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Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations

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Abstract

The binding mode prediction is of great importance to structure-based drug design. The discrimination of various binding poses of ligand generated by docking is a great challenge not only to docking score functions but also to the relatively expensive free energy calculation methods. Here we systematically analyzed the stability of various ligand poses under molecular dynamics (MD) simulation. First, a data set of 120 complexes was built based on the typical physicochemical properties of drug-like ligands. Three potential binding poses (one correct pose and two decoys) were selected for each ligand from self-docking in addition to the experimental pose. Then, five independent MD simulations for each pose were performed with different initial velocities for the statistical analysis. Finally, the stabilities of ligand poses under MD were evaluated and compared with the native one from crystal structure. We found that about 94% of the native poses were maintained stable during the simulations, which suggests that MD simulations are accurate enough to judge most experimental binding poses as stable properly. Interestingly, incorrect decoy poses were maintained much less and 38–44% of decoys could be excluded just by performing equilibrium MD simulations, though 56–62% of decoys were stable. The computationally-heavy binding free energy calculation can be performed only for these survived poses.

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References

  1. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9:203–214

    CAS  Google Scholar 

  2. Bajorath J (2002) Integration of virtual and high-throughput screening. Nat Rev Drug Discov 1:882–894

    Article  CAS  Google Scholar 

  3. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949

    Article  CAS  Google Scholar 

  4. Reddy AS, Pati SP, Kumar PP, Pradeep HN, Sastry GN (2007) Virtual screening in drug discovery—a computational perspective. Curr Protein Pept Sci 8:329–351

    Article  CAS  Google Scholar 

  5. Leach AR, Shoichet BK, Peishoff CE (2006) Prediction of protein–ligand interactions. Docking and scoring: successes and gaps. J Med Chem 49:5851–5855

    Article  CAS  Google Scholar 

  6. Warren GL, Andrews CW, Capelli A-M, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  CAS  Google Scholar 

  7. Kroemer RT (2007) Structure-based drug design: docking and scoring. Curr Protein Pept Sci 8:312–328

    Article  CAS  Google Scholar 

  8. Dunbar JB, Smith RD, Damm-Ganamet KL, Ahmed A, Esposito EX, Delproposto J, Chinnaswamy K, Kang Y-N, Kubish G, Gestwicki JE, Stuckey JA, Carlson HA (2013) CSAR data set release 2012: ligands, affinities, complexes, and docking decoys. J Chem Inf Model 53:1842–1852

    Article  CAS  Google Scholar 

  9. Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49:6789–6801

    Article  CAS  Google Scholar 

  10. Li Y, Liu Z, Li J, Han L, Liu J, Zhao Z, Wang R (2014) Comparative assessment of scoring functions on an updated benchmark: 1. compilation of the test set. J Chem Inf Model 54:1700–1716

    Article  CAS  Google Scholar 

  11. Lagarde N, Zagury J-F, Montes M (2015) Benchmarking data sets for the evaluation of virtual ligand screening methods: review and perspectives. J Chem Inf Model 55:1297–1307

    Article  CAS  Google Scholar 

  12. Halperin I, Ma B, Wolfson H, Nussinov R (2002) Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 47:409–443

    Article  CAS  Google Scholar 

  13. Cheng T, Li X, Li Y, Liu Z, Wang R (2009) Comparative assessment of scoring functions on a diverse test set. J Chem Inf Model 49:1079–1093

    Article  CAS  Google Scholar 

  14. Li Y, Han L, Liu Z, Wang R (2014) Comparative assessment of scoring functions on an updated benchmark: 2. evaluation methods and general results. J Chem Inf Model 54:1717–1736

    Article  CAS  Google Scholar 

  15. Hamza A, Wei N-N, Zhan C-G (2012) Ligand-based virtual screening approach using a new scoring function. J Chem Inf Model 52:963–974

