Journal of Computer-Aided Molecular Design

, Volume 28, Issue 4, pp 327–345 | Cite as

Blind prediction of HIV integrase binding from the SAMPL4 challenge

  • David L. MobleyEmail author
  • Shuai Liu
  • Nathan M. Lim
  • Karisa L. Wymer
  • Alexander L. Perryman
  • Stefano Forli
  • Nanjie Deng
  • Justin Su
  • Kim Branson
  • Arthur J. Olson


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.


HIV integrase Binding mode Virtual screening Pose prediction Affinity SAMPL4 



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.

Supplementary material

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David L. Mobley
    • 1
    • 2
    Email author
  • Shuai Liu
    • 1
  • Nathan M. Lim
    • 1
  • Karisa L. Wymer
    • 1
  • Alexander L. Perryman
    • 3
    • 6
  • Stefano Forli
    • 3
  • Nanjie Deng
    • 4
  • Justin Su
    • 1
  • Kim Branson
    • 5
  • Arthur J. Olson
    • 3
  1. 1.Department of Pharmaceutical Sciences and Department of ChemistryUniversity of California, IrvineIrvineUSA
  2. 2.Department of ChemistryUniversity of New OrleansNew OrleansUSA
  3. 3.Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaUSA
  4. 4.Department of Chemistry and Chemical Biology RutgersThe State University of New JerseyPiscatawayUSA
  5. 5.Hessian InformaticsEmerald HillsUSA
  6. 6.Department of Medicine, Division of Infectious DiseasesRutgers University-NJ Medical SchoolNewarkUSA

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