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比较酸碱计算和蛋白质设计的对加和多体广义 Born 模型。

Comparing pairwise-additive and many-body generalized Born models for acid/base calculations and protein design.

机构信息

Ecole Polytechnique, Laboratoire de Biochimie (CNRS UMR7654), Palaiseau, 91128, France.

出版信息

J Comput Chem. 2017 Oct 30;38(28):2396-2410. doi: 10.1002/jcc.24898. Epub 2017 Jul 27.

DOI:10.1002/jcc.24898
PMID:28749575
Abstract

Generalized Born (GB) solvent models are common in acid/base calculations and protein design. With GB, the interaction between a pair of solute atoms depends on the shape of the protein/solvent boundary and, therefore, the positions of all solute atoms, so that GB is a many-body potential. For compute-intensive applications, the model is often simplified further, by introducing a mean, native-like protein/solvent boundary, which removes the many-body property. We investigate a method for both acid/base calculations and protein design that uses Monte Carlo simulations in which side chains can explore rotamers, bind/release protons, or mutate. The fluctuating protein/solvent dielectric boundary is treated in a way that is numerically exact (within the GB framework), in contrast to a mean boundary. Its originality is that it captures the many-body character while retaining the residue-pairwise complexity given by a fixed boundary. The method is implemented in the Proteus protein design software. It yields a slight but systematic improvement for acid/base constants in nine proteins and a significant improvement for the computational design of three PDZ domains. It eliminates a source of model uncertainty, which will facilitate the analysis of other model limitations. © 2017 Wiley Periodicals, Inc.

摘要

广义 Born(GB)溶剂模型在酸碱计算和蛋白质设计中很常见。在 GB 中,一对溶质原子之间的相互作用取决于蛋白质/溶剂边界的形状,因此,GB 是一种多体势。对于计算密集型应用,该模型通常通过引入平均、天然样的蛋白质/溶剂边界进一步简化,从而去除多体性质。我们研究了一种用于酸碱计算和蛋白质设计的方法,该方法使用蒙特卡罗模拟,其中侧链可以探索构象、结合/释放质子或突变。波动的蛋白质/溶剂介电边界以数值精确的方式(在 GB 框架内)进行处理,与平均边界形成对比。其新颖之处在于它在保留固定边界给出的残基对复杂度的同时捕捉多体特征。该方法已在 Proteus 蛋白质设计软件中实现。它为九种蛋白质的酸碱常数带来了略微但系统的改善,为三个 PDZ 结构域的计算设计带来了显著的改善。它消除了模型不确定性的一个来源,这将有助于分析其他模型限制。© 2017 威利父子公司

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