Skip to main content

Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge

Journal of Computer-Aided Molecular Design volume 30, pages 695–706 (2016)Cite this article

Abstract

Induced fit or protein flexibility can make a given structure less useful for docking and/or scoring. The 2015 Drug Design Data Resource (D3R) Grand Challenge provided a unique opportunity to prospectively test optimal strategies for virtual screening in these type of targets: heat shock protein 90 (HSP90), a protein with multiple ligand-induced binding modes; and mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4), a kinase with a large flexible pocket. Using previously known co-crystal structures, we tested predictions from methods that keep the receptor structure fixed and used (a) multiple receptor/ligand co-crystals as binding templates for minimization or docking (“close”), (b) methods that align or dock to a single receptor (“cross”), and (c) a hybrid approach that chose from multiple bound ligands as initial templates for minimization to a single receptor (“min-cross”). Pose prediction using our “close” models resulted in average ligand RMSDs of 0.32 and 1.6 Å for HSP90 and MAP4K4, respectively, the most accurate models of the community-wide challenge. On the other hand, affinity ranking using our “cross” methods performed well overall despite the fact that a fixed receptor cannot model ligand-induced structural changes,. In addition, “close” methods that leverage the co-crystals of the different binding modes of HSP90 also predicted the best affinity ranking. Our studies suggest that analysis of changes on the receptor structure upon ligand binding can help select an optimal virtual screening strategy.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 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

    CAS  Article  Google Scholar 

  2. Rauh D, Klebe G, Stubbs MT (2004) Understanding protein-ligand interactions: the price of protein flexibility. J Mol Biol 335:1325–1341

    CAS  Article  Google Scholar 

  3. 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 49:5912–5931

    CAS  Article  Google Scholar 

  4. Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11:580–594

    CAS  Article  Google Scholar 

  5. Smith RD, Dunbar JB Jr, Ung PM, Esposito EX, Yang CY, Wang S, Carlson HA (2011) CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51:2115–2131

    CAS  Article  Google Scholar 

  6. Lauria A, Ippolito M, Almerico AM (2009) Inside the Hsp90 inhibitors binding mode through induced fit docking. J Mol Graph Model 27:712–722

    CAS  Article  Google Scholar 

  7. Wang L, Stanley M, Boggs JW, Crawford TD, Bravo BJ, Giannetti AM, Harris SF, Magnuson SR, Nonomiya J, Schmidt S, Wu P, Ye W, Gould SE, Murray LJ, Ndubaku CO, Chen H (2014) Fragment-based identification and optimization of a class of potent pyrrolo [2,1-f][1, 2, 4]triazine MAP4K4 inhibitors. Bioorg Med Chem Lett 24:4546–4552

    CAS  Article  Google Scholar 

  8. Kramer B, Rarey M, Lengauer T (1999) Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins 37:228–241

    CAS  Article  Google Scholar 

  9. Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52:609–623

    CAS  Article  Google Scholar 

  10. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662

    CAS  Article  Google Scholar 

  11. 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

    CAS  Article  Google Scholar 

  12. 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

    CAS  Article  Google Scholar 

  13. Rahaman O, Estrada TP, Doren DJ, Taufer M, Brooks CL 3rd, Armen RS (2011) Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy. J Chem Inf Model 51:2047–2065

    CAS  Article  Google Scholar 

  14. Bohm HJ (1994) The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des 8:243–256

    CAS  Article  Google Scholar 

  15. Bohm HJ (1998) Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J Comput Aided Mol Des 12:309–323

    CAS  Article  Google Scholar 

  16. 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

    CAS  Article  Google Scholar 

  17. Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (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

    CAS  Article  Google Scholar 

  18. Wang R, Liu L, Lai L, Tang Y (1998) SCORE: a new empirical method for estimating the binding affinity of a protein-ligand complex. Mol Model Annu 4:379–394

    CAS  Article  Google Scholar 

  19. Rognan D, Lauemoller SL, Holm A, Buus S, Tschinke V (1999) Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. J Med Chem 42:4650–4658

    CAS  Article  Google Scholar 

  20. Wang R, Lai L, Wang S (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16:11–26

    CAS  Article  Google Scholar 

  21. 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

    CAS  Google Scholar 

  22. Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295:337–356

    CAS  Article  Google Scholar 

  23. DeWitte RS, Shakhnovich EI (1996) SMoG: de Novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J Am Chem Soc 118:11733–11744

    CAS  Article  Google Scholar 

  24. Dunbar JB Jr, Smith RD, Yang CY, Ung PM, Lexa KW, Khazanov NA, Stuckey JA, Wang S, Carlson HA (2011) CSAR benchmark exercise of 2010: selection of the protein-ligand complexes. J Chem Inf Model 51:2036–2046

    CAS  Article  Google Scholar 

  25. Koes DR, Camacho CJ (2012) PocketQuery: protein–protein interaction inhibitor starting points from protein–protein interaction structure. Nucleic Acids Res 40:W387–W392