    Article  CAS  Google Scholar 

  16. Nabuurs SB, Wagener M, de Vlieg J (2007) A flexible approach to induced fit docking. J Med Chem 50:6507–6518

    Article  CAS  Google Scholar 

  17. Sun H, Li Y, Shen M, Tian S, Xu L, Pan P, Guan Y, Hou T (2014) Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys Chem Chem Phys 16:22035–22045

    Article  CAS  Google Scholar 

  18. Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the MM/PBSA and MM/GBSA Methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51:69–82

    Article  CAS  Google Scholar 

  19. Greenidge PA, Kramer C, Mozziconacci J-C, Sherman W (2014) Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA. J Chem Inf Model 54:2697–2717

    Article  CAS  Google Scholar 

  20. Thompson DC, Humblet C, Joseph-McCarthy D (2008) Investigation of MM-PBSA rescoring of docking poses. J Chem Inf Model 48:1081–1091

    Article  CAS  Google Scholar 

  21. Cao R, Huang N, Wang Y (2014) Evaluation and application of MD-PB/SA in structure-based hierarchical virtual screening. J Chem Inf Model 54:1987–1996

    Article  CAS  Google Scholar 

  22. Rastelli G, Del Rio A, Degliesposti G, Sgobba M (2010) Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA. J Comput Chem 31:797–810

    CAS  Google Scholar 

  23. Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. J Comput Chem 32:866–877

    Article  CAS  Google Scholar 

  24. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461

    Article  CAS  Google Scholar 

  25. 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

    Article  CAS  Google Scholar 

  26. Clark AJ, Tiwary P, Borrelli K, Feng S, Miller EB, Abel R, Friesner RA, Berne BJ (2016) Prediction of protein–ligand binding poses via a combination of induced fit docking and metadynamics simulations. J Chem Theory Comput 12:2990–2998

    Article  CAS  Google Scholar 

  27. Kellenberger E, Muller P, Schalon C, Bret G, Foata N, Rognan D (2006) sc-PDB: an annotated database of druggable binding sites from the Protein Data Bank. J Chem Inf Model 46:717–727

    Article  CAS  Google Scholar 

  28. Desaphy J, Bret G, Rognan D, Kellenberger E (2015) sc-PDB: a 3D-database of ligandable binding sites–10 years on. Nucleic Acids Res 43:D399–D404

    Article  Google Scholar 

  29. Hu L, Benson ML, Smith RD, Lerner MG, Carlson HA (2005) Binding MOAD (mother of all databases). Proteins Struct Funct Bioinform 60:333–340

    Article  CAS  Google Scholar 

  30. Ahmed A, Smith RD, Clark JJ, Dunbar JB, Carlson HA (2015) Recent improvements to Binding MOAD: a resource for protein–ligand binding affinities and structures. Nucleic Acids Res 43:D465–D469

    Article  Google Scholar 

  31. Kerns EH, Di L (2008) Drug-like properties concepts, structure design and methods: from ADME to toxicity optimization. Academic Press, Boston

    Google Scholar 

  32. Michalsky E, Dunkel M, Goede A, Preissner R (2005) SuperLigands—a database of ligand structures derived from the Protein Data Bank. BMC Bioinform 6:122

    Article  Google Scholar 

  33. Leeson PD, Springthorpe B (2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6:881–890

    Article  CAS  Google Scholar 

  34. Leeson PD, Young RJ (2015) Molecular property design: does everyone get it? ACS Med Chem Lett 6:722–725

    Article  CAS  Google Scholar 

  35. Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90–98

    Article  CAS  Google Scholar 

  36. Warren GL, Do TD, Kelley BP, Nicholls A, Warren SD (2012) Essential considerations for using protein–ligand structures in drug discovery. Drug Discov Today 17:1270–1281

    Article  CAS  Google Scholar 

  37. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Dyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  Google Scholar 

  38. Chemical Computing Group Inc. (2014) Molecular Operating Environment (MOE), 2014.09. 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7

