey M (2009) Beyond the virtual screening paradigm: structure-based searching for new lead compounds. J Chem Inf Model 49:800–809. doi:10.1021/ci9000212
Schnecke V, Kuhn L (2000) Virtual screening with solvation and ligand-induced complementarity. Perspect Drug Discov Des 20:171–190. doi:10.1023/A:1008737207775
Seifert MHJ (2009) Targeted scoring functions for virtual screening. Drug Discov Today 14:562–569. doi:10.1016/j.drudis.2009.03.013
Sherman W, Day T, Jacobson MP et al (2006) Novel Procedure for Modeling Ligand/Receptor Induced Fit Effects. J Med Chem 49:534–553. doi:10.1021/jm050540c
Shin W-H, Seok C (2012) GalaxyDock: Protein–Ligand Docking with Flexible Protein Side-chains. J Chem Inf Model 52:3225–3232. doi:10.1021/ci300342z
Shoichet BK, Leach AR, Kuntz ID (1999) Ligand solvation in molecular docking. Proteins 34:4–16
Smellie AS, Crippen GM, Richards WG (1991) Fast drug-receptor mapping by site-directed distances: a novel method of predicting new pharmacological leads. J Chem Inf Model 31:386–392. doi:10.1021/ci00003a004
Söderhjelm P, Kongsted J, Genheden S, Ryde U (2010) Estimates of ligand-binding affinities supported by quantum mechanical methods. Interdiscip Sci Comput Life Sci 2:21–37. doi:10.1007/s12539-010-0083-0
Sokkar P, Sathis V, Ramachandran M (2011) Computational modeling on the recognition of the HRE motif by HIF-1: molecular docking and molecular dynamics studies. J Mol Model 18:1691–1700. doi:10.1007/s00894-011-1150-0
Sotriffer CA, Dramburg I (2005) “In situ cross-docking” to simultaneously address multiple targets. J Med Chem 48:3122–3125. doi:10.1021/jm050075j
Sperandio O, Mouawad L, Pinto E et al (2010) How to choose relevant multiple receptor conformations for virtual screening: a test case of Cdk2 and normal mode analysis. Eur Biophys J EBJ 39:1365–1372. doi:10.1007/s00249-010-0592-0
Stjernschantz E, Oostenbrink C (2010) Improved ligand-protein binding affinity predictions using multiple binding modes. Biophys J 98:2682–2691. doi:10.1016/j.bpj.2010.02.034
Stoll V, Qin W, Stewart KD et al (2002) X-ray crystallographic structure of ABT-378 (lopinavir) bound to HIV-1 protease. Bioorg Med Chem 10:2803–2806
Takaya D, Yamashita A, Kamijo K et al (2011) A new method for induced fit docking (GENIUS) and its application to virtual screening of novel HCV NS3-4A protease inhibitors. Bioorg Med Chem 19:6892–6905. doi:10.1016/j.bmc.2011.09.023
Teodoro ML, Kavraki LE (2003) Conformational flexibility models for the receptor in structure based drug design. Curr Pharm Des 9:1635–1648
Teodoro ML, Phillips GN Jr, Kavraki LE (2003) Understanding protein flexibility through dimensionality reduction. J Comput Biol 10:617–634. doi:10.1089/10665270360688228
Teramoto R, Kashima H (2010) Prediction of protein–ligand binding affinities using multiple instance learning. J Mol Graph Model 29:492–497. doi:10.1016/j.jmgm.2010.09.006
Terp GE, Johansen BN, Christensen IT, Jørgensen FS (2001) A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein-ligand binding affinities. J Med Chem 44:2333–2343
Trosset J-Y, Scheraga HA (1999) Prodock: Software package for protein modeling and docking. J Comput Chem 20:412–427. doi:10.1002/(SICI)1096-987X(199903)20:4<412::AID-JCC3>3.0.CO;2-N
Tsai CJ, Ma B, Nussinov R (1999) Folding and binding cascades: shifts in energy landscapes. Proc Natl Acad Sci USA 96:9970–9972
Tuffery P, Derreumaux P (2011) Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches. J R Soc Interface 9:20–33. doi:10.1098/rsif.2011.0584
Velec HFG, Gohlke H, Klebe G (2005) DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem 48:6296–6303. doi:10.1021/jm050436v
Venkatraman V, Ritchie DW (2012) Flexible protein docking refinement using pose-dependent normal mode analysis. Proteins 80:2262–2274. doi:10.1002/prot.24115
Verdonk ML, Cole JC, Hartshorn MJ et al (2003) Improved protein-ligand docking using GOLD. Proteins Struct Funct Bioinform 52:609–623. doi:10.1002/prot.10465
