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使用甲基-TROSY、稀疏数据采集和多维分解的高分辨率四维1H-13C NOE光谱学。

High-resolution four-dimensional 1H-13C NOE spectroscopy using methyl-TROSY, sparse data acquisition, and multidimensional decomposition.

作者信息

Tugarinov Vitali, Kay Lewis E, Ibraghimov Ilghiz, Orekhov Vladislav Yu

机构信息

Protein Engineering Network Centers of Excellence and Departments of Medical Genetics, Biochemistry and Chemistry, The University of Toronto, Toronto, Ontario M5S 1A8, Canada.

出版信息

J Am Chem Soc. 2005 Mar 2;127(8):2767-75. doi: 10.1021/ja044032o.

DOI:10.1021/ja044032o
PMID:15725035
Abstract

An approach for recording four-dimensional (4D) methyl (1)H-(13)C-(13)C-(1)H NOESY spectra with high resolution and sensitivity is presented and applied to Malate Synthase G (723 residues, 82 kDa). Sensitivity and resolution have been optimized using a highly deuterated, methyl-protonated sample in concert with methyl-TROSY, sparse data sampling in the three indirect dimensions, and 4D spectral reconstruction using multidimensional decomposition (MDD). A sparse data acquisition protocol is introduced that ensures that sufficiently long indirect acquisition times can be employed to exploit the decreased relaxation rates associated with methyl-TROSY, without increasing the duration of the 4D experiment beyond acceptable measurement times. In this manner, only a fraction ( approximately 30%) of the experimental data that would normally be needed to achieve a spectrum of high resolution is acquired. The reconstructed 4D spectrum is of similar resolution and sensitivity to three-dimensional (3D) (13)C-edited NOE spectra, is straightforward to analyze, and resolves ambiguities that emerge when 3D data sets only are considered.

摘要

本文介绍了一种用于记录具有高分辨率和灵敏度的四维(4D)甲基(1)H-(13)C-(13)C-(1)H NOESY谱的方法,并将其应用于苹果酸合酶G(723个残基,82 kDa)。通过使用高度氘代、甲基质子化的样品,结合甲基-TROSY、在三个间接维度上的稀疏数据采样以及使用多维分解(MDD)的4D谱重建,优化了灵敏度和分辨率。引入了一种稀疏数据采集协议,该协议确保可以采用足够长的间接采集时间来利用与甲基-TROSY相关的降低的弛豫速率,而不会将4D实验的持续时间增加到超出可接受的测量时间。通过这种方式,仅获取了通常获得高分辨率谱所需实验数据的一小部分(约30%)。重建的4D谱具有与三维(3D)(13)C编辑的NOE谱相似的分辨率和灵敏度,易于分析,并且解决了仅考虑3D数据集时出现的模糊性问题。

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