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Docking-undocking combination applied to the D3R Grand Challenge 2015

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Abstract

Novel methods for drug discovery are constantly under development and independent exercises to test and validate them for different goals are extremely useful. The drug discovery data resource (D3R) Grand Challenge 2015 offers an excellent opportunity as an external assessment and validation experiment for Computer-Aided Drug Discovery methods. The challenge comprises two protein targets and prediction tests: binding mode and ligand ranking. We have faced both of them with the same strategy: pharmacophore-guided docking followed by dynamic undocking (a new method tested experimentally here) and, where possible, critical assessment of the results based on pre-existing information. In spite of using methods that are qualitative in nature, our results for binding mode and ligand ranking were amongst the best on Hsp90. Results for MAP4K4 were less positive and we track the different performance across systems to the level of previous knowledge about accessible conformational states. We conclude that docking is quite effective if supplemented by dynamic undocking and empirical information (e.g. binding hot spots, productive protein conformations). This setup is well suited for virtual screening, a frequent application that was not explicitly tested in this edition of the D3R Grand Challenge 2015. Protein flexibility remains as the main cause for hard failures.

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References

  1. Barril X, Javier Luque F (2012) Molecular simulation methods in drug discovery: a prospective outlook. J Comput Aided Mol Des 26:81–86. doi:10.1007/s10822-011-9506-1

    Article  CAS  Google Scholar 

  2. Bajorath J (2015) Computer-aided drug discovery. F1000Research. doi:10.12688/f1000research.6653.1

    Google Scholar 

  3. Sliwoski G, Kothiwale S, Meiler J, Lowe EWJ (2014) Computational methods in drug discovery. Pharmacol Rev 61:67–75. doi:10.1016/j.vascn.2010.02.005

    Google Scholar 

  4. Moult J, Fidelis K, Kryshtafovych A et al (2014) Critical assessment of methods of protein structure prediction (CASP)—round x. Proteins 82(Suppl 2):1–6. doi:10.1002/prot.24452

    Article  CAS  Google Scholar 

  5. Janin J (2005) Assessing predictions of protein-protein interaction: the CAPRI experiment. Protein Sci 14:278–283. doi:10.1110/ps.041081905

    Article  CAS  Google Scholar 

  6. Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) The SAMPL4 host-guest blind prediction challenge: an overview. J Comput Aided Mol Des 28:305–317. doi:10.1007/s10822-014-9735-1

    Article  CAS  Google Scholar 

  7. Ferreira L, dos Santos R, Oliva G, Andricopulo A (2015) Molecular docking and structure-based drug design strategies. Molecules. doi:10.3390/molecules200713384

    Google Scholar 

  8. 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. doi:10.1007/s10822-010-9363-3

    Article  CAS  Google Scholar 

  9. Steinbrecher TB, Dahlgren M, Cappel D et al (2015) Accurate binding free energy predictions in fragment optimization. J Chem Inf Model 55:2411–2420. doi:10.1021/acs.jcim.5b00538

    Article  CAS  Google Scholar 

  10. Wang L, Wu Y, Deng Y et al (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc. doi:10.1021/ja512751q

    Google Scholar 

  11. Chodera JD, Mobley DL, Shirts MR et al (2011) Alchemical free energy methods for drug discovery: progress and challenges. Curr Opin Struct Biol 21:150–160. doi:10.1016/j.sbi.2011.01.011

    Article  CAS  Google Scholar 

  12. Christ CD, Fox T (2014) Accuracy assessment and automation of free energy calculations for drug design. J Chem Inf Model 54:108–120. doi:10.1021/ci4004199

    Article  CAS  Google Scholar 

  13. Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432:862–865. doi:10.1038/nature03197

    Article  CAS  Google Scholar 

  14. Mobley DL, Graves AP, Chodera JD et al (2007) Predicting absolute ligand binding free energies to a simple model site. J Mol Biol 371:1118–1134. doi:10.1016/j.jmb.2007.06.002

    Article  CAS  Google Scholar 

  15. Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N et al (2014) rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput Biol 10:e1003571. doi:10.1371/journal.pcbi.1003571

    Article  Google Scholar 

  16. Joseph-McCarthy D, Thomas BE, Belmarsh M et al (2003) Pharmacophore-based molecular docking to account for ligand flexibility. Proteins 51:172–188. doi:10.1002/prot.10266

    Article  CAS  Google Scholar 

  17. Hindle SA, Rarey M, Buning C, Lengaue T (2002) Flexible docking under pharmacophore type constraints. J Comput Aided Mol Des 16:129–149

    Article  CAS  Google Scholar 

  18. Good AC, Cheney DL, Sitkoff DF et al (2003) Analysis and optimization of structure-based virtual screening protocols. 2. Examination of docked ligand orientation sampling methodology: mapping a pharmacophore for success. J Mol Gr Model 22:31–40. doi:10.1016/S1093-3263(03)00124-4

    Article  CAS  Google Scholar 

  19. Barril X, Morley SD (2005) Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J Med Chem 48:4432–4443. doi:10.1021/jm048972v

    Article  CAS  Google Scholar 

  20. Wright L, Barril X, Dymock B et al (2004) Structure-activity relationships in purine-based inhibitor binding to HSP90 isoforms. Chem Biol 11:775–785. doi:10.1016/j.chembiol.2004.03.033

    Article  CAS  Google Scholar 

  21. Ruiz-Carmona S et al (2016) Dynamic undocking and the Quasi-Bound state as tools for drug design. Nat Chem, In press

  22. McGovern SL, Shoichet BK (2003) Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. J Med Chem 46:2895–2907. doi:10.1021/jm0300330

