Proximity-dependent labeling methods for proteomic profiling in living cells: An update.
Proximity-dependent labeling methods for proteomic profiling in living cells: An update.
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DOI：
10.1002/wdev.392
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发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Perrimon N
中科院分区：
文献类型：
--
作者：
Bosch JA;Chen CL;Perrimon N
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蛋白质。然后可以分离标记的内源性蛋白质用于通过MS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区室的组分，通过将酶融合至靶向特定亚细胞区域的特定蛋白质或信号肽来实现空间表达。虽然这些技术最近才被引入，但它们已经为广泛的生物过程提供了深刻的见解。在这里，我们提供了一个更新的描述和比较接近标记方法，以及它们的应用和改进。由于每种方法都有其独特的功能，本文的目的是描述不同的邻近标记方法可以用来回答不同的生物学问题。
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.
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影响因子：
6.4
作者：
Chen AL;Kim EW;Toh JY;Vashisht AA;Rashoff AQ;Van C;Huang AS;Moon AS;Bell HN;Bentolila LA;Wohlschlegel JA;Bradley PJ
通讯作者：
Bradley PJ
DOI：
10.1126/science.aaf5573
发表时间：
2016-08-05
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Abudayyeh OO;Gootenberg JS;Konermann S;Joung J;Slaymaker IM;Cox DB;Shmakov S;Makarova KS;Semenova E;Minakhin L;Severinov K;Regev A;Lander ES;Koonin EV;Zhang F
通讯作者：
Zhang F
影响因子：
48
作者：
Bar DZ;Atkatsh K;Tavarez U;Erdos MR;Gruenbaum Y;Collins FS
通讯作者：
Collins FS
影响因子：
3.5
作者：
De Munter, Sofie;Gornemann, Janina;Bollen, Mathieu
通讯作者：
Bollen, Mathieu
影响因子：
5.6
作者：
Cronan, JE
通讯作者：
Cronan, JE