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通过对大肠杆菌蛋白质整体集合进行聚集分析揭示的双峰蛋白质溶解度分布。

Bimodal protein solubility distribution revealed by an aggregation analysis of the entire ensemble of Escherichia coli proteins.

作者信息

Niwa Tatsuya, Ying Bei-Wen, Saito Katsuyo, Jin WenZhen, Takada Shoji, Ueda Takuya, Taguchi Hideki

机构信息

Department of Medical Genome Sciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba 277-8562, Japan.

出版信息

Proc Natl Acad Sci U S A. 2009 Mar 17;106(11):4201-6. doi: 10.1073/pnas.0811922106. Epub 2009 Feb 27.

Abstract

Protein folding often competes with intermolecular aggregation, which in most cases irreversibly impairs protein function, as exemplified by the formation of inclusion bodies. Although it has been empirically determined that some proteins tend to aggregate, the relationship between the protein aggregation propensities and the primary sequences remains poorly understood. Here, we individually synthesized the entire ensemble of Escherichia coli proteins by using an in vitro reconstituted translation system and analyzed the aggregation propensities. Because the reconstituted translation system is chaperone-free, we could evaluate the inherent aggregation propensities of thousands of proteins in a translation-coupled manner. A histogram of the solubilities, based on data from 3,173 translated proteins, revealed a clear bimodal distribution, indicating that the aggregation propensities are not evenly distributed across a continuum. Instead, the proteins can be categorized into 2 groups, soluble and aggregation-prone proteins. The aggregation propensity is most prominently correlated with the structural classification of proteins, implying that the prediction of aggregation propensity requires structural information about the protein.

摘要

蛋白质折叠常常与分子间聚集相互竞争,在大多数情况下,分子间聚集会不可逆地损害蛋白质功能,例如包涵体的形成。虽然根据经验已经确定某些蛋白质易于聚集,但蛋白质聚集倾向与一级序列之间的关系仍知之甚少。在这里,我们使用体外重构翻译系统单独合成了大肠杆菌蛋白质的整个集合,并分析了聚集倾向。由于重构翻译系统不含伴侣蛋白,我们可以以翻译偶联的方式评估数千种蛋白质的固有聚集倾向。基于3173种翻译后蛋白质的数据绘制的溶解度直方图显示出明显的双峰分布,表明聚集倾向并非在连续范围内均匀分布。相反,蛋白质可分为两组,即可溶性蛋白质和易于聚集的蛋白质。聚集倾向与蛋白质的结构分类最显著相关,这意味着聚集倾向的预测需要有关蛋白质的结构信息。

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