Advertisement

Journal of Computer-Aided Molecular Design

, Volume 33, Issue 12, pp 1045–1055 | Cite as

Improving ligand 3D shape similarity-based pose prediction with a continuum solvent model

  • Ashutosh Kumar
  • Kam Y. J. ZhangEmail author
Article
  • 131 Downloads

Abstract

In order to improve the pose prediction performance of docking methods, we have previously developed the pose prediction using shape similarity (PoPSS) method. It identifies a ligand conformation of the highest shape similarity with target protein crystal ligands. The identified ligand conformation is then placed into the target protein binding pocket and refined using side-chain repacking and Monte Carlo energy minimization. Subsequently, we have reported a modification to PoPSS, named as PoPSS-Lite, using a simple grid-based energy minimization for side-chain repacking and Tversky correlation coefficient as the similarity metric. This modification has improved the pose prediction performance and PoPSS-Lite was one of the top performers in D3R GC3. Here we report a further modification to PoPSS that utilizes a continuum solvent model to account for water mediated protein ligand interactions. In this approach, named as PoPSS-PB, the ligand conformation of the highest shape similarity with crystal ligands is refined along with the target protein binding site by incorporating the Poisson–Boltzmann electrostatics. The performance of PoPSS-PB along with PoPSS and PoPSS-Lite was prospectively evaluated in D3R GC4. PoPSS-PB not only demonstrated excellent performance with mean and median RMSDs of 1.20 and 1.13 Å but also achieved improved performance over PoPSS and PoPSS-Lite. Furthermore, the comparison with other D3R GC4 pose prediction submissions revealed admirable performance. Our results showed that the binding poses of ligands with unknown binding modes can be successfully predicted by utilizing ligand 3D shape similarity with known crystallographic ligands and that taking the solvation into consideration improves pose prediction.

Graphic abstract

Keywords

Molecular docking Pose prediction Ligand 3D shape similarity Drug design data resource D3R D3R Grand Challenge 4 

Notes

Acknowledgements

We acknowledge RIKEN ACCC for the supercomputing resources at the Hokusai BigWaterfall supercomputer used in this study. This research was supported by Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under Grant Number JP18am0101082. We thank members of our lab for help and discussions.

Supplementary material

10822_2019_220_MOESM1_ESM.docx (132 kb)
Supplementary file1 (DOCX 132 kb)

