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 in | Journal of computer-aided molecular design Vol. 30; no. 9; pp. 651 - 668 |
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Main Authors | , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Cham
Springer International Publishing
01.09.2016
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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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. |
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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 – name: 8 Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States – name: 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 – 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 |
Author_xml | – sequence: 1 givenname: Symon surname: Gathiaka fullname: Gathiaka, Symon organization: Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego – sequence: 2 givenname: Shuai surname: Liu fullname: Liu, Shuai organization: Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego – sequence: 3 givenname: Michael surname: Chiu fullname: Chiu, Michael organization: Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego – sequence: 4 givenname: Huanwang surname: Yang fullname: Yang, Huanwang 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 – sequence: 5 givenname: Jeanne A. surname: Stuckey fullname: Stuckey, Jeanne A. organization: Center for Structural Biology, Life Sciences Institute, University of Michigan – sequence: 6 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 – sequence: 8 givenname: Ginger surname: Kubish fullname: Kubish, Ginger organization: Center for Structural Biology, Life Sciences Institute, University of Michigan – sequence: 9 givenname: James B. surname: Dunbar fullname: Dunbar, James B. organization: Department of Medicinal Chemistry, University of Michigan – sequence: 10 givenname: Heather A. 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 – sequence: 12 givenname: W. Patrick surname: Walters fullname: Walters, W. Patrick organization: Relay Therapeutics – sequence: 13 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 – sequence: 14 givenname: Victoria A. orcidid: 0000-0001-9899-8304 surname: Feher fullname: Feher, Victoria A. 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 – sequence: 15 givenname: Michael K. surname: Gilson fullname: Gilson, Michael K. 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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Copyright | Springer International Publishing Switzerland 2016 |
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Keywords | Docking Scoring D3R Ligand Free energy Protein |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Current affiliation: Schrodinger, Inc. victoria.feher@schrodinger.com. |
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PublicationTitle | Journal of computer-aided molecular design |
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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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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 |
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