Protein structural information derived from NMR chemical shift with the neural network program TALOS-N.
Protein structural information derived from NMR chemical shift with the neural network program TALOS-N.
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DOI：
10.1007/978-1-4939-2239-0_2
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Bax, Ad
中科院分区：
文献类型：
--
作者：
Shen, Yang;Bax, Ad
化学位移是在任何蛋白质结构研究的第一阶段通过NMR光谱学获得的。已知化学位移受到广泛的结构因素的影响，并且基于人工神经网络的TALOS-N程序已被训练以从1H、15 N和13 C位移提取主链和侧链扭转角。该程序是相当稳健的，并且除了可靠地预测二级结构之外，通常产生超过90%的残基的主链扭转角，以及这些残基中约一半的侧链X1旋转异构体信息。TALOS-N在蛋白质DinI中的应用举例说明，并将通过TALOS-N分析从其主链和13 C β核的测量化学位移获得的扭转角与先前实验确定的结构中观察到的扭转角进行比较。该程序也特别适用于生成扭转角约束，然后可以在标准NMR蛋白质结构计算过程中使用。
Chemical shifts are obtained at the first stage of any protein structural study by NMR spectroscopy. Chemical shifts are known to be impacted by a wide range of structural factors and the artificial neural network based TALOS-N program has been trained to extract backbone and sidechain torsion angles from 1H, 15N and 13C shifts. The program is quite robust, and typically yields backbone torsion angles for more than 90% of the residues, and sidechain χ1 rotamer information for about half of these, in addition to reliably predicting secondary structure. The use of TALOS-N is illustrated for the protein DinI, and torsion angles obtained by TALOS-N analysis from the measured chemical shifts of its backbone and 13Cβ nuclei are compared to those seen in a prior, experimentally determined structure. The program is also particularly useful for generating torsion angle restraints, which then can be used during standard NMR protein structure calculations.