References

  1. 1.
    Zauhar RJ, Moyna G, Tian L, Li Z, Welsh WJ (2003) Shape signatures: a new approach to computer-aided ligand- and receptor-based drug design. J Med Chem 46(26):5674–5690.  https://doi.org/10.1021/jm030242k CrossRefPubMedGoogle Scholar
  2. 2.
    Rush TS 3rd, Grant JA, Mosyak L, Nicholls A (2005) A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. J Med Chem 48(5):1489–1495.  https://doi.org/10.1021/jm040163o CrossRefPubMedGoogle Scholar
  3. 3.
    Kortagere S, Krasowski MD, Ekins S (2009) The importance of discerning shape in molecular pharmacology. Trends Pharmacol Sci 30(3):138–147.  https://doi.org/10.1016/j.tips.2008.12.001 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Schnecke V, Bostrom J (2006) Computational chemistry-driven decision making in lead generation. Drug Discov Today 11(1–2):43–50.  https://doi.org/10.1016/S1359-6446(05)03703-7 CrossRefPubMedGoogle Scholar
  5. 5.
    Kumar A, Zhang KYJ (2018) Advances in the development of shape similarity methods and their application in drug discovery. Front Chem 6:315.  https://doi.org/10.3389/fchem.2018.00315 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in review. J Mol Recognit 24(2):149–164.  https://doi.org/10.1002/jmr.1077 CrossRefPubMedGoogle Scholar
  7. 7.
    Yuriev E, Ramsland PA (2013) Latest developments in molecular docking: 2010–2011 in review. J Mol Recognit 26(5):215–239.  https://doi.org/10.1002/jmr.2266 CrossRefPubMedGoogle Scholar
  8. 8.
    Yuriev E, Holien J, Ramsland PA (2015) Improvements, trends, and new ideas in molecular docking: 2012–2013 in review. J Mol Recognit 28(10):581–604.  https://doi.org/10.1002/jmr.2471 CrossRefPubMedGoogle Scholar
  9. 9.
    Irwin JJ, Shoichet BK (2016) Docking screens for novel ligands conferring new biology. J Med Chem 59(9):4103–4120.  https://doi.org/10.1021/acs.jmedchem.5b02008 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Kelley BP, Brown SP, Warren GL, Muchmore SW (2015) POSIT: flexible shape-guided docking for pose prediction. J Chem Inf Model 55(8):1771–1780.  https://doi.org/10.1021/acs.jcim.5b00142 CrossRefPubMedGoogle Scholar
  11. 11.
    Huang SY, Li M, Wang J, Pan Y (2016) HybridDock: a hybrid protein-ligand docking protocol integrating protein- and ligand-based approaches. J Chem Inf Model 56(6):1078–1087.  https://doi.org/10.1021/acs.jcim.5b00275 CrossRefPubMedGoogle Scholar
  12. 12.
    Wu G, Vieth M (2004) SDOCKER: a method utilizing existing X-ray structures to improve docking accuracy. J Med Chem 47(12):3142–3148.  https://doi.org/10.1021/jm040015y CrossRefPubMedGoogle Scholar
  13. 13.
    Fukunishi Y, Nakamura H (2008) Prediction of protein-ligand complex structure by docking software guided by other complex structures. J Mol Graph Model 26(6):1030–1033.  https://doi.org/10.1016/j.jmgm.2007.07.001 CrossRefPubMedGoogle Scholar
  14. 14.
    Fukunishi Y, Nakamura H (2012) Integration of ligand-based drug screening with structure-based drug screening by combining maximum volume overlapping score with ligand docking. Pharmaceuticals (Basel) 5(12):1332–1345.  https://doi.org/10.3390/ph5121332 CrossRefGoogle Scholar
  15. 15.
    Kumar A, Zhang KY (2016) A pose prediction approach based on ligand 3D shape similarity. J Comput Aided Mol Des 30(6):457–469.  https://doi.org/10.1007/s10822-016-9923-2 CrossRefPubMedGoogle Scholar
  16. 16.
    Kumar A, Zhang KY (2016) Prospective evaluation of shape similarity based pose prediction method in D3R Grand Challenge 2015. J Comput Aided Mol Des 30(9):685–693.  https://doi.org/10.1007/s10822-016-9931-2 CrossRefPubMedGoogle Scholar
  17. 17.
    Kumar A, Zhang KYJ (2019) Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3. J Comput Aided Mol Des 33(1):47–59.  https://doi.org/10.1007/s10822-018-0142-x CrossRefPubMedGoogle Scholar
  18. 18.
    Kumar A, Zhang KY (2016) Application of Shape Similarity in Pose Selection and Virtual Screening in CSARdock2014 Exercise. J Chem Inf Model 56(6):965–973.  https://doi.org/10.1021/acs.jcim.5b00279 CrossRefPubMedGoogle Scholar
  19. 19.
    Burley SK, Berman HM, Bhikadiya C, Bi C, Chen L, Di Costanzo L, Christie C, Dalenberg K, Duarte JM, Dutta S, Feng Z, Ghosh S, Goodsell DS, Green RK, Guranovic V, Guzenko D, Hudson BP, Kalro T, Liang Y, Lowe R, Namkoong H, Peisach E, Periskova I, Prlic A, Randle C, Rose A, Rose P, Sala R, Sekharan M, Shao C, Tan L, Tao YP, Valasatava Y, Voigt M, Westbrook J, Woo J, Yang H, Young J, Zhuravleva M, Zardecki C (2019) RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res 47(D1):D464–D474.  https://doi.org/10.1093/nar/gky1004 CrossRefPubMedGoogle Scholar
  20. 20.
