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.
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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
Rauh D, Klebe G, Stubbs MT (2004) Understanding protein-ligand interactions: the price of protein flexibility. J Mol Biol 335:1325–1341
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
Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11:580–594
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
Lauria A, Ippolito M, Almerico AM (2009) Inside the Hsp90 inhibitors binding mode through induced fit docking. J Mol Graph Model 27:712–722
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
Kramer B, Rarey M, Lengauer T (1999) Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins 37:228–241
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52:609–623
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
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
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
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
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
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
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
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
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
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
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
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
Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295:337–356
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
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
Koes DR, Camacho CJ (2012) PocketQuery: protein–protein interaction inhibitor starting points from protein–protein interaction structure. Nucleic Acids Res 40:W387–W392
Koes DR, Camacho CJ (2012) ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res 40:W409–W414
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
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
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
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
Schrödinger L. The PyMOL Molecular Graphics System, Version 1.7.4 Schrödinger, LLC
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
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
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
Chen X, Liu M, Gilson MK (2001) BindingDB: a web-accessible molecular recognition database. Comb Chem High Throughput Screen 4:719–725
Hu L, Benson ML, Smith RD, Lerner MG, Carlson HA (2005) Binding MOAD (mother of all databases). Proteins 60:333–340
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
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
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
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
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
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
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
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.
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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
- Drug discovery
- Virtual screening
- Induced fit
- Affinity ranking
- Pose prediction