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“溶剂氢键束缚”:生物分子能量学中极性去溶剂化的一种新模型。

"Solvent hydrogen-bond occlusion": A new model of polar desolvation for biomolecular energetics.

机构信息

Center for Computational Biology, University of Kansas, 2030 Becker Dr, Lawrence, Kansas, 66045-7534.

Computational Chemical Biology Core, University of Kansas, 2030 Becker Dr, Lawrence, Kansas, 66045-7534.

出版信息

J Comput Chem. 2017 Jun 15;38(16):1321-1331. doi: 10.1002/jcc.24740. Epub 2017 Mar 20.

Abstract

Water engages in two important types of interactions near biomolecules: it forms ordered "cages" around exposed hydrophobic regions, and it participates in hydrogen bonds with surface polar groups. Both types of interaction are critical to biomolecular structure and function, but explicitly including an appropriate number of solvent molecules makes many applications computationally intractable. A number of implicit solvent models have been developed to address this problem, many of which treat these two solvation effects separately. Here, we describe a new model to capture polar solvation effects, called SHO ("solvent hydrogen-bond occlusion"); our model aims to directly evaluate the energetic penalty associated with displacing discrete first-shell water molecules near each solute polar group. We have incorporated SHO into the Rosetta energy function, and find that scoring protein structures with SHO provides superior performance in loop modeling, virtual screening, and protein structure prediction benchmarks. These improvements stem from the fact that SHO accurately identifies and penalizes polar groups that do not participate in hydrogen bonds, either with solvent or with other solute atoms ("unsatisfied" polar groups). We expect that in future, SHO will enable higher-resolution predictions for a variety of molecular modeling applications. © 2017 Wiley Periodicals, Inc.

摘要

水在靠近生物分子的地方会发生两种重要的相互作用

它在暴露的疏水区周围形成有序的“笼状”结构,并且与表面极性基团形成氢键。这两种相互作用对生物分子的结构和功能都至关重要,但明确包含适当数量的溶剂分子会使许多应用的计算变得难以处理。已经开发了许多隐式溶剂模型来解决这个问题,其中许多模型分别处理这两种溶剂化效应。在这里,我们描述了一种新的模型来捕捉极性溶剂化效应,称为 SHO(“溶剂氢键阻塞”);我们的模型旨在直接评估与置换每个溶质极性基团附近离散的第一层水分子相关的能量罚分。我们已经将 SHO 纳入 Rosetta 能量函数中,并发现使用 SHO 对蛋白质结构进行评分可以在环建模、虚拟筛选和蛋白质结构预测基准测试中提供更好的性能。这些改进源于 SHO 准确识别和惩罚不参与氢键的极性基团(无论是与溶剂还是与其他溶质原子)的事实,这些极性基团被称为“未满足”的极性基团。我们预计,在未来,SHO 将能够为各种分子建模应用提供更高分辨率的预测。 © 2017 威利父子公司

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