VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein–Ligand Binding Free Energy Calculations

• DOI: 10.1021/acs.jcim.1c00091
• Publication Date: 2021-05-20T20:10:46Z
• Authors: Ercheng Wang; Weitao Fu; Dejun Jiang; Huiyong Sun; Junmei Wang; Xujun Zhang; Gaoqi Weng; Hui Liu; Peng Tao; Tingjun Hou
• Keywords: Entropy; 0103 physical sciences; Humans; Proteins; Thermodynamics; Molecular Dynamics Simulation; Ligands; 01 natural sciences; Protein Binding; 3. Good health

The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction structure-based drug design (SBDD). However, practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed novel version MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning constants directly to the protein/ligand atoms. new strategy exhibits markedly improved accuracy affinity calculations for various protein–ligand systems and promising be postprocessing virtual screening. Moreover, VAD-MM/GBSA outperformed prime Schrödinger software showed remarkable predictive performance specific protein targets, such POL polyprotein, human immunodeficiency virus type 1 (HIV-1) protease, etc. Our study that little extra computational overhead provides potential replacement AMBER software. An online web server VAD-MMGBSA now available at http://cadd.zju.edu.cn/vdgb.