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基于 NMR 的天然产物结构确定:浮动手性距离几何计算如何简化与 2 个非对映异构体的赌博。

NMR-Based Configurational Assignments of Natural Products: How Floating Chirality Distance Geometry Calculations Simplify Gambling with 2 Diastereomers.

机构信息

Clemens-Schöpf-Institut für Organische Chemie und Biochemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 4, 64287 Darmstadt, Germany.

Alfred-Wegener-Institut für Polar- und Meeresforschung in der Helmholtz-Gemeinschaft, Am Handelshafen 12, 27570 Bremerhaven, Germany.

出版信息

J Nat Prod. 2022 Jul 22;85(7):1837-1849. doi: 10.1021/acs.jnatprod.2c00427. Epub 2022 Jul 12.

Abstract

Using NMR data, the assignment of the correct 3D configuration and conformation to unknown natural products is of pivotal importance in pharmaceutical and medicinal chemistry. In this report, we quantify the probability of configurational assignments to judge the quality of structural elucidations using Bayesian inference in combination with floating-chirality distance geometry simulations. Based on reference-free NOE/ROE data, residual dipolar couplings (RDCs), and residual quadrupolar couplings (RQCs) in various combinations, we demonstrate how the relative configurations of three natural compounds, namely, jatrohemiketal (), artemisinin (), and Taxol (), can be unambiguously established without the necessity to carry out time-consuming DFT-based configurational and conformational analyses. Our results quantitatively describe how reliably molecular geometries can be inferred from experimental NMR data, thereby unequivocally unveiling remaining assignment ambiguities. The methodology presented here will dramatically reduce the risk of incorrect structural assignments based on the overinterpretation of incomplete data and DFT-based structure models in chemistry.

摘要

利用 NMR 数据,为未知天然产物分配正确的三维构型和构象对于药物和医药化学至关重要。在本报告中,我们通过贝叶斯推断结合浮动手性距离几何模拟来量化构象分配的概率,以判断结构解析的质量。基于无参考的 NOE/ROE 数据、残差偶极耦合(RDC)和残差四极耦合(RQC)的各种组合,我们展示了如何在无需进行耗时的基于 DFT 的构型和构象分析的情况下,明确确定三个天然化合物(分别为 jatrorrhizome hemiketal ()、青蒿素 () 和 Taxol ())的相对构型。我们的结果定量描述了如何从实验 NMR 数据中可靠地推断分子几何形状,从而明确揭示了剩余的分配歧义。这里提出的方法将大大降低基于不完全数据和基于 DFT 的结构模型的过度解释的错误结构分配的风险,从而应用于化学领域。

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