    CAS  Article  Google Scholar 

  26. Koes DR, Camacho CJ (2012) ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res 40:W409–W414

    CAS  Article  Google Scholar 

  27. Koes DR, Baumgartner MP, Camacho CJ (2013) Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model 53:1893–1904

    CAS  Article  Google Scholar 

  28. Koes D, Khoury K, Huang Y, Wang W, Bista M, Popowicz GM, Wolf S, Holak TA, Domling A, Camacho CJ (2012) Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists. PLoS One 7:e32839

    CAS  Article  Google Scholar 

  29. Baumgartner MP, Camacho CJ (2015) Choosing the optimal rigid receptor for docking and scoring in the CSAR 2013/2014 experiment. J Chem Inf Model 56:1004–1012

  30. Smith RD, Damm-Ganamet KL, Dunbar Jr JB, Ahmed A, Chinnaswamy K, Delproposto JE, Kubish GM, Tinberg CE, Khare SD, Dou J, Doyle L, Stuckey JA, Baker D, Carlson HA (2015) CSAR benchmark exercise 2013: evaluation of results from a combined computational protein design, docking, and scoring/ranking challenge. J Chem Inf Model 56:1022–1031

  31. Schrödinger L. The PyMOL Molecular Graphics System, Version 1.7.4 Schrödinger, LLC

  32. 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 50:572–584

    CAS  Article  Google Scholar 

  33. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Chem Inf 3:33

    Google Scholar 

  34. Tosco P, Balle T, Shiri F (2011) Open3DALIGN: an open-source software aimed at unsupervised ligand alignment. J Comput Aided Mol Des 25:777–783

    CAS  Article  Google Scholar 

  35. Chen X, Liu M, Gilson MK (2001) BindingDB: a web-accessible molecular recognition database. Comb Chem High Throughput Screen 4:719–725

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  37. Wang R, Fang X, Lu Y, Wang S (2004) The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J Med Chem 47:2977–2980

    CAS  Article  Google Scholar 

  38. Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582–6594

    CAS  Article  Google Scholar 

  39. Tosco P, Balle T (2011) Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields. J Mol Model 17:201–208

    Article  Google Scholar 

  40. Ndubaku CO, Crawford TD, Chen H, Boggs JW, Drobnick J, Harris SF, Jesudason R, McNamara E, Nonomiya J, Sambrone A, Schmidt S, Smyczek T, Vitorino P, Wang L, Wu P, Yeung S, Chen J, Chen K, Ding CZ, Wang T, Xu Z, Gould SE, Murray LJ, Ye W (2015) Structure-based design of GNE-495, a potent and selective MAP4K4 inhibitor with efficacy in retinal angiogenesis. ACS Med Chem Lett 6:913–918

    CAS  Article  Google Scholar 

  41. Schroder P, Forster T, Kleine S, Becker C, Richters A, Ziegler S, Rauh D, Kumar K, Waldmann H (2015) Neuritogenic militarinone-inspired 4-hydroxypyridones target the stress pathway kinase MAP4K4. Angew Chem Int Ed Engl 54:12398–12403

    Article  Google Scholar 

  42. Patel RY, Doerksen RJ (2010) Protein kinase-inhibitor database: structural variability of and inhibitor interactions with the protein kinase P-loop. J Proteome Res 9:4433–4442

    CAS  Article  Google Scholar 

  43. Stebbins CE, Russo AA, Schneider C, Rosen N, Hartl FU, Pavletich NP (1997) Crystal structure of an Hsp90-geldanamycin complex: targeting of a protein chaperone by an antitumor agent. Cell 89:239–250

    CAS  Article  Google Scholar 

Download references

Acknowledgments

The authors thank D3R for organizing and evaluating the 2015 Grand Challenge. We are grateful to the OpenEye Scientific for providing an academic license for their software. The work is funded by National Institution of Health 2GM097082. Zhaofeng Ye also thanks the Tsinghua University–University of Pittsburgh joint Program and Chinese Scholar Council for providing the research opportunities and resources.

Author information

Authors and Affiliations

  1. Department of Computational and System Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA

    Zhaofeng Ye, Matthew P. Baumgartner, Bentley M. Wingert & Carlos J. Camacho

  2. School of Medicine, Tsinghua University, Beijing, 100084, China

    Zhaofeng Ye

Authors
  1. Zhaofeng Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew P. Baumgartner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bentley M. Wingert
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlos J. Camacho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos J. Camacho.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 105 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ye, Z., Baumgartner, M.P., Wingert, B.M. et al. Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge. J Comput Aided Mol Des 30, 695–706 (2016). https://doi.org/10.1007/s10822-016-9941-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-016-9941-0

Keywords

  • Drug discovery
  • Virtual screening
  • D3R
  • Induced fit
  • Affinity ranking
  • Pose prediction