TY - JOUR
T1 - VAD-MM/GBSA
T2 - A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein-Ligand Binding Free Energy Calculations
AU - Wang, Ercheng
AU - Fu, Weitao
AU - Jiang, Dejun
AU - Sun, Huiyong
AU - Wang, Junmei
AU - Zhang, Xujun
AU - Weng, Gaoqi
AU - Liu, Hui
AU - Tao, Peng
AU - Hou, Tingjun
N1 - Funding Information:
This work was financially supported by the Key R&D Program of Zhejiang Province (2020C03010), the National Natural Science Foundation of China (21575128 and 81773632), the Natural Science Foundation of Zhejiang Province (LZ19H300001), and the Fundamental Research Funds for the Zhejiang Provincial Universities (2021XZZX035).
Publisher Copyright:
© 2021 American Chemical Society.
PY - 2021/6/28
Y1 - 2021/6/28
N2 - The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction in structure-based drug design (SBDD). However, in practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed a novel version of MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning variable dielectric constants directly to the protein/ligand atoms. The new strategy exhibits markedly improved accuracy in binding affinity calculations for various protein-ligand systems and is promising to be used in the postprocessing of structure-based virtual screening. Moreover, VAD-MM/GBSA outperformed prime MM/GBSA in Schrödinger software and showed remarkable predictive performance for specific protein targets, such as POL polyprotein, human immunodeficiency virus type 1 (HIV-1) protease, etc. Our study showed that the VAD-MM/GBSA method with little extra computational overhead provides a potential replacement of the MM/GBSA in AMBER software. An online web server of VAD-MMGBSA has been developed and is now available at http://cadd.zju.edu.cn/vdgb.
AB - The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction in structure-based drug design (SBDD). However, in practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed a novel version of MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning variable dielectric constants directly to the protein/ligand atoms. The new strategy exhibits markedly improved accuracy in binding affinity calculations for various protein-ligand systems and is promising to be used in the postprocessing of structure-based virtual screening. Moreover, VAD-MM/GBSA outperformed prime MM/GBSA in Schrödinger software and showed remarkable predictive performance for specific protein targets, such as POL polyprotein, human immunodeficiency virus type 1 (HIV-1) protease, etc. Our study showed that the VAD-MM/GBSA method with little extra computational overhead provides a potential replacement of the MM/GBSA in AMBER software. An online web server of VAD-MMGBSA has been developed and is now available at http://cadd.zju.edu.cn/vdgb.
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U2 - 10.1021/acs.jcim.1c00091
DO - 10.1021/acs.jcim.1c00091
M3 - Article
C2 - 34014672
AN - SCOPUS:85108328654
SN - 1549-9596
VL - 61
SP - 2844
EP - 2856
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 6
ER -