Suppr超能文献

内在无序蛋白质的结合机制:来自实验研究和结构预测的见解。

Binding mechanisms of intrinsically disordered proteins: Insights from experimental studies and structural predictions.

作者信息

Orand Thibault, Jensen Malene Ringkjøbing

机构信息

Univ. Grenoble Alpes, CEA, CNRS, IBS, Grenoble, France.

Univ. Grenoble Alpes, CEA, CNRS, IBS, Grenoble, France.

出版信息

Curr Opin Struct Biol. 2025 Feb;90:102958. doi: 10.1016/j.sbi.2024.102958. Epub 2024 Dec 30.

Abstract

Advances in the characterization of intrinsically disordered proteins (IDPs) have unveiled a remarkably complex and diverse interaction landscape, including coupled folding and binding, highly dynamic complexes, multivalent interactions, and even interactions between entirely disordered proteins. Here we review recent examples of IDP binding mechanisms elucidated by experimental techniques such as nuclear magnetic resonance spectroscopy, single-molecule Förster resonance energy transfer, and stopped-flow fluorescence. These techniques provide insights into the structural details of transition pathways and complex intermediates, and they capture the dynamics of IDPs within complexes. Furthermore, we discuss the growing role of artificial intelligence, exemplified by AlphaFold, in identifying interaction sites within IDPs and predicting their bound-state structures. Our review highlights the powerful complementarity between experimental methods and artificial intelligence-based approaches in advancing our understanding of the intricate interaction landscape of IDPs.

摘要

内在无序蛋白质(IDP)表征方面的进展揭示了一个极其复杂和多样的相互作用格局,包括耦合折叠与结合、高度动态的复合物、多价相互作用,甚至完全无序蛋白质之间的相互作用。在这里,我们回顾了通过核磁共振光谱、单分子Förster共振能量转移和停流荧光等实验技术阐明的IDP结合机制的最新实例。这些技术提供了对过渡途径和复合中间体结构细节的深入了解,并捕捉了复合物中IDP的动力学。此外,我们讨论了以AlphaFold为例的人工智能在识别IDP内的相互作用位点和预测其结合态结构方面日益重要的作用。我们的综述强调了实验方法与基于人工智能的方法之间强大的互补性,有助于推进我们对IDP复杂相互作用格局的理解。

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