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Title: Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction

Abstract

The electrostatic potential (ESP) is a powerful property for understanding and predicting electrostatic charge distributions that drive interactions between molecules. In this study, we compare various charge partitioning schemes including fitted charges, density-based quantum mechanical (QM) partitioning schemes, charge equilibration methods, and our recently introduced coarse-grained electron model, C-GeM, to describe the ESP for protein systems. Furthermore, when benchmarked against high quality density functional theory calculations of the ESP for tripeptides and the crambin protein, we find that the C-GeM model is of comparable accuracy to ab initio charge partitioning methods, but with orders of magnitude improvement in computational efficiency since it does not require either the electron density or the electrostatic potential as input.

Authors:
 [1];  [1];  [2]; ORCiD logo [3]
+ Show Author Affiliations
  1. Univ. of California, Berkeley, CA (United States). Dept. of Chemistry, Kenneth S. Pitzer Center for Theoretical Chemistry; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Univ. of California, Berkeley, CA (United States). Dept. of Chemistry, Kenneth S. Pitzer Center for Theoretical Chemistry; Queen's Univ., Kingston, ON (Canada)
  3. Univ. of California, Berkeley, CA (United States). Dept. of Chemistry, Kenneth S. Pitzer Center for Theoretical Chemistry; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)
OSTI Identifier:
1838650
Grant/Contract Number:  
AC02-05CH11231; CHE-1955643
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Information and Modeling
Additional Journal Information:
Journal Volume: 61; Journal Issue: 9; Journal ID: ISSN 1549-9596
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Molecular modeling; Carbon; Molecules; Electrostatic potential; Molecular structure

Citation Formats

Guan, Xingyi, Leven, Itai, Heidar-Zadeh, Farnaz, and Head-Gordon, Teresa. Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction. United States: N. p., 2021. Web. doi:10.1021/acs.jcim.1c00388.
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Guan, Xingyi, Leven, Itai, Heidar-Zadeh, Farnaz, & Head-Gordon, Teresa. Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction. United States. https://doi.org/10.1021/acs.jcim.1c00388
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Guan, Xingyi, Leven, Itai, Heidar-Zadeh, Farnaz, and Head-Gordon, Teresa. Tue . "Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction". United States. https://doi.org/10.1021/acs.jcim.1c00388. https://www.osti.gov/servlets/purl/1838650.
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@article{osti_1838650,
title = {Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction},
author = {Guan, Xingyi and Leven, Itai and Heidar-Zadeh, Farnaz and Head-Gordon, Teresa},
abstractNote = {The electrostatic potential (ESP) is a powerful property for understanding and predicting electrostatic charge distributions that drive interactions between molecules. In this study, we compare various charge partitioning schemes including fitted charges, density-based quantum mechanical (QM) partitioning schemes, charge equilibration methods, and our recently introduced coarse-grained electron model, C-GeM, to describe the ESP for protein systems. Furthermore, when benchmarked against high quality density functional theory calculations of the ESP for tripeptides and the crambin protein, we find that the C-GeM model is of comparable accuracy to ab initio charge partitioning methods, but with orders of magnitude improvement in computational efficiency since it does not require either the electron density or the electrostatic potential as input.},
doi = {10.1021/acs.jcim.1c00388},
journal = {Journal of Chemical Information and Modeling},
number = 9,
volume = 61,
place = {United States},
year = {Tue Sep 07 00:00:00 EDT 2021},
month = {Tue Sep 07 00:00:00 EDT 2021}
}
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https://doi.org/10.1021/acs.jcim.1c00388
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