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基于实验获得的氨基酸特定的骨架构象统计数据改进 ABSINTH 蛋白质力场。

Improvements to the ABSINTH Force Field for Proteins Based on Experimentally Derived Amino Acid Specific Backbone Conformational Statistics.

机构信息

Department of Biomedical Engineering and Center for Biological Systems Engineering , Washington University in St. Louis , One Brookings Drive , Campus Box 1097, St. Louis , Missouri 63130 , United States.

出版信息

J Chem Theory Comput. 2019 Feb 12;15(2):1367-1382. doi: 10.1021/acs.jctc.8b00573. Epub 2019 Jan 22.

Abstract

We present an improved version of the ABSINTH implicit solvation model and force field paradigm (termed ABSINTH-C) by incorporating a grid-based term that bootstraps against experimentally derived and computationally optimized conformational statistics for blocked amino acids. These statistics provide high-resolution descriptions of the intrinsic backbone dihedral angle preferences for all 20 amino acids. The original ABSINTH model generates Ramachandran plots that are too shallow in terms of the basin structures and too permissive in terms of dihedral angle preferences. We bootstrap against the reference optimized landscapes and incorporate CMAP-like residue-specific terms that help us reproduce the intrinsic dihedral angle preferences of individual amino acids. These corrections that lead to ABSINTH-C are achieved by balancing the incorporation of the new residue-specific terms with the accuracies inherent to the original ABSINTH model. We demonstrate the robustness of ABSINTH-C through a series of examples to highlight the preservation of accuracies as well as examples that demonstrate the improvements. Our efforts show how the recent experimentally derived and computationally optimized coil-library landscapes can be used as a touchstone for quantifying errors and making improvements to molecular mechanics force fields.

摘要

我们提出了 ABSINTH 隐溶剂模型和力场范式(称为 ABSINTH-C)的改进版本,方法是引入一个基于网格的项,该项基于针对受阻氨基酸进行实验得出和计算优化的构象统计数据进行引导。这些统计数据为所有 20 种氨基酸的固有骨架二面角偏好提供了高分辨率描述。原始的 ABSINTH 模型生成的 Ramachandran 图谱在盆地结构方面过于浅,在二面角偏好方面过于宽松。我们针对参考优化的景观进行引导,并引入类似于 CMAP 的残基特异性项,以帮助我们再现单个氨基酸的固有二面角偏好。通过平衡新的残基特异性项的引入与原始 ABSINTH 模型固有的准确性,实现了导致 ABSINTH-C 的这些修正。我们通过一系列示例展示了 ABSINTH-C 的稳健性,突出了准确性的保持以及展示改进的示例。我们的努力表明,如何使用最近从实验和计算优化中得出的无规卷曲文库景观作为量化误差和改进分子力学力场的基准。

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