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从一级序列预测蛋白质的聚集倾向:蛋白质组的聚集特性。

Prediction of the aggregation propensity of proteins from the primary sequence: aggregation properties of proteomes.

机构信息

Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular and Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain.

出版信息

Biotechnol J. 2011 Jun;6(6):674-85. doi: 10.1002/biot.201000331. Epub 2011 Apr 29.

Abstract

In the cell, protein folding into stable globular conformations is in competition with aggregation into non-functional and usually toxic structures, since the biophysical properties that promote folding also tend to favor intermolecular contacts, leading to the formation of β-sheet-enriched insoluble assemblies. The formation of protein deposits is linked to at least 20 different human disorders, ranging from dementia to diabetes. Furthermore, protein deposition inside cells represents a major obstacle for the biotechnological production of polypeptides. Importantly, the aggregation behavior of polypeptides appears to be strongly influenced by the intrinsic properties encoded in their sequences and specifically by the presence of selective short regions with high aggregation propensity. This allows computational methods to be used to analyze the aggregation properties of proteins without the previous requirement for structural information. Applications range from the identification of individual amyloidogenic regions in disease-linked polypeptides to the analysis of the aggregation properties of complete proteomes. Herein, we review these theoretical approaches and illustrate how they have become important and useful tools in understanding the molecular mechanisms underlying protein aggregation.

摘要

在细胞中,蛋白质折叠成稳定的球状构象与聚集形成非功能且通常有毒的结构是竞争关系,因为促进折叠的生物物理特性也倾向于有利于分子间接触,导致富含β-折叠的不溶性组装体的形成。蛋白质沉积的形成与至少 20 种不同的人类疾病有关,范围从痴呆到糖尿病。此外,蛋白质在细胞内的沉积是生物技术生产多肽的主要障碍。重要的是,多肽的聚集行为似乎受到其序列中编码的固有特性的强烈影响,特别是受到具有高聚集倾向的选择性短区域的存在的影响。这使得可以使用计算方法来分析蛋白质的聚集特性,而无需先前对结构信息的要求。应用范围从鉴定与疾病相关多肽中的单个淀粉样蛋白区域到分析完整蛋白质组的聚集特性。本文综述了这些理论方法,并说明了它们如何成为理解蛋白质聚集分子机制的重要且有用的工具。

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