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arXiv 2409.02240physics.bio-phq-bio.BM
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研究固有无序蛋白质及其复合物的计算方法

Computational Methods to Investigate Intrinsically Disordered Proteins and their Complexes

Zi Hao Liu, Maria Tsanai, Oufan Zhang, Julie Forman-Kay, Teresa Head-Gordon

AI总结:

本文综述了研究固有无序蛋白质及其复合物和相分离状态的计算方法,涵盖统计模型、物理方法和机器学习手段,用于结构系综生成与实验验证。

摘要:

1999年,Wright和Dyson强调了一个事实:所有生物体蛋白质组的大部分由在生理条件下缺乏球状折叠结构的蛋白质序列组成。此后,生物物理学界在揭示固有无序蛋白质(IDPs)和固有无序区域(IDRs)复杂的结构与动态特征方面取得了重大进展。与晶体学光束线及其在简化折叠蛋白结构获取方面的作用不同,一种针对IDPs/IDRs的整合实验与计算方法已经出现。在本篇Perspective中,我们旨在对当前用于IDPs和IDRs及其最新涉及的复合物和相分离状态的计算工具提供一个稳健的概述,包括统计模型、基于物理的方法以及机器学习方法,这些方法能够生成结构系综并针对多种溶液实验数据类型进行验证。

英文摘要:

In 1999 Wright and Dyson highlighted the fact that large sections of the proteome of all organisms are comprised of protein sequences that lack globular folded structures under physiological conditions. Since then the biophysics community has made significant strides in unraveling the intricate structural and dynamic characteristics of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs). Unlike crystallographic beamlines and their role in streamlining acquisition of structures for folded proteins, an integrated experimental and computational approach aimed at IDPs/IDRs has emerged. In this Perspective we aim to provide a robust overview of current computational tools for IDPs and IDRs, and most recently their complexes and phase separated states, including statistical models, physics-based approaches, and machine learning methods that permit structural ensemble generation and validation against many solution experimental data types.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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