Establishment of Proximity-Dependent Biotinylation Approaches in Different Plant Model Systems

在不同植物模型系统中建立邻近依赖型生物素化方法

阅读:6
作者:Deepanksha Arora, Nikolaj B Abel, Chen Liu, Petra Van Damme, Klaas Yperman, Dominique Eeckhout, Lam Dai Vu, Jie Wang, Anna Tornkvist, Francis Impens, Barbara Korbei, Jelle Van Leene, Alain Goossens, Geert De Jaeger, Thomas Ott, Panagiotis Nikolaou Moschou, Daniël Van Damme

Abstract

Proximity labeling is a powerful approach for detecting protein-protein interactions. Most proximity labeling techniques use a promiscuous biotin ligase or a peroxidase fused to a protein of interest, enabling the covalent biotin labeling of proteins and subsequent capture and identification of interacting and neighboring proteins without the need for the protein complex to remain intact. To date, only a few studies have reported on the use of proximity labeling in plants. Here, we present the results of a systematic study applying a variety of biotin-based proximity labeling approaches in several plant systems using various conditions and bait proteins. We show that TurboID is the most promiscuous variant in several plant model systems and establish protocols that combine mass spectrometry-based analysis with harsh extraction and washing conditions. We demonstrate the applicability of TurboID in capturing membrane-associated protein interactomes using Lotus japonicus symbiotically active receptor kinases as a test case. We further benchmark the efficiency of various promiscuous biotin ligases in comparison with one-step affinity purification approaches. We identified both known and novel interactors of the endocytic TPLATE complex. We furthermore present a straightforward strategy to identify both nonbiotinylated and biotinylated peptides in a single experimental setup. Finally, we provide initial evidence that our approach has the potential to suggest structural information of protein complexes.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。