Journal of Computer-Aided Molecular Design

, Volume 28, Issue 4, pp 475–490 | Cite as

Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge

  • Emilio Gallicchio
  • Nanjie Deng
  • Peng He
  • Lauren Wickstrom
  • Alexander L. Perryman
  • Daniel N. Santiago
  • Stefano Forli
  • Arthur J. Olson
  • Ronald M. Levy


As part of the SAMPL4 blind challenge, filtered AutoDock Vina ligand docking predictions and large scale binding energy distribution analysis method binding free energy calculations have been applied to the virtual screening of a focused library of candidate binders to the LEDGF site of the HIV integrase protein. The computational protocol leveraged docking and high level atomistic models to improve enrichment. The enrichment factor of our blind predictions ranked best among all of the computational submissions, and second best overall. This work represents to our knowledge the first example of the application of an all-atom physics-based binding free energy model to large scale virtual screening. A total of 285 parallel Hamiltonian replica exchange molecular dynamics absolute protein-ligand binding free energy simulations were conducted starting from docked poses. The setup of the simulations was fully automated, calculations were distributed on multiple computing resources and were completed in a 6-weeks period. The accuracy of the docked poses and the inclusion of intramolecular strain and entropic losses in the binding free energy estimates were the major factors behind the success of the method. Lack of sufficient time and computing resources to investigate additional protonation states of the ligands was a major cause of mispredictions. The experiment demonstrated the applicability of binding free energy modeling to improve hit rates in challenging virtual screening of focused ligand libraries during lead optimization.


Binding free energy Reorganization free energy Free energy ligand screening BEDAM HIV Integrase 



This work has been supported in part by Research Grants from the National Institute of Health (GM30580 and P50 GM103368). The calculations reported in this work have been performed at the BioMaPS High Performance Computing Center at Rutgers University funded in part by the NIH shared instrumentation Grants Nos. 1 S10 RR022375 and 1 S10 RR027444, on XSEDE resources under National Science Foundation allocation Grant No. TG-MCB100145, and on the Garibaldi cluster at the Scripps Research Institute. We thank David Mobley and the SAMPL4 organizers.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Emilio Gallicchio
    • 1
    • 3
  • Nanjie Deng
    • 1
  • Peng He
    • 1
  • Lauren Wickstrom
    • 1
    • 4
  • Alexander L. Perryman
    • 2
  • Daniel N. Santiago
    • 2
  • Stefano Forli
    • 2
  • Arthur J. Olson
    • 2
  • Ronald M. Levy
    • 1
  1. 1.Department of Chemistry and Chemical BiologyRutgers The State University of New JerseyPiscatawayUSA
  2. 2.Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaUSA
  3. 3.Department of ChemistryBrooklyn CollegeBrooklynUSA
  4. 4.Department of Science, Borough of Manhattan Community CollegeCity University of New YorkNew YorkUSA

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