State Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Electronic Science, Xiamen University, Xiamen, Fujian 361005, China.
J Phys Chem Lett. 2021 Nov 4;12(43):10622-10630. doi: 10.1021/acs.jpclett.1c03063. Epub 2021 Oct 26.
Multidimensional NMR spectroscopy provides a powerful tool for structure elucidation and dynamic analysis of complex samples, particularly for biological macromolecules. Multidimensional sparse sampling effectively accelerates NMR experiments while an efficient reconstruction method is generally required for unraveling spectra. Various reconstruction methods were proposed for pure Fourier NMR (only involving chemical shifts and couplings detection). However, reconstruction concerned with Laplace-related NMR (i.e., involving relaxation or diffusion detection) is more challenging due to its ill-posed property. The existing Laplace-related NMR sparse sampling reconstruction methods suffer from poor resolution and possible artifacts in the resulting spectra owing to the pitfalls of the optimization algorithms. Herein, we propose a general approach for fast high-resolution reconstruction of multidimensional sparse sampling NMR, including pure Fourier, mixed Fourier-Laplace, and pure Laplace NMR, benefiting from the comprehensive sparse constraint and effective optimization algorithm and thus showing the promising prospects of multidimensional NMR.
多维核磁共振波谱为复杂样品的结构阐明和动态分析提供了强大的工具，特别是对于生物大分子。多维稀疏采样有效地加速了 NMR 实验，而一般需要有效的重建方法来解析光谱。已经提出了各种用于纯傅里叶 NMR（仅涉及化学位移和耦合检测）的重建方法。然而，由于拉普拉斯相关 NMR（即涉及弛豫或扩散检测）的不适定性，其相关的重建更具挑战性。由于优化算法的缺陷，现有的拉普拉斯相关 NMR 稀疏采样重建方法在重建的光谱中存在分辨率差和可能的伪影的问题。在此，我们提出了一种用于快速高分辨率重建多维稀疏采样 NMR 的通用方法，包括纯傅里叶、混合傅里叶-拉普拉斯和纯拉普拉斯 NMR，得益于全面的稀疏约束和有效的优化算法，从而展示了多维 NMR 的广阔前景。