Verdonk ML, Berdini V, Hartshorn MJ et al (2004) Virtual Screening Using Protein-Ligand Docking: Avoiding Artificial Enrichment. J Chem Inf Model 44:793–806. doi:10.1021/ci034289q
Vigers GPA, Rizzi JP (2004) Multiple active site corrections for docking and virtual screening. J Med Chem 47:80–89. doi:10.1021/jm030161o
Wallqvist A, Covell DG (1996) Docking enzyme-inhibitor complexes using a preference-based free-energy surface. Proteins 25:403–419. doi:10.1002/prot.1
Wang J-C, Lin J-H (2013) Scoring functions for prediction of protein-ligand interactions. Curr Pharm Des 19:2174–2182
Wang J, Verkhivker GM (2003) Energy landscape theory, funnels, specificity, and optimal criterion of biomolecular binding. Phys Rev Lett 90:188101
Wang J, Morin P, Wang W, Kollman PA (2001) Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. J Am Chem Soc 123:5221–5230
Wang R, Lu Y, Wang S (2003) Comparative Evaluation of 11 Scoring Functions for Molecular Docking. J Med Chem 46:2287–2303. doi:10.1021/jm0203783
Wang J, Kang X, Kuntz ID, Kollman PA (2005) Hierarchical database screenings for HIV-1 reverse transcriptase using a pharmacophore model, rigid docking, solvation docking, and MM-PB/SA. J Med Chem 48:2432–2444. doi:10.1021/jm049606e
Weber J, Mesters JR, Lepsík M et al (2002) Unusual binding mode of an HIV-1 protease inhibitor explains its potency against multi-drug-resistant virus strains. J Mol Biol 324:739–754
Welch W, Ruppert J, Jain AN (1996) Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. Chem Biol 3:449–462
Wu G, Robertson DH, Brooks CL, Vieth M (2003) Detailed analysis of grid-based molecular docking: A case study of CDOCKER?A CHARMm-based MD docking algorithm. J Comput Chem 24:1549–1562. doi:10.1002/jcc.10306
Xue M, Zheng M, Xiong B et al (2010) Knowledge-Based Scoring Functions in Drug Design. 1. Developing a Target-Specific Method for Kinase − Ligand Interactions. J Chem Inf Model 50:1378–1386. doi:10.1021/ci100182c
Yan Z, Wang J (2012) Specificity quantification of biomolecular recognition and its implication for drug discovery. Sci Rep. doi:10.1038/srep00309
Yang C-Y, Wang R, Wang S (2006) M-Score: A Knowledge-Based Potential Scoring Function Accounting for Protein Atom Mobility. J Med Chem 49:5903–5911. doi:10.1021/jm050043w
Yang C-Y, Sun H, Chen J et al (2009) Importance of Ligand Reorganization Free Energy in Protein − Ligand Binding-Affinity Prediction. J Am Chem Soc 131:13709–13721. doi:10.1021/ja9039373
Yuriev E, Ramsland PA (2013) Latest developments in molecular docking: 2010-2011 in review. J Mol Recognit JMR 26:215–239. doi:10.1002/jmr.2266
Zacharias M, Sklenar H (1999) Harmonic modes as variables to approximately account for receptor flexibility in ligand-receptor docking simulations: Application to DNA minor groove ligand complex. J Comput Chem 20:287–300. doi:10.1002/(SICI)1096-987X(199902)20:3<287::AID-JCC1>3.0.CO;2-H
Zhang C, Vasmatzis G, Cornette JL, DeLisi C (1997) Determination of atomic desolvation energies from the structures of crystallized proteins. J Mol Biol 267:707–726. doi:10.1006/jmbi.1996.0859
Zsoldos Z, Reid D, Simon A et al (2007) eHiTS: a new fast, exhaustive flexible ligand docking system. J Mol Graph Model 26:198–212. doi:10.1016/j.jmgm.2006.06.002
Acknowledgments
The authors would like to thank FAPERJ project grant number N. E-26/102.443/2009 and CNPq project grant number N. 307062/2010-4.
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Isabella Alvim Guedes, Camila Silva de Magalhães, and Laurent Emmanuel Dardenne declare that they have no conflict of interest.
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Special Issue: Advances in Biophysics in Latin America
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Guedes, I.A., de Magalhães, C.S. & Dardenne, L.E. Receptor–ligand molecular docking. Biophys Rev 6, 75–87 (2014). https://doi.org/10.1007/s12551-013-0130-2
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DOI: https://doi.org/10.1007/s12551-013-0130-2