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Surflex-Dock: Docking benchmarks and real-world application

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Abstract

Benchmarks for molecular docking have historically focused on re-docking the cognate ligand of a well-determined protein–ligand complex to measure geometric pose prediction accuracy, and measurement of virtual screening performance has been focused on increasingly large and diverse sets of target protein structures, cognate ligands, and various types of decoy sets. Here, pose prediction is reported on the Astex Diverse set of 85 protein ligand complexes, and virtual screening performance is reported on the DUD set of 40 protein targets. In both cases, prepared structures of targets and ligands were provided by symposium organizers. The re-prepared data sets yielded results not significantly different than previous reports of Surflex-Dock on the two benchmarks. Minor changes to protein coordinates resulting from complex pre-optimization had large effects on observed performance, highlighting the limitations of cognate ligand re-docking for pose prediction assessment. Docking protocols developed for cross-docking, which address protein flexibility and produce discrete families of predicted poses, produced substantially better performance for pose prediction. Performance on virtual screening performance was shown to benefit by employing and combining multiple screening methods: docking, 2D molecular similarity, and 3D molecular similarity. In addition, use of multiple protein conformations significantly improved screening enrichment.

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Acknowledgments

The authors gratefully acknowledge NIH for partial funding of the work (grant GM070481) and Ann Cleves for comments on the manuscript. Dr. Jain has a financial interest in BioPharmics LLC, a biotechnology company whose main focus is in the development of methods for computational modeling in drug discovery. Tripos Inc. has exclusive commercial distribution rights for Surflex-Dock and Surflex-Sim, licensed from BioPharmics LLC.

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Authors and Affiliations

  1. Deparment of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA

    Russell Spitzer & Ajay N. Jain

  2. University of California, San Francisco, 1450 3rd Street, Room D373, San Francisco, CA, 94158, USA

    Ajay N. Jain

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  1. Russell Spitzer
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  2. Ajay N. Jain
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Correspondence to Ajay N. Jain.

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Spitzer, R., Jain, A.N. Surflex-Dock: Docking benchmarks and real-world application. J Comput Aided Mol Des 26, 687–699 (2012). https://doi.org/10.1007/s10822-011-9533-y

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  • DOI: https://doi.org/10.1007/s10822-011-9533-y

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