Faculty for Chemistry and Pharmacy, Ludwig-Maximilians-University Munich, Butenandtstr. 5-13, 81377, Munich, Germany.
Faculty of Chemistry and Chemical Biology, Technical University Dortmund, Otto-Hahn-Straße 4a, 44227, Dortmund, Germany.
J Biomol NMR. 2020 Jan;74(1):71-82. doi: 10.1007/s10858-019-00291-z. Epub 2019 Dec 13.
Non-uniform sampling has been successfully used for solution and solid-state NMR of homogeneous samples. In the solid state, protein samples are often dominated by inhomogeneous contributions to the homogeneous line widths. In spite of different technical strategies for peak reconstruction by different methods, we validate that NUS can generally be used also for such situations where spectra are made up of complex peak shapes rather than Lorentian lines. Using the RMSD between subsampled and reconstructed data and those spectra obtained with uniform sampling for a sample comprising a wide conformational distribution, we quantitatively evaluate the identity of inhomogeneous peak patterns. The evaluation comprises Iterative Soft Thresholding (hmsIST implementation) as a method explicitly not assuming Lorentian lineshapes, as well as Sparse Multidimensional Iterative Lineshape Enhanced (SMILE) algorithm and Signal Separation Algorithm (SSA) reconstruction, which do work on the basis of Lorentian lineshape models, with different sampling densities. Even though individual peculiarities are apparent, all methods turn out principally viable to reconstruct the heterogeneously broadened peak shapes.
非均匀采样已成功用于同质样品的溶液和固态 NMR 研究。在固态中，蛋白质样品的均匀线宽通常主要由不均匀贡献主导。尽管不同方法采用不同的技术策略进行峰重建，但我们验证了 NUS 通常也可用于由复杂峰形而不是洛伦兹线组成的光谱的情况。我们使用均方根偏差（RMSD）在子采样和重建数据之间进行比较，以及使用具有均匀采样的那些光谱用于包含广泛构象分布的样品，对不均匀峰模式的相似性进行定量评估。评估包括迭代软阈值（hmsIST 实现），这是一种不明确假设洛伦兹线形状的方法，以及稀疏多维迭代线形状增强（SMILE）算法和信号分离算法（SSA）重建，这些方法基于洛伦兹线形状模型，具有不同的采样密度。尽管存在明显的个别特点，但所有方法在重建异质展宽的峰形方面都表现出基本可行性。
