Natively Disordered Proteins

P. Romero, Z. Obradovic, A. Dunker
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引用次数: 271

Abstract

Proteins can exist in at least three forms: the ordered form (solid-like), the partially folded form (collapsed, molten globule-like or liquid-like) and the extended form (extended, random coil-like or gas-like). The protein trinity hypothesis has two components: (i) a given native protein can be in any one of the three forms, depending on the sequence and the environment; and (ii) function can arise from any one of the three forms or from transitions between them. In this study, bioinformatics and data mining were used to investigate intrinsic disorder in proteins and develop neural network-based predictors of natural disordered regions (PONDR) that can discriminate between ordered and disordered residues with up to 84% accuracy. Predictions of intrinsic disorder indicate that the three kingdoms follow the disorder ranking eubacteria < archaebacteria ≪ eukaryotes, with approximately half of eukaryotic proteins predicted to contain substantial regions of intrinsic disorder. Many of the known disordered regions are involved in signalling, regulation or control. Involvement of highly flexible or disordered regions in signalling is logical: a flexible sensor more readily undergoes conformational change in response to environmental perturbations than does a rigid one. Thus, the increased disorder in the eukaryotes is likely the direct result of an increased need for signalling and regulation in nucleated organisms. PONDR can also be used to detect molecular recognition elements that are disordered in the unbound state and become structured when bound to a biologically meaningful partner. Application of disorder predictions to cell-signalling, cancer-associated and control protein databases supports the widespread occurrence of protein disorder in these processes.
天然无序蛋白
蛋白质至少可以以三种形式存在:有序形式(固体状),部分折叠形式(塌陷,熔融球状或液体状)和扩展形式(扩展,随机线圈状或气体状)。蛋白质三位一体假说有两个组成部分:(i)给定的天然蛋白质可以是三种形式中的任何一种,取决于序列和环境;(ii)函数可以由三种形式中的任何一种或它们之间的转换产生。在这项研究中,生物信息学和数据挖掘被用于研究蛋白质的内在无序性,并开发基于神经网络的自然无序区(PONDR)预测器,该预测器可以区分有序和无序残基,准确率高达84%。对内在紊乱的预测表明,真核生物的紊乱程度依次为真细菌<古细菌<真核生物,大约一半的真核蛋白质被预测含有大量的内在紊乱区域。许多已知的紊乱区域与信号传导、调节或控制有关。高柔性或无序区域参与信号传导是合乎逻辑的:柔性传感器比刚性传感器更容易在环境扰动下经历构象变化。因此,真核生物中紊乱的增加可能是有核生物对信号和调节需求增加的直接结果。PONDR还可以用于检测在未结合状态下无序的分子识别元件,当与生物学上有意义的伴侣结合时,它们会变得结构化。将疾病预测应用于细胞信号、癌症相关和控制蛋白数据库,支持这些过程中蛋白质疾病的广泛发生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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