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通过预测其氧化态来研究未表征的半胱氨酸损失背后的分子机制。

Investigating the Molecular Mechanisms Behind Uncharacterized Cysteine Losses from Prediction of Their Oxidation State.

作者信息

Raimondi Daniele, Orlando Gabriele, Messens Joris, Vranken Wim F

机构信息

Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.

Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.

出版信息

Hum Mutat. 2017 Jan;38(1):86-94. doi: 10.1002/humu.23129. Epub 2016 Oct 13.

Abstract

Cysteines are among the rarest amino acids in nature, and are both functionally and structurally very important for proteins. The ability of cysteines to form disulfide bonds is especially relevant, both for constraining the folded state of the protein and for performing enzymatic duties. But how does the variation record of human proteins reflect their functional importance and structural role, especially with regard to deleterious mutations? We created HUMCYS, a manually curated dataset of single amino acid variants that (1) have a known disease/neutral phenotypic outcome and (2) cause the loss of a cysteine, in order to investigate how mutated cysteines relate to structural aspects such as surface accessibility and cysteine oxidation state. We also have developed a sequence-based in silico cysteine oxidation predictor to overcome the scarcity of experimentally derived oxidation annotations, and applied it to extend our analysis to classes of proteins for which the experimental determination of their structure is technically challenging, such as transmembrane proteins. Our investigation shows that we can gain insights into the reason behind the outcome of cysteine losses in otherwise uncharacterized proteins, and we discuss the possible molecular mechanisms leading to deleterious phenotypes, such as the involvement of the mutated cysteine in a structurally or enzymatically relevant disulfide bond.

摘要

半胱氨酸是自然界中最为稀有的氨基酸之一,对蛋白质而言,它在功能和结构上都非常重要。半胱氨酸形成二硫键的能力尤为关键,这对于限制蛋白质的折叠状态以及执行酶促功能都具有重要意义。但是,人类蛋白质的变异记录如何反映其功能重要性和结构作用,特别是在有害突变方面呢?我们创建了HUMCYS,这是一个经过人工整理的单氨基酸变体数据集,其中的变体(1)具有已知的疾病/中性表型结果,并且(2)导致半胱氨酸缺失,目的是研究突变的半胱氨酸与诸如表面可及性和半胱氨酸氧化状态等结构方面之间的关系。我们还开发了一种基于序列的计算机半胱氨酸氧化预测器,以克服实验得出的氧化注释稀缺的问题,并将其应用于扩展我们对结构测定在技术上具有挑战性的蛋白质类别(如跨膜蛋白)的分析。我们的研究表明,我们能够深入了解在其他未表征的蛋白质中半胱氨酸缺失结果背后的原因,并且我们讨论了导致有害表型的可能分子机制,例如突变的半胱氨酸参与结构或酶促相关的二硫键。

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