Development of Shell-Space Electrostatic Potential Fitting Charges in a United-Atom Model for Amino Acids Simulations

In the development of coarse-grained (CG) models, accurately capturing the electrostatic interactions between CG particles is crucial for enhancing the accuracy of these models. In this study, we developed a shell-space electrostatic potential (SS-ESP) method to derive the partial charges for CG particles by fitting the ESP of all-atom (AA) models. To validate the accuracy of the SS-ESP method, we systematically tested and compared the ESPs and electric dipole moments of CG charges obtained using four different methods, with AA models of amino acids serving as the reference. Based on the SS-ESP charges, we proposed a new Side-chain Reduced CG (SRCG) model for amino acids. The parameterization of the new SRCG force field follows a bottom-up strategy, where the bonding interaction parameters are inherited from the AA model, the CG charges are derived using the SS-ESP method, and the Lennard-Jones (LJ) parameters are fitted to reproduce the static potential for van der Waals (vdW) interactions. Molecular dynamics simulations of amino acids show that the test results for dipeptides and tripeptides based on this SRCG model are consistent with those obtained from the corresponding AA models. Furthermore, the SRCG force field can accurately describe the solvation free energy of amino acids and preserve the stability of protein secondary structures. Hence, the present SS-ESP charge fitting method provides a new framework for accurately describing the electrostatic interactions of CG particles for bottom-up development of protein CG models.

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Publication Details

Journal
ChemRxiv
Published
2025-09-19
DOI
https://doi.org/10.26434/chemrxiv-2025-fsqh4
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Development of Shell-Space Electrostatic Potential Fitting Charges in a United-Atom Model for Amino Acids Simulations

Fei Xia, Yuwei Zhang, Xin Xu, Fengyu Li
ChemRxiv
Machine Learning in Materials Science
article

Development of Shell-Space Electrostatic Potential Fitting Charges in a United-Atom Model for Amino Acids Simulations

Fei Xia, Yuwei Zhang, Xin Xu, Fengyu Li
article en

Abstract

In the development of coarse-grained (CG) models, accurately capturing the electrostatic interactions between CG particles is crucial for enhancing the accuracy of these models. In this study, we developed a shell-space electrostatic potential (SS-ESP) method to derive the partial charges for CG particles by fitting the ESP of all-atom (AA) models. To validate the accuracy of the SS-ESP method, we systematically tested and compared the ESPs and electric dipole moments of CG charges obtained using four different methods, with AA models of amino acids serving as the reference. Based on the SS-ESP charges, we proposed a new Side-chain Reduced CG (SRCG) model for amino acids. The parameterization of the new SRCG force field follows a bottom-up strategy, where the bonding interaction parameters are inherited from the AA model, the CG charges are derived using the SS-ESP method, and the Lennard-Jones (LJ) parameters are fitted to reproduce the static potential for van der Waals (vdW) interactions. Molecular dynamics simulations of amino acids show that the test results for dipeptides and tripeptides based on this SRCG model are consistent with those obtained from the corresponding AA models. Furthermore, the SRCG force field can accurately describe the solvation free energy of amino acids and preserve the stability of protein secondary structures. Hence, the present SS-ESP charge fitting method provides a new framework for accurately describing the electrostatic interactions of CG particles for bottom-up development of protein CG models.

ChemRxiv
Nanjing Normal University (CN), Fudan University (CN), East China Normal University (CN)
New York University Shanghai, National Natural Science Foundation of China, East China Normal University, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 80%
Machine Learning in Materials Science
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