  39. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  Google Scholar 

  40. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas Ö, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09 Revision, vol C 01. Gaussian Inc, Wallingford CT

    Google Scholar 

  41. Walker RC, Crowley MF, Case DA (2008) The implementation of a fast and accurate QM/MM potential method in Amber. J Comput Chem 29:1019–1031

    Article  CAS  Google Scholar 

  42. Case DA, Berryman JT, Betz RM, Cerutti DS, Cheatham, III TE, Darden TA, Duke RE, Giese TJ, Gohlke H, Goetz AW, Homeyer N, Izadi S, Janowski P, Kaus J, Kovalenko A, Lee TS, LeGrand S, Li P, Luchko T, Luo R, Madej B, Merz KM, Monard G, Needham P, Nguyen H, Nguyen HT, Omelyan I, Onufriev A, Roe DR, Roitberg A, Salomon-Ferrer R, Simmerling CL, Smith W, Swails J, Walker RC, Wang J, Wolf RM (2015) AMBER 2015. University of California, San Francisco

    Google Scholar 

  43. Bradbrook GM, Gleichmann T, Harrop SJ, Habash J, Raftery J, Kalb (Gilboa) J, Yariv J, Hillier IH, Helliwell JR (1998) X-Ray and molecular dynamics studies of concanavalin-A glucoside and mannoside complexes Relating structure to thermodynamics of binding. J Chem Soc Faraday Trans 94:1603–1611

    Article  CAS  Google Scholar 

  44. Li P, Merz KM (2014) Taking into account the ion-induced dipole interaction in the nonbonded model of ions. J Chem Theory Comput 10:289–297

    Article  CAS  Google Scholar 

  45. Sindhikara DJ, Kim S, Voter AF, Roitberg AE (2009) Bad seeds sprout perilous dynamics: stochastic thermostat induced trajectory synchronization in biomolecules. J Chem Theory Comput 5:1624–1631

    Article  CAS  Google Scholar 

  46. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34:D668–D672

    Article  CAS  Google Scholar 

  47. Chipot C, Pohorille A (2007) Free energy calculations: theory and applications in chemistry and biology. Springer, Berlin

    Book  Google Scholar 

  48. Kovalenko A, Hirata F (1999) Self-consistent description of a metal-water interface by the Kohn–Sham density functional theory and the three-dimensional reference interaction site model. J Chem Phys 110:10095

    Article  CAS  Google Scholar 

  49. Essex JW, Jorgensen WL (1995) An empirical boundary potential for water droplet simulations. J Comput Chem 16:951–997

    Article  CAS  Google Scholar 

  50. Woo H-J, Dinner AR, Roux B (2004) Grand canonical Monte Carlo simulations of water in protein environments. J Chem Phys 121:6392

    Article  CAS  Google Scholar 

  51. Ahmed A, Sandler SI (2013) Hydration free energies of multifunctional nitroaromatic compounds. J Chem Theory Comput 9:2774–2785

    Article  CAS  Google Scholar 

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Acknowledgements

The authors are grateful to Dr. Satoshi Endo and Dr. Toshimasa Tanaka for careful reading of the manuscript and valuable suggestions and comments. The computational resources are supported by the K computer of the RIKEN Advanced Institute for Computational Science through the HPCI System Research project (Project ID: hp120013) and the TSUBAME Grid Cluster at the Global Scientific Information and Computing Center of Tokyo Institute of Technology, supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) Open Advanced Research Facilities Initiative (Project ID: 15IBD).

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  1. Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan

    Kai Liu, Etsurou Watanabe & Hironori Kokubo

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  1. Kai Liu
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  2. Etsurou Watanabe
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Correspondence to Hironori Kokubo.

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Liu, K., Watanabe, E. & Kokubo, H. Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations. J Comput Aided Mol Des 31, 201–211 (2017). https://doi.org/10.1007/s10822-016-0005-2

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