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). Dept. of Chemistry, Kenneth S. Pitzer Center for Theoretical Chemistry; Queen's Univ., Kingston, ON (Canada)
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)
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.
Guan, Xingyi, et al. "Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction." Journal of Chemical Information and Modeling, vol. 61, no. 9, Sep. 2021. https://doi.org/10.1021/acs.jcim.1c00388
Guan, Xingyi, Leven, Itai, Heidar-Zadeh, Farnaz, & Head-Gordon, Teresa (2021). Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction. Journal of Chemical Information and Modeling, 61(9). https://doi.org/10.1021/acs.jcim.1c00388
Guan, Xingyi, Leven, Itai, Heidar-Zadeh, Farnaz, et al., "Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction," Journal of Chemical Information and Modeling 61, no. 9 (2021), https://doi.org/10.1021/acs.jcim.1c00388
@article{osti_1838650,
author = {Guan, Xingyi and Leven, Itai and Heidar-Zadeh, Farnaz and Head-Gordon, Teresa},
title = {Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction},
annote = {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},
url = {https://www.osti.gov/biblio/1838650},
journal = {Journal of Chemical Information and Modeling},
issn = {ISSN 1549-9596},
number = {9},
volume = {61},
place = {United States},
publisher = {American Chemical Society},
year = {2021},
month = {09}}