Suppr超能文献

鉴定蛋白赖氨酸甲基转移酶新型底物的方法和指南。

Approaches and Guidelines for the Identification of Novel Substrates of Protein Lysine Methyltransferases.

机构信息

Faculty of Chemistry, Institute of Biochemistry, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany.

Faculty of Chemistry, Institute of Biochemistry, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany.

出版信息

Cell Chem Biol. 2016 Sep 22;23(9):1049-1055. doi: 10.1016/j.chembiol.2016.07.013. Epub 2016 Aug 25.

Abstract

Protein lysine methylation is emerging as a general post-translational modification (PTM) with essential functions regulating protein stability, activity, and protein-protein interactions. One of the outstanding challenges in this field is linking protein lysine methyltransferases (PKMTs) with specific substrates and lysine methylation events in a systematic manner. Inability to validate reported PKMT substrates delayed progress in the field and cast unnecessary doubt about protein lysine methylation as a truly general PTM. Here, we aim to provide a concise guide to help avoid some of the most common pitfalls in studies searching for new PKMT substrates and propose a set of seven basic biochemical rules: (1) include positive controls; (2) use target lysine mutations of substrate proteins as negative controls; (3) use inactive enzyme variants as negative controls; (4) report quantitative methylation data; (5) consider PKMT specificity; (6) validate methyl lysine antibodies; and (7) connect cellular and in vitro results. We explain the logic behind them and discuss how they should be implemented in the experimental work.

摘要

蛋白质赖氨酸甲基化是一种新兴的普遍翻译后修饰(PTM),对调节蛋白质稳定性、活性和蛋白质-蛋白质相互作用具有重要功能。该领域的一个突出挑战是系统地将蛋白赖氨酸甲基转移酶(PKMTs)与特定的底物和赖氨酸甲基化事件联系起来。无法验证报道的 PKMT 底物延迟了该领域的进展,并对蛋白质赖氨酸甲基化作为一种真正普遍的 PTM 产生了不必要的怀疑。在这里,我们旨在提供一个简洁的指南,以帮助避免在寻找新的 PKMT 底物的研究中出现一些最常见的陷阱,并提出了一套七个基本的生化规则:(1)包含阳性对照;(2)使用底物蛋白的靶赖氨酸突变作为阴性对照;(3)使用无活性酶变体作为阴性对照;(4)报告定量甲基化数据;(5)考虑 PKMT 特异性;(6)验证甲基化赖氨酸抗体;(7)连接细胞和体外结果。我们解释了它们背后的逻辑,并讨论了如何在实验工作中实施这些规则。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验