Journal of Computer-Aided Molecular Design

, Volume 30, Issue 9, pp 651–668

D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions

  • Symon Gathiaka
  • Shuai Liu
  • Michael Chiu
  • Huanwang Yang
  • Jeanne A. Stuckey
  • You Na Kang
  • Jim Delproposto
  • Ginger Kubish
  • James B. DunbarJr.
  • Heather A. Carlson
  • Stephen K. Burley
  • W. Patrick Walters
  • Rommie E. Amaro
  • Victoria A. Feher
  • Michael K. Gilson
Article

DOI: 10.1007/s10822-016-9946-8

Cite this article as:
Gathiaka, S., Liu, S., Chiu, M. et al. J Comput Aided Mol Des (2016) 30: 651. doi:10.1007/s10822-016-9946-8

Abstract

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand–protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand–protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.

Keywords

D3R Docking Scoring Free energy Ligand Protein 

Supplementary material

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Symon Gathiaka
    • 1
  • Shuai Liu
    • 1
  • Michael Chiu
    • 1
  • Huanwang Yang
    • 2
  • Jeanne A. Stuckey
    • 3
  • You Na Kang
    • 3
  • Jim Delproposto
    • 3
  • Ginger Kubish
    • 3
  • James B. DunbarJr.
    • 4
  • Heather A. Carlson
    • 4
  • Stephen K. Burley
    • 2
    • 5
    • 6
  • W. Patrick Walters
    • 7
  • Rommie E. Amaro
    • 1
    • 8
  • Victoria A. Feher
    • 1
    • 8
    • 9
  • Michael K. Gilson
    • 1
    • 5
    • 8
  1. 1.Drug Design Data Resource, Center for Research in Biological SystemsUniversity of California, San DiegoLa JollaUSA
  2. 2.RCSB Protein Data Bank, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, Department Chemistry and Chemical Biology, RutgersThe State University of New JerseyPiscatawayUSA
  3. 3.Center for Structural Biology, Life Sciences InstituteUniversity of MichiganAnn ArborUSA
  4. 4.Department of Medicinal ChemistryUniversity of MichiganAnn ArborUSA
  5. 5.Department of Pharmacy, Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of California, San DiegoLa JollaUSA
  6. 6.San Diego Supercomputer CenterUniversity of California, San DiegoLa JollaUSA
  7. 7.Relay TherapeuticsCambridgeUSA
  8. 8.Department of Chemistry and BiochemistryUniversity of California, San DiegoLa JollaUSA
  9. 9.Schrodinger, Inc.New YorkUSA

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