Journal of Computer-Aided Molecular Design

, Volume 33, Issue 1, pp 105–117 | Cite as

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 WangEmail author


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.


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



This work was supported by the research grants from the National Institutes of Health of USA (R01-GM079383, R21-GM097617, P30 DA035778-01A1). Computational support from the Center for Research Computing of University of Pittsburgh, and the Extreme Science and Engineering Discovery Environment (CHE090098), is acknowledged.

Supplementary material

10822_2018_162_MOESM1_ESM.pdf (695 kb)
Supporting Information. Supplementary Material is provided with this manuscript. Figure S1, 2D structures of the tetrahydropyrido-pyrazole core and the pyridinone-like core in the Cathepsin S ligands. Figure S2, 2D structure of CatS-5. Figure S3, Performance of ranking predictions in Stage 2 of CatS set. Figure S4, Performance of submissions for the CatS Free Energy set in Stage 2. (PDF 694 KB)


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of PharmacyUniversity of PittsburghPittsburghUSA

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