通过31P核磁共振参数解读BI/BII DNA二分法之外的Dickerson-Drew DNA平衡。

IF 5.5 1区 化学 Q2 CHEMISTRY, PHYSICAL
Jiří Fukal, , , Miloš Buděšínský, , , Jakub Šebera, , , Marie Zgarbová, , , Petr Jurečka, , and , Vladimír Sychrovský*, 
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引用次数: 0

摘要

DNA双链是溶液中相互转换构象的动态集合。传统的核磁共振(NMR)数据解释通常通过假设一个主导结构来简化这种行为,但多个基态(例如不同的主干构象)可以共存。在这里,我们提出了一种方法,通过整合核苷酸构象分类(NtC) (Černý等人,核酸研究2020,48,6367-6381)与分子动力学(MD)模拟来改进Dickerson-Drew DNA中31P NMR数据的解释。通过将主干构象精细分类为不同的ntc定义状态,并使用MD预测它们的种群,我们实现了实验核磁共振观测值与DNA结构动力学异质性之间更细致的对应关系。该框架的应用证明了核磁共振数据解释的根本性改进,从而提高了推断溶液中DNA构象平衡的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deciphering Dickerson–Drew DNA Equilibrium beyond the BI/BII DNA Dichotomy by Interpretation of 31P NMR Parameters

DNA duplexes exist as dynamic ensembles of interconverting conformations in solution. Conventional nuclear magnetic resonance (NMR) data interpretation often simplifies this behavior by assuming one dominant structure, but multiple substates (such as different backbone conformers) can coexist. Here, we present an approach that refines the interpretation of 31P NMR data in the Dickerson–Drew DNA by integrating a nucleotide conformational classification (NtC) (Černý et al., Nucleic Acids Research 2020, 48, 6367–6381) with molecular dynamics (MD) simulations. By finely classifying backbone conformers into distinct NtC-defined states and using MD to predict their populations, we achieve a more nuanced correspondence between experimental NMR observables and DNA structure-dynamical heterogeneity. Application of this framework demonstrates a radical improvement of NMR data interpretation, thereby enhancing the reliability of deducing DNA conformational equilibria in solution.

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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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