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Evaluation of DOCK 6 as a pose generation and database enrichment tool

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Abstract

In conjunction with the recent American Chemical Society symposium titled “Docking and Scoring: A Review of Docking Programs” the performance of the DOCK6 program was evaluated through (1) pose reproduction and (2) database enrichment calculations on a common set of organizer-specified systems and datasets (ASTEX, DUD, WOMBAT). Representative baseline grid score results averaged over five docking runs yield a relatively high pose identification success rate of 72.5 % (symmetry corrected rmsd) and sampling rate of 91.9 % for the multi site ASTEX set (N = 147) using organizer-supplied structures. Numerous additional docking experiments showed that ligand starting conditions, symmetry, multiple binding sites, clustering, and receptor preparation protocols all affect success. Encouragingly, in some cases, use of more sophisticated scoring and sampling methods yielded results which were comparable (Amber score ligand movable protocol) or exceeded (LMOD score) analogous baseline grid-score results. The analysis highlights the potential benefit and challenges associated with including receptor flexibility and indicates that different scoring functions have system dependent strengths and weaknesses. Enrichment studies with the DUD database prepared using the SB2010 preparation protocol and native ligand pairings yielded individual area under the curve (AUC) values derived from receiver operating characteristic curve analysis ranging from 0.29 (bad enrichment) to 0.96 (good enrichment) with an average value of 0.60 (27/38 have AUC ≥ 0.5). Strong early enrichment was also observed in the critically important 1.0–2.0 % region. Somewhat surprisingly, an alternative receptor preparation protocol yielded comparable results. As expected, semi-random pairings yielded poorer enrichments, in particular, for unrelated receptors. Overall, the breadth and number of experiments performed provide a useful snapshot of current capabilities of DOCK6 as well as starting points to guide future development efforts to further improve sampling and scoring.

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Acknowledgments

Greg Warren, Neysa Nevins, and Georgia McGauhey are thanked for organizing the special Docking and Scoring symposium. William J. Allen and Jiangyang Liu are thanked for code development and Steve Skiena is thanked for helpful discussions regarding implementation of symmetry corrected rmsd using the Hungarian matching algorithm. This work was supported in part by NIH grants GM57513 (D.A.C.), R01GM083669 (R.C.R.), and F31CA134201 (T.E.B.), as well as the Stony Brook University Office of the Vice President for Research and the New York State Office of Science Technology and Academic Research (NYSTAR). S.R.B. gratefully acknowledges the use of computational facilities at the Ohio Supercomputer Center and thanks OpenEye Scientific Software for an academic license. This work also used resources at the New York Center for Computational Sciences at Stony Brook University/Brookhaven National Laboratory supported by the US Department of Energy under Contract No. DE-AC02-98CH10886 and by the State of New York. Molecular graphics and analyses were performed with the UCSF Chimera package. Chimera is developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from the National Institutes of Health (National Center for Research Resources grant 2P41RR001081, National Institute of General Medical Sciences grant 9P41GM103311).

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Authors and Affiliations

  1. BioMaPS Institute and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, 08854, USA

    Scott R. Brozell, Daniel R. Roe & David A. Case

  2. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, 11794, USA

    Sudipto Mukherjee, Trent E. Balius & Robert C. Rizzo

  3. Institute of Chemical Biology and Drug Discovery, Stony Brook University, Stony Brook, NY, 11794, USA

    Robert C. Rizzo

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  1. Scott R. Brozell
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Correspondence to Robert C. Rizzo.

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Scott R. Brozell and Sudipto Mukherjee contributed equally to this work.

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Brozell, S.R., Mukherjee, S., Balius, T.E. et al. Evaluation of DOCK 6 as a pose generation and database enrichment tool. J Comput Aided Mol Des 26, 749–773 (2012). https://doi.org/10.1007/s10822-012-9565-y

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