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蛋白 C-GeM:用于快速准确预测蛋白质静电势的粗粒度电子模型。

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

机构信息

Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States.

Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.

出版信息

J Chem Inf Model. 2021 Sep 27;61(9):4357-4369. doi: 10.1021/acs.jcim.1c00388. Epub 2021 Sep 7.

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. 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 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.

摘要

静电势(ESP)是理解和预测驱动分子间相互作用的静电电荷分布的有力属性。在这项研究中,我们比较了各种电荷分割方案,包括拟合电荷、基于密度的量子力学(QM)分割方案、电荷平衡方法以及我们最近引入的粗粒电子模型 C-GeM,以描述蛋白质体系的静电势。当与三肽和 crambin 蛋白的静电势的高质量密度泛函理论计算进行基准测试时,我们发现 C-GeM 模型的准确性与电荷分割方法相当,但计算效率提高了几个数量级,因为它不需要电子密度或静电势作为输入。

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