Journal of Computer-Aided Molecular Design

, Volume 30, Issue 9, pp 695–706 | Cite as

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

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

Keywords

Drug discovery Virtual screening D3R Induced fit Affinity ranking Pose prediction 

Notes

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.

Supplementary material

10822_2016_9941_MOESM1_ESM.docx (105 kb)
Supplementary material 1 (DOCX 105 kb)

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhaofeng Ye
    • 1
    • 2
  • Matthew P. Baumgartner
    • 1
  • Bentley M. Wingert
    • 1
  • Carlos J. Camacho
    • 1
  1. 1.Department of Computational and System BiologyUniversity of PittsburghPittsburghUSA
  2. 2.School of MedicineTsinghua UniversityBeijingChina

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