    Gathiaka S, Liu S, Chiu M, Yang H, Stuckey JA, Kang YN, Delproposto J, Kubish G, Dunbar JB Jr, Carlson HA, Burley SK, Walters WP, Amaro RE, Feher VA, Gilson MK (2016) D3R grand challenge 2015: evaluation of protein-ligand pose and affinity predictions. J Comput Aided Mol Des 30(9):651–668.  https://doi.org/10.1007/s10822-016-9946-8 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Gaieb Z, Liu S, Gathiaka S, Chiu M, Yang H, Shao C, Feher VA, Walters WP, Kuhn B, Rudolph MG, Burley SK, Gilson MK, Amaro RE (2018) D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des 32(1):1–20.  https://doi.org/10.1007/s10822-017-0088-4 CrossRefPubMedGoogle Scholar
  22. 22.
    Gaieb Z, Parks CD, Chiu M, Yang H, Shao C, Walters WP, Lambert MH, Nevins N, Bembenek SD, Ameriks MK, Mirzadegan T, Burley SK, Amaro RE, Gilson MK (2019) D3R Grand Challenge 3: blind prediction of protein-ligand poses and affinity rankings. J Comput Aided Mol Des 33(1):1–18.  https://doi.org/10.1007/s10822-018-0180-4 CrossRefPubMedGoogle Scholar
  23. 23.
    Tversky A (1977) Features of similarity. Psychol Rev 84(4):327–352.  https://doi.org/10.1037/0033-295X.84.4.327 CrossRefGoogle Scholar
  24. 24.
    Fogolari F, Brigo A, Molinari H (2002) The Poisson-Boltzmann equation for biomolecular electrostatics: a tool for structural biology. J Mol Recognit 15(6):377–392.  https://doi.org/10.1002/jmr.577 CrossRefPubMedGoogle Scholar
  25. 25.
    Hawkins PC, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. J Chem Inf Model 50(4):572–584.  https://doi.org/10.1021/ci100031x CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Hawkins PC, Nicholls A (2012) Conformer generation with OMEGA: learning from the data set and the analysis of failures. J Chem Inf Model 52(11):2919–2936.  https://doi.org/10.1021/ci300314k CrossRefPubMedGoogle Scholar
  27. 27.
    Sindhikara D, Spronk SA, Day T, Borrelli K, Cheney DL, Posy SL (2017) Improving accuracy, diversity, and speed with prime macrocycle conformational sampling. J Chem Inf Model 57(8):1881–1894.  https://doi.org/10.1021/acs.jcim.7b00052 CrossRefPubMedGoogle Scholar
  28. 28.
    Winn MD, Ballard CC, Cowtan KD, Dodson EJ, Emsley P, Evans PR, Keegan RM, Krissinel EB, Leslie AGW, McCoy A, McNicholas SJ, Murshudov GN, Pannu NS, Potterton EA, Powell HR, Read RJ, Vagin A, Wilson KS (2011) Overview of the CCP4 suite and current developments. Acta Crystallogr Sect D 67(4):235–242.  https://doi.org/10.1107/S0907444910045749 CrossRefGoogle Scholar
  29. 29.
    Hawkins PCD, Skillman AG, Nicholls A (2006) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50(1):74–82.  https://doi.org/10.1021/jm0603365 CrossRefGoogle Scholar
  30. 30.
    Lindström A, Edvinsson L, Johansson A, Andersson CD, Andersson IE, Raubacher F, Linusson A (2011) Postprocessing of docked protein−ligand complexes using implicit solvation models. J Chem Inf Model 51(2):267–282.  https://doi.org/10.1021/ci100354x CrossRefPubMedGoogle Scholar
  31. 31.
    Sgobba M, Caporuscio F, Anighoro A, Portioli C, Rastelli G (2012) Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations. Eur J Med Chem 58:431–440.  https://doi.org/10.1016/j.ejmech.2012.10.024 CrossRefPubMedGoogle Scholar
  32. 32.
    Rastelli G, Degliesposti G, Del Rio A, Sgobba M (2009) Binding estimation after refinement, a new automated procedure for the refinement and rescoring of docked ligands in virtual screening. Chem Bio Drug Des 73(3):283–286.  https://doi.org/10.1111/j.1747-0285.2009.00780.x CrossRefGoogle Scholar
  33. 33.
    Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17(5–6):490–519.  https://doi.org/10.1002/(sici)1096-987x(199604)17:5/6%3c490:Aid-jcc1%3e3.0.Co;2-p CrossRefGoogle Scholar
  34. 34.
    Grant JA, Pickup BT, Sykes MJ, Kitchen CA, Nicholls A (2007) A simple formula for dielectric polarisation energies: the Sheffield Solvation Model. Chem Phys Lett 441(1):163–166.  https://doi.org/10.1016/j.cplett.2007.05.008 CrossRefGoogle Scholar
  35. 35.
    Wlodek S, Skillman AG, Nicholls A (2010) Ligand entropy in gas-phase, upon solvation and protein complexation. Fast estimation with quasi-Newton Hessian. J Chem Theory Comput 6(7):2140–2152.  https://doi.org/10.1021/ct100095p CrossRefPubMedGoogle Scholar
  36. 36.
    Hawkins PCD (2017) Conformation generation: the state of the art. J Chem Inf Model 57(8):1747–1756.  https://doi.org/10.1021/acs.jcim.7b00221 CrossRefPubMedGoogle Scholar
  37. 37.
    Warren GL, Andrews CW, Capelli AM, 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(20):5912–5931.  https://doi.org/10.1021/jm050362n CrossRefPubMedGoogle Scholar
  38. 38.
    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(4):742–755.  https://doi.org/10.1002/jcc.21643 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics ResearchRIKENYokohamaJapan

Personalised recommendations