Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge
- 463 Downloads
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.
KeywordsDrug discovery Virtual screening D3R Induced fit Affinity ranking Pose prediction
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.
- 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 24:4546–4552CrossRefGoogle 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 47:1739–1749CrossRefGoogle 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 51:2047–2065CrossRefGoogle 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 31:455–461Google 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 56:1004–1012Google Scholar
- 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 56:1022–1031Google Scholar
- 31.Schrödinger L. The PyMOL Molecular Graphics System, Version 1.7.4 Schrödinger, LLCGoogle 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 3:33Google 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 6:913–918CrossRefGoogle Scholar