Development and evaluation of MM/GBSA based on a variable dielectric GB model for predicting protein-ligand binding affinities

Ercheng Wang, Hui Liu, Junmei Wang, Gaoqi Weng, Huiyong Sun, Zhe Wang, Yu Kang, Tingjun Hou

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


In structure-based drug design (SBDD), the molecular mechanics generalized Born surface area (MM/GBSA) approach has been widely used in ranking the binding affinity of small molecule ligands. However, an accurate estimation of protein-ligand binding affinity still remains a challenge due to the intrinsic limitation of the standard generalized Born (GB) model used in MM/GBSA. In this study, we proposed and evaluated the MM/GBSA approach based on a variable dielectric generalized Born (VDGB) model using residue-type-based dielectric constants. In the VDGB model, different dielectric values were assigned for the three types of protein residues, and the magnitude of the dielectric constants for residue types follows this order: charged ≥ polar ≥ nonpolar. We found that MM/GBSA based on a VDGB model (MM/GBSAVDGB) with an optimal dielectric constant of 4.0 for the charged residues and 1.0 for the noncharged residues together with a net-charge-dependent dielectric value for ligands achieved better predictions as judged by Pearson's correlation coefficient than the standard MM/GBSA with a uniform solute dielectric constant of 4.0 for the training set of 130 protein-ligand complexes. The prediction on the test set with 165 protein-ligand complexes also validated the better performance of MM/GBSAVDGB. Moreover, this method exhibited potential in predicting the relative binding free energies for multiple ligands against the same target. Furthermore, we found that rational truncation of protein residues far from the binding site can significantly speed up the MM/GBSAVDGB calculations, while it almost does not influence the prediction accuracy. Therefore, it is feasible to implement the system-truncated MM/GBSAVDGB as a scoring function for SBDD.

Original languageEnglish (US)
Pages (from-to)5353-5365
Number of pages13
JournalJournal of Chemical Information and Modeling
Issue number11
StatePublished - Nov 23 2020
Externally publishedYes

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • Computer Science Applications
  • Library and Information Sciences


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