4.7 Article

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

期刊

JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 61, 期 9, 页码 4357-4369

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.1c00388

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资金

  1. National Science Foundation [CHE-1955643]
  2. C3.ai Digital Transformation Institute
  3. National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility [DE-AC02-05CH11231]
  4. Natural Sciences and Engineering Research Council (NSERC) of Canada

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In this study, various charge partitioning schemes were compared to describe the ESP for protein systems, with the C-GeM model found to be as accurate as ab initio methods but significantly more computationally efficient.
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. 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.

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