Article

Journal of Computer-Aided Molecular Design

, Volume 28, Issue 4, pp 475-490

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

  • Emilio GallicchioAffiliated withDepartment of Chemistry and Chemical Biology, Rutgers The State University of New JerseyDepartment of Chemistry, Brooklyn College Email author 
  • , Nanjie DengAffiliated withDepartment of Chemistry and Chemical Biology, Rutgers The State University of New Jersey
  • , Peng HeAffiliated withDepartment of Chemistry and Chemical Biology, Rutgers The State University of New Jersey
  • , Lauren WickstromAffiliated withDepartment of Chemistry and Chemical Biology, Rutgers The State University of New JerseyDepartment of Science, Borough of Manhattan Community College, City University of New York
  • , Alexander L. PerrymanAffiliated withDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute
  • , Daniel N. SantiagoAffiliated withDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute
  • , Stefano ForliAffiliated withDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute
  • , Arthur J. OlsonAffiliated withDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute
  • , Ronald M. LevyAffiliated withDepartment of Chemistry and Chemical Biology, Rutgers The State University of New Jersey Email author 

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Abstract

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.

Keywords

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