Suppr超能文献

活细胞蛋白质组学分析中的邻近依赖性标记方法:最新进展。

Proximity-dependent labeling methods for proteomic profiling in living cells: An update.

机构信息

Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, USA.

Howard Hughes Medical Institute, Boston, Massachusetts, USA.

出版信息

Wiley Interdiscip Rev Dev Biol. 2021 Jan;10(1):e392. doi: 10.1002/wdev.392. Epub 2020 Sep 10.

Abstract

Characterizing the proteome composition of organelles and subcellular regions of living cells can facilitate the understanding of cellular organization as well as protein interactome networks. Proximity labeling-based methods coupled with mass spectrometry (MS) offer a high-throughput approach for systematic analysis of spatially restricted proteomes. Proximity labeling utilizes enzymes that generate reactive radicals to covalently tag neighboring proteins. The tagged endogenous proteins can then be isolated for further analysis by MS. To analyze protein-protein interactions or identify components that localize to discrete subcellular compartments, spatial expression is achieved by fusing the enzyme to specific proteins or signal peptides that target to particular subcellular regions. Although these technologies have only been introduced recently, they have already provided deep insights into a wide range of biological processes. Here, we provide an updated description and comparison of proximity labeling methods, as well as their applications and improvements. As each method has its own unique features, the goal of this review is to describe how different proximity labeling methods can be used to answer different biological questions. This article is categorized under: Technologies > Analysis of Proteins.

摘要

对活细胞细胞器和亚细胞区域的蛋白质组组成进行特征分析,可以促进对细胞组织以及蛋白质互作网络的理解。基于邻近标记与质谱(MS)联用的方法为系统分析空间受限的蛋白质组提供了一种高通量方法。邻近标记利用产生反应性自由基的酶来共价标记邻近的蛋白质。然后可以通过 MS 对标记的内源性蛋白质进行进一步分析。为了分析蛋白质-蛋白质相互作用或鉴定定位于离散亚细胞隔室的成分,可以通过将酶融合到特定蛋白质或靶向特定亚细胞区域的信号肽来实现空间表达。尽管这些技术最近才被引入,但它们已经为广泛的生物学过程提供了深刻的见解。在这里,我们提供了邻近标记方法的最新描述和比较,以及它们的应用和改进。由于每种方法都有其独特的特点,因此本文的目的是描述如何使用不同的邻近标记方法来回答不同的生物学问题。本文属于以下类别:技术 > 蛋白质分析

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3b6/8142282/117794faf917/nihms-1701002-f0001.jpg

相似文献

3
Filling the Void: Proximity-Based Labeling of Proteins in Living Cells.填补空白:活细胞中基于邻近性的蛋白质标记
Trends Cell Biol. 2016 Nov;26(11):804-817. doi: 10.1016/j.tcb.2016.09.004. Epub 2016 Sep 22.
9
Protein Neighbors and Proximity Proteomics.蛋白质邻域与邻近蛋白质组学
Mol Cell Proteomics. 2015 Nov;14(11):2848-56. doi: 10.1074/mcp.R115.052902. Epub 2015 Sep 8.

引用本文的文献

10
Progress toward a comprehensive brain protein interactome.迈向全面脑蛋白相互作用组的进展。
Biochem Soc Trans. 2025 Feb 12;53(1):BST20241135. doi: 10.1042/BST20241135.

本文引用的文献

1
Split-TurboID enables contact-dependent proximity labeling in cells.Split-TurboID 可实现细胞内依赖接触的邻近标记。
Proc Natl Acad Sci U S A. 2020 Jun 2;117(22):12143-12154. doi: 10.1073/pnas.1919528117. Epub 2020 May 18.
7
Capturing RNA-protein interaction via CRUIS.通过 CRUIS 捕获 RNA-蛋白质相互作用。
Nucleic Acids Res. 2020 May 21;48(9):e52. doi: 10.1093/nar/gkaa143.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验