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Assessing the performance of the g_mmpbsa tools to simulate the inhibition of oseltamivir to influenza virus neuraminidase by molecular mechanics Poisson–Boltzmann surface area methods
Author(s) -
Ren Jiayi,
Yuan Xiaohui,
Li Junqi,
Lin Shujian,
Yang Bing,
Chen Chun,
Zhao Jian,
Zheng Weihong,
Liao Huaxin,
Yang Zhiwei,
Qu Zhangyi
Publication year - 2020
Publication title -
journal of the chinese chemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 45
eISSN - 2192-6549
pISSN - 0009-4536
DOI - 10.1002/jccs.201900148
Subject(s) - molecular mechanics , chemistry , molecular dynamics , force field (fiction) , solvent models , neuraminidase , water model , oseltamivir , energy landscape , neuraminidase inhibitor , computational chemistry , statistical physics , computational science , molecule , chemical physics , solvation , computer science , physics , quantum mechanics , medicine , biochemistry , disease , covid-19 , pathology , infectious disease (medical specialty) , enzyme , organic chemistry
The molecular mechanics Poisson–Boltzmann surface area (MM‐PBSA) method for GROMACS (g_mmpbsa) is an open‐source tool that is capable of reading the trajectories generated by GROMACS and calculating the binding free energy using the MM‐PBSA method. However, there are multiple force fields available for users to choose from in the GROMACS software, and there are also different solvent water models to combine with the chosen force fields. These different combinations of parameters may significantly impact the results of g_mmpbsa calculation. Unfortunately, the exact combination of force field and solvent water that can well calculate the free energy of the receptor–ligand binding in GROMACS has not been explored yet. To resolve the above issues, this study mainly explored the molecular dynamics (MD) simulations by GROMACS with the six commonly used force fields and three solvent water models, in combination with g_mmpbsa, to calculate the binding free energies of the influenza virus neuraminidase and its mutants with inhibitor oseltamivir carboxylate and compared the present results with previous published results of Amber software from ours and other researchers. Finally, we provided an optimized calculation model, as well as suggestions that may serve as advice and guidance for future computer‐aided designs of drug molecules.