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对SUMO结合蛋白的全球分析确定SUMO化是INO80染色质重塑复合体的关键调节因子。

Global Analysis of SUMO-Binding Proteins Identifies SUMOylation as a Key Regulator of the INO80 Chromatin Remodeling Complex.

作者信息

Cox Eric, Hwang Woochang, Uzoma Ijeoma, Hu Jianfei, Guzzo Catherine M, Jeong Junseop, Matunis Michael J, Qian Jiang, Zhu Heng, Blackshaw Seth

机构信息

From the ‡Biochemistry, Cellular and Molecular Biology Graduate Program.

§Solomon H. Snyder Department of Neuroscience.

出版信息

Mol Cell Proteomics. 2017 May;16(5):812-823. doi: 10.1074/mcp.M116.063719. Epub 2017 Mar 2.

Abstract

SUMOylation is a critical regulator of a broad range of cellular processes, and is thought to do so in part by modulation of protein interaction. To comprehensively identify human proteins whose interaction is modulated by SUMOylation, we developed an binding assay using human proteome microarrays to identify targets of SUMO1 and SUMO2. We then integrated these results with protein SUMOylation and protein-protein interaction data to perform network motif analysis. We focused on a single network motif we termed a SUMOmodPPI (SUMO-modulated Protein-Protein Interaction) that included the INO80 chromatin remodeling complex subunits TFPT and INO80E. We validated the SUMO-binding activity of INO80E, and showed that TFPT is a SUMO substrate both and We then demonstrated a key role for SUMOylation in mediating the interaction between these two proteins, both and By demonstrating a key role for SUMOylation in regulating the INO80 chromatin remodeling complex, this work illustrates the power of bioinformatics analysis of large data sets in predicting novel biological phenomena.

摘要

小泛素样修饰(SUMOylation)是多种细胞过程的关键调节因子,并且被认为部分是通过调节蛋白质相互作用来实现的。为了全面鉴定其相互作用受SUMOylation调节的人类蛋白质,我们开发了一种结合测定法,利用人类蛋白质组芯片来鉴定SUMO1和SUMO2的靶标。然后,我们将这些结果与蛋白质SUMOylation和蛋白质-蛋白质相互作用数据相结合,以进行网络基序分析。我们专注于一个单一的网络基序,我们将其称为SUMOmodPPI(SUMO调节的蛋白质-蛋白质相互作用),它包括INO80染色质重塑复合体亚基TFPT和INO80E。我们验证了INO80E的SUMO结合活性,并表明TFPT在体内和体外都是SUMO底物。然后,我们证明了SUMOylation在介导这两种蛋白质在体内和体外的相互作用中起关键作用。通过证明SUMOylation在调节INO80染色质重塑复合体中的关键作用,这项工作说明了对大数据集进行生物信息学分析在预测新的生物学现象方面的强大作用。

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