Lieutaud Philippe, Ferron François, Uversky Alexey V, Kurgan Lukasz, Uversky Vladimir N, Longhi Sonia
Aix-Marseille Université, AFMB UMR, Marseille, France; CNRS, AFMB UMR, Marseille, France.
Center for Data Analytics and Biomedical Informatics, Department of Computer and Information Sciences, College of Science and Technology, Temple University , Philadelphia, PA, USA.
Intrinsically Disord Proteins. 2016 Dec 21;4(1):e1259708. doi: 10.1080/21690707.2016.1259708. eCollection 2016.
In the last 2 decades it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins lack a stable 3D structure, are ubiquitous and fulfill essential biological functions. Their conformational heterogeneity is encoded in their amino acid sequences, thereby allowing intrinsically disordered proteins or regions to be recognized based on properties of these sequences. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to structural determination with X-ray crystallization. This article discusses a comprehensive selection of databases and methods currently employed to disseminate experimental and putative annotations of disorder, predict disorder and identify regions involved in induced folding. It also provides a set of detailed instructions that should be followed to perform computational analysis of disorder.
在过去二十年中,越来越明显的是,大量蛋白质要么完全无序,要么部分无序。内在无序蛋白质缺乏稳定的三维结构,普遍存在并履行重要的生物学功能。它们的构象异质性编码在其氨基酸序列中,从而使得内在无序蛋白质或区域能够基于这些序列的特性被识别。无序区域的识别有助于蛋白质的功能注释,并且对于划定适合通过X射线结晶进行结构测定的蛋白质结构域边界至关重要。本文讨论了目前用于传播无序的实验和推定注释、预测无序以及识别参与诱导折叠区域的一系列综合数据库和方法。它还提供了一套进行无序计算分析时应遵循的详细说明。