Prediction of anisotropic NMR data without knowledge of alignment medium structure by surface decomposition
Prediction of anisotropic NMR data without knowledge of alignment medium structure by surface decomposition
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在不了解排列介质结构的情况下通过表面分解预测各向异性 NMR 数据
DOI：
10.1039/d2cp02621j
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发表时间：
2022
影响因子：
3.3
通讯作者：
Williamson, R. Thomas
中科院分区：
文献类型：
--
作者：
Liu, Yizhou;Ndukwe, Ikenna E.;Reibarkh, Mikhail;Martin, Gary E.;Williamson, R. Thomas
直接从溶质-介质相互作用预测各向异性核磁共振数据具有重要的理论和实际意义，特别是对于复杂有机分子和天然产物的结构阐明、构型分析和构象研究。当前的预测方法需要对准介质的显式结构模型：对于小有机分子所需的规模来说，这一要求要么是不可能的，要么是不切实际的。在这里，我们为参数化协议制定了一个全面的数学框架，该协议将介质的任意表面解卷积为几个简单的局部景观，这些景观通过特定的方向顺序参数分布在介质表面上。这些局部景观的形状和有序参数是通过拟合确定的，该拟合最大化了实验确定的各向异性 NMR 可测量值与其预测对应物之间的一致性，从而避免了对全局介质形态的先验知识的需要。与当前方法相比，该方法在预测各向异性 NMR 值的准确性方面实现了显着提高，如本文用 16 种天然产物所证明的。此外，由于这种形式通过结合不同化合物的各向异性 NMR 数据来提取介质的结构共性，因此随着更多的实验数据可用于进一步重新优化拟合参数，其鲁棒性和准确性有望提高。
Prediction of anisotropic NMR data directly from solute-medium interaction is of significant theoretical and practical interest, particularly for structure elucidation, configurational analysis and conformational studies of complex organic molecules and natural products. Current prediction methods require an explicit structural model of the alignment medium: a requirement either impossible or impractical on a scale necessary for small organic molecules. Here we formulate a comprehensive mathematical framework for a parametrization protocol that deconvolutes an arbitrary surface of the medium into several simple local landscapes that are distributed over the medium's surface by specific orientational order parameters. The shapes and order parameters of these local landscapes are determined via fitting that maximizes the congruence between experimentally determined anisotropic NMR measurables and their predicted counterparts, thus avoiding the need for an a priori knowledge of the global medium morphology. This method achieves substantial improvements in the accuracy of predicted anisotropic NMR values compared to current methods, as demonstrated herein with sixteen natural products. Furthermore, because this formalism extracts structural commonalities of the medium by combining anisotropic NMR data from different compounds, its robustness and accuracy are expected to improve as more experimental data become available for further re-optimization of fitting parameters.