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变性蛋白质的化学位移预测。

Chemical shift prediction for denatured proteins.

出版信息

J Biomol NMR. 2013 Feb;55(2):201-9. doi: 10.1007/s10858-012-9702-x. Epub 2013 Jan 8.

Abstract

While chemical shift prediction has played an important role in aspects of protein NMR that include identification of secondary structure, generation of torsion angle constraints for structure determination, and assignment of resonances in spectra of intrinsically disordered proteins, interest has arisen more recently in using it in alternate assignment strategies for crosspeaks in (1)H-(15)N HSQC spectra of sparsely labeled proteins. One such approach involves correlation of crosspeaks in the spectrum of the native protein with those observed in the spectrum of the denatured protein, followed by assignment of the peaks in the latter spectrum. As in the case of disordered proteins, predicted chemical shifts can aid in these assignments. Some previously developed empirical formulas for chemical shift prediction have depended on basis data sets of 20 pentapeptides. In each case the central residue was varied among the 20 amino common acids, with the flanking residues held constant throughout the given series. However, previous choices of solvent conditions and flanking residues make the parameters in these formulas less than ideal for general application to denatured proteins. Here, we report (1)H and (15)N shifts for a set of alanine based pentapeptides under the low pH urea denaturing conditions that are more appropriate for sparse label assignments. New parameters have been derived and a Perl script was created to facilitate comparison with other parameter sets. A small, but significant, improvement in shift predictions for denatured ubiquitin is demonstrated.

摘要

虽然化学位移预测在蛋白质 NMR 的各个方面都发挥了重要作用,包括确定二级结构、为结构确定生成扭转角约束以及对无规卷曲蛋白质的谱中的共振进行分配,但最近人们对其在(1)H-(15)N HSQC 谱中交叉峰的替代分配策略中的应用产生了兴趣稀疏标记蛋白质。一种这样的方法涉及将天然蛋白质谱中的交叉峰与变性蛋白质谱中的观察到的交叉峰相关联,然后对后者谱中的峰进行分配。与无规卷曲蛋白质的情况一样,预测的化学位移可以帮助进行这些分配。一些以前开发的化学位移预测经验公式依赖于包含 20 个五肽的基础数据集。在每种情况下,中央残基在 20 种常见氨基酸中变化,在给定系列中侧翼残基保持不变。然而,以前溶剂条件和侧翼残基的选择使得这些公式中的参数不太适合普遍应用于变性蛋白质。在这里,我们报告了一组基于丙氨酸的五肽在低 pH 脲变性条件下的(1)H 和(15)N 位移,这些条件更适合稀疏标签分配。已经推导出了新的参数,并创建了一个 Perl 脚本以方便与其他参数集进行比较。证明了对变性泛素的位移预测有较小但显著的改善。

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Chemical shift prediction for denatured proteins.变性蛋白质的化学位移预测。
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