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基于AlphaFold的核小体结合蛋白结构分析

AlphaFold-guided structural analyses of nucleosome binding proteins.

作者信息

Yang Xin, Zhu Haoqiang, Shi Liuxin, Song Tingrui, Gong Weibin, He Shunmin, Shan Shan, Xu Chunfu, Zhou Zheng

机构信息

Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.

National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.

出版信息

Nucleic Acids Res. 2025 Jul 19;53(14). doi: 10.1093/nar/gkaf735.

Abstract

The nucleosome, as the fundamental unit of chromatin, interacts with a diverse range of proteins, crucially regulating gene expression. In this study, we introduce an AlphaFold-based algorithm designed to analyze nucleosome-binding proteins from a dataset of over 7600 human nuclear proteins. Using proteins that interact with the nucleosome acidic patch as a benchmark, our screening achieves a successful prediction rate of 77% (23 out of 30 proteins). This predictive approach has led to the identification of ARID4A and ARID4B as novel nucleosome-binding proteins. Additionally, this analytical method was used to study RING-family ubiquitin E3 ligase RNF168, demonstrating that RNF168 dimerization enhances its binding to the nucleosome, a finding confirmed by cryogenic-electron microscopy structural analysis. Our findings offer a rapid and effective method for the discovery and characterization of nucleosome-binding proteins and emphasize the significant role of ubiquitin E3 ligase dimerization in epigenetic regulation.

摘要

核小体作为染色质的基本单位,与多种蛋白质相互作用,对基因表达起着关键的调控作用。在本研究中,我们引入了一种基于AlphaFold的算法,用于从一个包含7600多种人类核蛋白的数据集分析核小体结合蛋白。以与核小体酸性补丁相互作用的蛋白质作为基准,我们的筛选实现了77%的成功预测率(30种蛋白质中有23种)。这种预测方法已导致鉴定出ARID4A和ARID4B为新型核小体结合蛋白。此外,该分析方法被用于研究RING家族泛素E3连接酶RNF168,结果表明RNF168二聚化增强了其与核小体的结合,这一发现通过低温电子显微镜结构分析得到了证实。我们的研究结果为核小体结合蛋白的发现和表征提供了一种快速有效的方法,并强调了泛素E3连接酶二聚化在表观遗传调控中的重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78cc/12341937/7d4288f59a2c/gkaf735figgra1.jpg

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