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极化连续介质模型为具有挑战性的系统中基于片段的化学位移预测提供了一种有效的静电嵌入模型。

Polarizable continuum models provide an effective electrostatic embedding model for fragment-based chemical shift prediction in challenging systems.

作者信息

Unzueta Pablo A, Beran Gregory J O

机构信息

Department of Chemistry, Univeristy of California, Riverside, California, USA.

出版信息

J Comput Chem. 2020 Oct 5;41(26):2251-2265. doi: 10.1002/jcc.26388. Epub 2020 Aug 4.

Abstract

Ab initio nuclear magnetic resonance chemical shift prediction provides an important tool for interpreting and assigning experimental spectra, but it becomes computationally prohibitive in large systems. The computational costs can be reduced considerably by fragmentation of the large system into a series of contributions from many smaller subsystems. However, the presence of charged functional groups and the need to partition the system across covalent bonds create complications in biomolecules that typically require the use of large fragments and careful descriptions of the electrostatic environment. The present work shows how a model that combines chemical shielding contributions from non-overlapping monomer and dimer fragments embedded in a polarizable continuum model provides a simple, easy-to-implement, and computationally inexpensive approach for predicting chemical shifts in complex systems. The model's performance proves rather insensitive to the continuum dielectric constant, making the selection of the optimal embedding dielectric less critical. The PCM-embedded fragment model is demonstrated to perform well across systems ranging from molecular crystals to proteins.

摘要

从头算核磁共振化学位移预测为解释和指定实验光谱提供了一个重要工具,但在大型系统中计算量变得过高。通过将大型系统分解为许多较小子系统的一系列贡献,可以显著降低计算成本。然而,带电官能团的存在以及在共价键上划分系统的需求给生物分子带来了复杂性,这通常需要使用大的片段并仔细描述静电环境。目前的工作展示了一种模型,该模型结合了嵌入可极化连续介质模型中的非重叠单体和二聚体片段的化学屏蔽贡献,为预测复杂系统中的化学位移提供了一种简单、易于实现且计算成本低的方法。该模型的性能对连续介质介电常数相当不敏感,使得选择最佳嵌入介电常数的重要性降低。PCM嵌入片段模型在从分子晶体到蛋白质的各种系统中都表现良好。

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