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质谱驱动的磷酸化蛋白质组学:构建系统生物学拼图

Mass spectrometry-driven phosphoproteomics: patterning the systems biology mosaic.

作者信息

Jünger Martin A, Aebersold Ruedi

机构信息

Department of Biology, Institute of Molecular Systems Biology, Zurich, Switzerland.

出版信息

Wiley Interdiscip Rev Dev Biol. 2014 Jan-Feb;3(1):83-112. doi: 10.1002/wdev.121. Epub 2013 Jul 2.

DOI:10.1002/wdev.121
PMID:24902836
Abstract

Protein phosphorylation is the best-studied posttranslational modification and plays a role in virtually every biological process. Phosphoproteomics is the analysis of protein phosphorylation on a proteome-wide scale, and mainly uses the same instrumentation and analogous strategies as conventional mass spectrometry (MS)-based proteomics. Measurements can be performed either in a discovery-type, also known as shotgun mode, or in a targeted manner which monitors a set of a priori known phosphopeptides, such as members of a signal transduction pathway, across biological samples. Here, we delineate the different experimental levels at which measures can be taken to optimize the scope, reliability, and information content of phosphoproteomic analyses. Various chromatographic and chemical protocols exist to physically enrich phosphopeptides from proteolytic digests of biological samples. Subsequent mass spectrometric analysis revolves around peptide ion fragmentation to generate sequence information and identify the backbone sequence of phosphopeptides as well as the phosphate group attachment site(s), and different modes of fragmentation like collision-induced dissociation (CID), electron transfer dissociation (ETD), and higher energy collisional dissociation (HCD) have been established for phosphopeptide analysis. Computational tools are important for the identification and quantification of phosphopeptides and mapping of phosphorylation sites, the deposition of large-scale phosphoproteome datasets in public databases, and the extraction of biologically meaningful information by data mining, integration with other data types, and descriptive or predictive modeling. Finally, we discuss how orthogonal experimental approaches can be employed to validate newly identified phosphorylation sites on a biochemical, mechanistic, and physiological level.

摘要

蛋白质磷酸化是研究最为深入的翻译后修饰,几乎在每个生物学过程中都发挥作用。磷酸化蛋白质组学是在蛋白质组范围内对蛋白质磷酸化进行分析,主要使用与基于传统质谱(MS)的蛋白质组学相同的仪器和类似策略。测量可以采用发现型(也称为鸟枪法)进行,也可以采用靶向方式,即监测一组先验已知的磷酸肽,如信号转导途径的成员,贯穿生物样品。在这里,我们阐述了可以采取措施来优化磷酸化蛋白质组分析的范围、可靠性和信息含量的不同实验层面。存在各种色谱和化学方案,用于从生物样品的蛋白水解消化物中物理富集磷酸肽。随后的质谱分析围绕肽离子碎裂展开,以生成序列信息并鉴定磷酸肽的主链序列以及磷酸基团连接位点,并且已经建立了不同的碎裂模式,如碰撞诱导解离(CID)、电子转移解离(ETD)和高能碰撞解离(HCD)用于磷酸肽分析。计算工具对于磷酸肽的鉴定和定量、磷酸化位点的定位、大规模磷酸化蛋白质组数据集在公共数据库中的存储以及通过数据挖掘提取生物学上有意义的信息、与其他数据类型整合以及描述性或预测性建模都很重要。最后,我们讨论如何采用正交实验方法在生化、机制和生理层面验证新鉴定的磷酸化位点。

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