    Article  CAS  Google Scholar 

  23. Verdonk ML, Mortenson PN, Hall RJ et al (2008) Protein-ligand docking against non-native protein conformers. J Chem Inf Model 48:2214–2225. doi:10.1021/ci8002254

    Article  CAS  Google Scholar 

  24. Barril X, Hubbard RE, Morley SD (2004) Virtual screening in structure-based drug discovery. Mini Rev Med Chem 4:779–791

    CAS  Google Scholar 

  25. Bavi R, Kumar R, Choi L, Woo Lee K (2016) Exploration of novel inhibitors for Bruton’s tyrosine kinase by 3D QSAR modeling and molecular dynamics simulation. PLoS One 11:e0147190. doi:10.1371/journal.pone.0147190

    Article  Google Scholar 

  26. Quesada-Romero L, Mena-Ulecia K, Tiznado W, Caballero J (2014) Insights into the interactions between maleimide derivates and GSK3β combining molecular docking and QSAR. PLoS One 9:e102212. doi:10.1371/journal.pone.0102212

    Article  Google Scholar 

  27. Morley SD, Afshar M (2004) Validation of an empirical RNA-ligand scoring function for fast flexible docking using Ribodock. J Comput Aided Mol Des 18:189–208

    Article  CAS  Google Scholar 

  28. Clark M, Cramer RD, Van Opdenbosch N (1989) Validation of the general purpose tripos 5.2 force field. J Comput Chem 10:982–1012. doi:10.1002/jcc.540100804

    Article  CAS  Google Scholar 

  29. Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2016.

  30. LigPrep, version 2.3, Schrödinger, LLC, New York, NY, 2009.

  31. Case DA, Babin V, Berryman JT, et al (2014) AMBER 14. University of California, San Francisco.

  32. Bayly CI, McKay D, Truchon J-F (2011) An informal AMBER small molecule force field: parm@Frosst

  33. Kroemer RT (2003) Molecular modelling probes: docking and scoring. Biochem Soc Trans 31(5):980–984. doi:10.1042/BST0310980

    Article  CAS  Google Scholar 

  34. Yusuf D, Davis AM, Kleywegt GJ, Schmitt S (2008) An alternative method for the evaluation of docking performance: RSR vs RMSD. J Chem Inf Model 48:1411–1422. doi:10.1021/ci800084x

    Article  CAS  Google Scholar 

  35. Warren GL, Do TD, Kelley BP et al (2012) Essential considerations for using protein-ligand structures in drug discovery. Drug Discov Today 17:1270–1281. doi:10.1016/j.drudis.2012.06.011

    Article  CAS  Google Scholar 

  36. Cozzini P, Kellogg GE, Spyrakis F et al (2009) Target flexibility: an emerging consideration in drug discovery and design. J Med Chem 51:804–828. doi:10.1021/jm800562d.Target

    Google Scholar 

  37. Spyrakis F, BidonChanal A, Barril X, Luque FJ (2011) Protein flexibility and ligand recognition: challenges for molecular modeling. Curr Top Med Chem 11:192–210. doi:10.2174/156802611794863571

    Article  CAS  Google Scholar 

  38. Barril X, Fradera X (2006) Incorporating protein flexibility into docking and structure-based drug design. Expert Opin Drug Discov 1:335–349. doi:10.1517/17460441.1.4.335

    Article  CAS  Google Scholar 

  39. Cheng LS, Amaro RE, Xu D et al (2008) Ensemble-based virtual screening reveals potential novel antiviral compounds for avian influenza neuraminidase. J Med Chem 51:3878–3894. doi:10.1021/jm8001197

    Article  CAS  Google Scholar 

  40. Abagyan R, Rueda M, Bottegoni G (2010) Recipes for the selection of experimental protein conformations for virtual screening. J Chem Inf Model 50:186–193. doi:10.1021/ci9003943

    Article  Google Scholar 

  41. Campbell AJ, Lamb ML, Joseph-McCarthy D (2014) Ensemble-based docking using biased molecular dynamics. J Chem Inf Model 54:2127–2138. doi:10.1021/ci400729j

    Article  CAS  Google Scholar 

  42. Birch L, Murray CW, Hartshorn MJ et al (2002) Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase. J Comput Aided Mol Des 16:855–869. doi:10.1023/A:1023844626572

    Article  CAS  Google Scholar 

  43. Barril X (2014) Ligand discovery: Docking points. Nat Chem 6:560–561. doi:10.1038/nchem.1986

    Article  CAS  Google Scholar 

  44. Fischer M, Coleman RG, Fraser JS, Shoichet BK (2014) Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat Chem 6:575–583. doi:10.1038/nchem.1954

    Article  CAS  Google Scholar 

  45. Álvarez-García D, Barril X (2014) Molecular simulations with solvent competition quantify water displaceability and provide accurate interaction maps of protein binding sites. J Med Chem, 57(20):8530–8539. doi:10.1021/jm5010418

    Article  Google Scholar 

  46. Bento AP, Gaulton A, Hersey A et al (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42:1083–1090. doi:10.1093/nar/gkt1031

    Article  Google Scholar 

  47. Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242. doi:10.1093/nar/28.1.235

    Article  CAS  Google Scholar 

  48. Lavecchia A (2015) Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today 20:318–331. doi:10.1016/j.drudis.2014.10.012

    Article  Google Scholar 

  49. Wale N (2011) Machine learning in drug discovery and development. Drug Dev Res 72:112–119. doi:10.1002/ddr.20407

    Article  CAS  Google Scholar 

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Correspondence to Xavier Barril.

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Ruiz-Carmona, S., Barril, X. Docking-undocking combination applied to the D3R Grand Challenge 2015. J Comput Aided Mol Des 30, 805–815 (2016). https://doi.org/10.1007/s10822-016-9979-z

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