title: Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge.
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.
authors: Department of Computational and System Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA.|School of Medicine, Tsinghua University, Beijing, 100084, China.
authors: Zhaofeng
authors: Z
authors: Ye
authors: Department of Computational and System Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
authors: Matthew P
authors: MP
authors: Baumgartner
authors: Department of Computational and System Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
authors: Bentley M
authors: BM
authors: Wingert
authors: Department of Computational and System Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA. ccamacho@pitt.edu.
authors: Carlos J
authors: CJ
authors: Camacho
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D018360:Crystallography, X-Ray; D015195:Drug Design; D018841:HSP90 Heat-Shock Proteins / Q000737:chemistry*; D047908:Intracellular Signaling Peptides and Proteins / Q000737:chemistry*; D008024:Ligands; D008958:Models, Molecular; D011485:Protein Binding; D011487:Protein Conformation; D017346:Protein Serine-Threonine Kinases / Q000737:chemistry*; D054852:Small Molecule Libraries / Q000737:chemistry*; D014584:User-Computer Interface
chemical_list: D018841:HSP90 Heat-Shock Proteins; D047908:Intracellular Signaling Peptides and Proteins; D008024:Ligands; D054852:Small Molecule Libraries; C117090:MAP4K4 protein, human; D017346:Protein Serine-Threonine Kinases
keywords: Affinity ranking; D3R; Drug discovery; Induced fit; Pose prediction; Virtual screening