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

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 Gene...

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Published inJournal of computer-aided molecular design Vol. 30; no. 9; pp. 651 - 668
Main Authors Gathiaka, Symon, Liu, Shuai, Chiu, Michael, Yang, Huanwang, Stuckey, Jeanne A., Kang, You Na, Delproposto, Jim, Kubish, Ginger, Dunbar, James B., Carlson, Heather A., Burley, Stephen K., Walters, W. Patrick, Amaro, Rommie E., Feher, Victoria A., Gilson, Michael K.
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.09.2016
Springer Nature B.V
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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.
AbstractList 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.
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: (i) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (ii) 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.
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.
Author Kang, You Na
Liu, Shuai
Gathiaka, Symon
Stuckey, Jeanne A.
Gilson, Michael K.
Delproposto, Jim
Amaro, Rommie E.
Carlson, Heather A.
Chiu, Michael
Kubish, Ginger
Yang, Huanwang
Dunbar, James B.
Walters, W. Patrick
Feher, Victoria A.
Burley, Stephen K.
AuthorAffiliation 4 Department of Medicinal Chemistry, University of Michigan, 428 Church St., Ann Arbor, MI 48109-1065
2 RCSB Protein Data Bank, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, Department Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854, United States
3 Center for Structural Biology, Life Sciences Institute, University of Michigan, 210 Washtenaw Avenue, Ann Arbor, MI 48109-2216
6 San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
8 Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
5 Department of Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
7 Relay Therapeutics, 215 First St, Cambridge, MA 20142
1 Drug De
AuthorAffiliation_xml – name: 7 Relay Therapeutics, 215 First St, Cambridge, MA 20142
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– name: 5 Department of Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
– name: 3 Center for Structural Biology, Life Sciences Institute, University of Michigan, 210 Washtenaw Avenue, Ann Arbor, MI 48109-2216
– name: 1 Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
– name: 4 Department of Medicinal Chemistry, University of Michigan, 428 Church St., Ann Arbor, MI 48109-1065
– name: 6 San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
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  fullname: Gathiaka, Symon
  organization: Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego
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  organization: Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego
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  organization: Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego
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  surname: Yang
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  organization: RCSB Protein Data Bank, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, Department Chemistry and Chemical Biology, Rutgers, The State University of New Jersey
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  givenname: Jeanne A.
  surname: Stuckey
  fullname: Stuckey, Jeanne A.
  organization: Center for Structural Biology, Life Sciences Institute, University of Michigan
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  givenname: You Na
  surname: Kang
  fullname: Kang, You Na
  organization: Center for Structural Biology, Life Sciences Institute, University of Michigan
– sequence: 7
  givenname: Jim
  surname: Delproposto
  fullname: Delproposto, Jim
  organization: Center for Structural Biology, Life Sciences Institute, University of Michigan
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  surname: Dunbar
  fullname: Dunbar, James B.
  organization: Department of Medicinal Chemistry, University of Michigan
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  surname: Carlson
  fullname: Carlson, Heather A.
  organization: Department of Medicinal Chemistry, University of Michigan
– sequence: 11
  givenname: Stephen K.
  surname: Burley
  fullname: Burley, Stephen K.
  organization: RCSB Protein Data Bank, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, Department Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Department of Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego Supercomputer Center, University of California, San Diego
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  givenname: W. Patrick
  surname: Walters
  fullname: Walters, W. Patrick
  organization: Relay Therapeutics
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  givenname: Rommie E.
  surname: Amaro
  fullname: Amaro, Rommie E.
  email: drugdesigndata@gmail.com
  organization: Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, Department of Chemistry and Biochemistry, University of California, San Diego
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  givenname: Victoria A.
  orcidid: 0000-0001-9899-8304
  surname: Feher
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  email: victoria.feher@schrodinger.com, drugdesigndata@gmail.com
  organization: Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, Department of Chemistry and Biochemistry, University of California, San Diego, Schrodinger, Inc
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  givenname: Michael K.
  surname: Gilson
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  email: mgilson@ucsd.edu, drugdesigndata@gmail.com
  organization: Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, Department of Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, Department of Chemistry and Biochemistry, University of California, San Diego
BackLink https://www.ncbi.nlm.nih.gov/pubmed/27696240$$D View this record in MEDLINE/PubMed
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D3R
Ligand
Free energy
Protein
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Current affiliation: Schrodinger, Inc. victoria.feher@schrodinger.com.
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Snippet 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...
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...
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StartPage 651
SubjectTerms Affinity
Animal Anatomy
Binding Sites
Chemistry
Chemistry and Materials Science
Communities
Community structure
Computer Applications in Chemistry
Crystallography, X-Ray
Design analysis
Docking
Drug Design
Histology
HSP90 Heat-Shock Proteins - chemistry
Ligands
Mathematical analysis
Molecular Docking Simulation
Morphology
Physical Chemistry
Predictions
Protein Binding
Protein Conformation
Proteins
Quantitative Structure-Activity Relationship
Receptors
Scoring
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Title D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions
URI https://link.springer.com/article/10.1007/s10822-016-9946-8
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Volume 30
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