Calculate protein–ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3

Xibing He, Viet H. Man, Beihong Ji, Xiang Qun Xie, Junmei Wang

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

We participated in the Cathepsin S (CatS) sub-challenge of the Drug Design Data Resource (D3R) Grand Challenge 3 (GC3) in 2017 to blindly predict the binding poses of 24 CatS-bound ligands, the binding affinity ranking of 136 ligands, and the binding free energies of a subset of 33 ligands in Stage 1A and Stage 2. Our submitted predictions ranked relatively well compared to the submissions from other participants. Here we present our methodologies used in the challenge. For the binding pose prediction, we employed the Glide module in the Schrodinger Suite 2017 and AutoDock Vina. For the binding affinity/free energy prediction, we carried out molecular dynamics simulations of the complexes in explicit water solvent with counter ions, and then estimated the binding free energies with our newly developed model of extended linear interaction energy (ELIE), which is inspired by two other popular end-point approaches: the linear interaction energy (LIE) method, and the molecular mechanics with Poisson–Boltzmann surface area solvation method (MM/PBSA). Our studies suggest that ELIE is a good trade-off between efficiency and accuracy, and it is appropriate for filling the gap between the high-throughput docking and scoring methods and the rigorous but much more computationally demanding methods like free energy perturbation (FEP) or thermodynamics integration (TI) in computer-aided drug design (CADD) projects.

Original languageEnglish (US)
Pages (from-to)105-117
Number of pages13
JournalJournal of Computer-Aided Molecular Design
Volume33
Issue number1
DOIs
StatePublished - Jan 15 2019
Externally publishedYes

Keywords

  • Alchemical free energy calculations
  • Binding affinity
  • Binding mode
  • Lead identification
  • Lead optimization
  • MM/GBSA
  • Virtual screening

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications
  • Physical and Theoretical Chemistry

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