School of Basic Medical Sciences, Xianning Medical College, Hubei University of Science and Technology, Xianning, Hubei, 437000, China.
School of Pharmacy, Xianning Medical College, Hubei University of Science and Technology, Xianning, Hubei, 437000, China.
Cell Commun Signal. 2023 Sep 30;21(1):269. doi: 10.1186/s12964-023-01310-1.
Protein‒protein, protein‒RNA, and protein‒DNA interaction networks form the basis of cellular regulation and signal transduction, making it crucial to explore these interaction networks to understand complex biological processes. Traditional methods such as affinity purification and yeast two-hybrid assays have been shown to have limitations, as they can only isolate high-affinity molecular interactions under nonphysiological conditions or in vitro. Moreover, these methods have shortcomings for organelle isolation and protein subcellular localization. To address these issues, proximity labeling techniques have been developed. This technology not only overcomes the limitations of traditional methods but also offers unique advantages in studying protein spatial characteristics and molecular interactions within living cells. Currently, this technique not only is indispensable in research on mammalian nucleoprotein interactions but also provides a reliable approach for studying nonmammalian cells, such as plants, parasites and viruses. Given these advantages, this article provides a detailed introduction to the principles of proximity labeling techniques and the development of labeling enzymes. The focus is on summarizing the recent applications of TurboID and miniTurbo in mammals, plants, and microorganisms. Video Abstract.
蛋白质-蛋白质、蛋白质-RNA 和蛋白质-DNA 相互作用网络构成了细胞调节和信号转导的基础,因此探索这些相互作用网络对于理解复杂的生物过程至关重要。传统的方法,如亲和纯化和酵母双杂交实验,已经被证明存在局限性,因为它们只能在非生理条件下或体外分离高亲和力的分子相互作用。此外,这些方法在细胞器分离和蛋白质亚细胞定位方面存在缺陷。为了解决这些问题,已经开发了邻近标记技术。这项技术不仅克服了传统方法的局限性,而且在研究活细胞内蛋白质的空间特征和分子相互作用方面具有独特的优势。目前,这项技术不仅是研究哺乳动物核蛋白相互作用不可或缺的手段,也是研究非哺乳动物细胞(如植物、寄生虫和病毒)的可靠方法。鉴于这些优势,本文详细介绍了邻近标记技术的原理和标记酶的发展。重点总结了 TurboID 和 miniTurbo 在哺乳动物、植物和微生物中的最新应用。视频摘要。