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Blind prediction of HIV integrase binding from the SAMPL4 challenge

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Abstract

Here, we give an overview of the protein-ligand binding portion of the Statistical Assessment of Modeling of Proteins and Ligands 4 (SAMPL4) challenge, which focused on predicting binding of HIV integrase inhibitors in the catalytic core domain. The challenge encompassed three components—a small “virtual screening” challenge, a binding mode prediction component, and a small affinity prediction component. Here, we give summary results and statistics concerning the performance of all submissions at each of these challenges. Virtual screening was particularly challenging here in part because, in contrast to more typical virtual screening test sets, the inactive compounds were tested because they were thought to be likely binders, so only the very top predictions performed significantly better than random. Pose prediction was also quite challenging, in part because inhibitors in the set bind to three different sites, so even identifying the correct binding site was challenging. Still, the best methods managed low root mean squared deviation predictions in many cases. Here, we give an overview of results, highlight some features of methods which worked particularly well, and refer the interested reader to papers in this issue which describe specific submissions for additional details.

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Notes

  1. The SAMPL4 challenge was designed, run and evaluated by the Mobley lab with some help from Kim Branson, so when this report uses the word “we” to refer to an action relating to challenge design, logistics, and analysis, it refers to these authors—specifically, Mobley, Branson, Su, Lim, Wymer, and Liu.

  2. The challenge began with 322 compounds, 260 non-binders, and 62 binders, but due to errors and redundancies, final analysis was run on 305 compounds and 56 binders.

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Acknowledgments

We acknowledge the financial support of the National Institutes of Health (1R15GM096257-01A1 to DLM and R01 GM073087 and P50 GM103368 to AJO), and computing support from the UCI GreenPlanet cluster, supported in part by NSF Grant CHE-0840513. We are also grateful to OpenEye Scientific Software for support for SAMPL, including for the meeting and for logistical help with the website, and in particular would like to thank Matt Geballe for help with the website and submissions and for helpful discussions, and Paul Hawkins, Greg Warren, and Geoff Skillman for helpful discussions and pointers on analysis. We are also thankful to Tom Peat (CSIRO) and colleagues for the experimental data which made the integrase portion of SAMPL possible, and helped initiate SAMPL4.

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Author notes

  1. Alexander L. Perryman

    Present address: Department of Medicine, Division of Infectious Diseases, Rutgers University-NJ Medical School, Newark, NJ, USA

Authors and Affiliations

  1. Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, 147 Bison Modular, Irvine, CA, 92697, USA

    David L. Mobley, Shuai Liu, Nathan M. Lim, Karisa L. Wymer & Justin Su

  2. Department of Chemistry, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA, 70148, USA

    David L. Mobley

  3. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA

    Alexander L. Perryman, Stefano Forli & Arthur J. Olson

  4. Department of Chemistry and Chemical Biology Rutgers, The State University of New Jersey, A203, 610 Taylor Road, Piscataway, NJ, 08854, USA

    Nanjie Deng

  5. Hessian Informatics, LLC. 609 Lakeview Way, Emerald Hills, CA, USA

    Kim Branson

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  1. David L. Mobley
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Mobley, D.L., Liu, S., Lim, N.M. et al. Blind prediction of HIV integrase binding from the SAMPL4 challenge. J Comput Aided Mol Des 28, 327–345 (2014). https://doi.org/10.1007/s10822-014-9723-5

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