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Proximity-dependent labeling methods for proteomic profiling in living cells: An update.
WIREs Mechanisms of Disease ( IF 4.6 ) Pub Date : 2020-09-10 , DOI: 10.1002/wdev.392
Justin A Bosch 1 , Chiao-Lin Chen 1 , Norbert Perrimon 1, 2
Affiliation  

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.

中文翻译:

活细胞蛋白质组学分析的邻近依赖性标记方法:更新。

表征活细胞的细胞器和亚细胞区域的蛋白质组组成可以促进对细胞组织以及蛋白质相互作用组网络的理解。基于邻近标记的方法与质谱 (MS) 相结合,为空间受限蛋白质组的系统分析提供了一种高通量方法。接近标记利用产生反应性自由基的酶来共价标记相邻的蛋白质。然后可以分离标记的内源性蛋白质以供 MS 进一步分析。为了分析蛋白质-蛋白质相互作用或识别定位于离散亚细胞区室的成分,通过将酶与靶向特定亚细胞区域的特定蛋白质或信号肽融合来实现空间表达。虽然这些技术是最近才引入的,他们已经对广泛的生物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更新描述和比较,以及它们的应用和改进。由于每种方法都有其独特的特点,本综述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。
更新日期:2020-09-10
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