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 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.
KeywordsD3R Docking Scoring Free energy Ligand Protein
This work was supported by National Institutes of Health (NIH) grant 1U01GM111528 for the Drug Design Data Resource (D3R) and U01 GM086873 to the Community Structure Activity Resource (CSAR). We are grateful to Dr. Seth Harris and to Genentech, Inc. for their collaboration and generous donation of the MAP4K4 dataset and Dr. Phil Hadjuk of Abbvie for the HSP90 dataset. We also thank OpenEye Scientific Software for generously donating the use of their software and Jenny Chong for her assistance in preparation of figures for the manuscript. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. MKG has an equity interest in, and is a co-founder and scientific advisor of, VeraChem LLC. REA has equity interest in and is a co-founder of Actavalon, Inc., VAF has equity interest in Actavalon, Inc.
- 4.Carlson HA, Smith RD, Damm-Ganamet KL et al (2016) CSAR 2014: a benchmark exercise using unpublished data from pharma. J Chem Inf Model 56:1063–1077. doi: 10.1021/acs.jcim.5b00523
- 11.Schroder P, Forster T, Kleine S et al (2015) Neuritogenic militarinone-inspired 4-hydroxypyridones target the stress pathway kinase MAP4K4. AngewChemIntEdEngl 54:12398–12403Google Scholar
- 12.Wang L, Stanley M, Boggs J et al (2014) Fragment-based identification and optimization of a class of potent pyrrolo[2,1-f][1, 2, 4]triazine MAP4K4 inhibitors. BioorgMedChemLett 24:4546–4552Google Scholar
- 18.Molecular Operating Environment (MOE) 2013.08; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2016.Google Scholar
- 21.OpenEye Toolkits 2016. Jun.1 OpenEye Scientific Software, Santa Fe, NM. http://www.eyesopen.com
- 25.Klopman G, Li J-Y, Wang S, Dimayuga M (1994) Computer automated log P calculations based on an extended group contribution approach. Computer automated log P calculations based on an extended group contribution approach. J Chem Inf Comput Sci. 34:752–781. doi: 10.1021/ci00020a009
- 35.Kelley BP, Brown SP, Warren GL, Muchmore SW (2015) POSIT: flexible shape-guided docking for pose prediction. J Chem Inf Model. 55:1771-80. doi: 10.1021/acs.jcim.5b00142