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Assessment of Amino Acid Electrostatic Parametrizations of the Polarizable Gaussian Multipole Model.
Zhao, Shiji; Cieplak, Piotr; Duan, Yong; Luo, Ray.
Affiliation
  • Zhao S; Nurix Therapeutics, Inc., 1700 Owens Street Suite 205, San Francisco, California 94158, United States.
  • Cieplak P; SBP Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, United States.
  • Duan Y; UC Davis Genome Center and Department of Biomedical Engineering, University of California, Davis, One Shields Avenue, Davis, California 95616, United States.
  • Luo R; Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, University of California, Irvine, Irvine, California 92697, United States.
J Chem Theory Comput ; 20(5): 2098-2110, 2024 Mar 12.
Article in En | MEDLINE | ID: mdl-38394331
ABSTRACT
Accurate parametrization of amino acids is pivotal for the development of reliable force fields for molecular modeling of biomolecules such as proteins. This study aims to assess amino acid electrostatic parametrizations with the polarizable Gaussian Multipole (pGM) model by evaluating the performance of the pGM-perm (with atomic permanent dipoles) and pGM-ind (without atomic permanent dipoles) variants compared to the traditional RESP model. The 100-conf-combterm fitting strategy on tetrapeptides was adopted, in which (1) all peptide bond atoms (-CO-NH-) share identical set of parameters and (2) the total charges of the two terminal N-acetyl (ACE) and N-methylamide (NME) groups were set to neutral. The accuracy and transferability of electrostatic parameters across peptides with varying lengths and real-world examples were examined. The results demonstrate the enhanced performance of the pGM-perm model in accurately representing the electrostatic properties of amino acids. This insight underscores the potential of the pGM-perm model and the 100-conf-combterm strategy for the future development of the pGM force field.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Proteins / Amino Acids Language: En Journal: J Chem Theory Comput Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Proteins / Amino Acids Language: En Journal: J Chem Theory Comput Year: 2024 Document type: Article