Suppr超能文献

天然无序蛋白质:功能与预测

Natively disordered proteins: functions and predictions.

作者信息

Romero Pedro, Obradovic Zoran, Dunker A Keith

机构信息

Center for Computational Biology and Bioinformatics, Indiana University-Purdue University Indianapolis, 714 North Senate Avenue, Indianapolis, IN 46202, USA.

出版信息

Appl Bioinformatics. 2004;3(2-3):105-13. doi: 10.2165/00822942-200403020-00005.

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.

摘要

蛋白质至少可以以三种形式存在

有序形式(类似固体)、部分折叠形式(塌陷的、熔球状或液体状)和伸展形式(伸展的、无规卷曲状或气体状)。蛋白质三位一体假设有两个组成部分:(i)给定的天然蛋白质可以处于三种形式中的任何一种,这取决于其序列和环境;(ii)功能可以源自三种形式中的任何一种,或者源自它们之间的转变。在本研究中,利用生物信息学和数据挖掘来研究蛋白质中的内在无序性,并开发基于神经网络的天然无序区域预测器(PONDR),该预测器能够以高达84%的准确率区分有序和无序残基。内在无序性的预测表明,三个生物界遵循无序程度的排序:真细菌<古细菌<<真核生物,预计约一半的真核生物蛋白质含有大量内在无序区域。许多已知的无序区域参与信号传导、调节或控制。高度灵活或无序的区域参与信号传导是合乎逻辑的:与刚性传感器相比,灵活的传感器更容易响应环境扰动而发生构象变化。因此,真核生物中无序性的增加可能是有核生物对信号传导和调节需求增加的直接结果。PONDR还可用于检测在未结合状态下无序但与生物学上有意义的伴侣结合时会变得结构化的分子识别元件。将无序性预测应用于细胞信号传导、癌症相关和调控蛋白数据库,支持了蛋白质无序在这些过程中的广泛存在。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验