A Label-free Mass Spectrometry Method to Predict Endogenous Protein Complex Composition
A Label-free Mass Spectrometry Method to Predict Endogenous Protein Complex Composition
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预测内源蛋白质复合物组成的无标记质谱方法
DOI：
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发表时间：
2019
影响因子：
7
通讯作者：
Daniel B. Szymanski
中科院分区：
文献类型：
--
作者：
Zachary McBride;Donglai Chen;Youngwoo Lee;U. Aryal;Jun Xie;Daniel B. Szymanski
预计至少三分之一的可溶性蛋白质以稳定的寡聚状态存在。然而，绝大多数的成分是未知的。本文描述了一种基于正交色谱分离和无标记蛋白质相关分析的生物化学方法来预测蛋白质复合物的组成。经过验证的方法预测了数百种新的同源和异源寡聚复合物，并提供了一种新的方法来分析任何生物体中的蛋白质复合物与注释良好的蛋白质组。利用正交蛋白质分离、蛋白质相关性分析和新型数据过滤脚本预测内源性蛋白质复合物的组成。经验证的方法可准确鉴别同源和异源寡聚复合物。对AIMP 1突变体的分析验证了t-RNA合成酶-聚集复合物的发现。蛋白质复合物组成的信息可以加速细胞系统的机理分析。蛋白质复合物的组成确定基因一起发挥作用，并提供有关细胞内和之间的调节途径的线索。胞质蛋白质复合物控制代谢通量、信号转导、蛋白质丰度以及细胞骨架和内膜系统的活动。据估计，三分之一的细胞溶质蛋白质存在于一个寡聚体状态，但几乎所有的组成仍然未知。稳定蛋白质复合物的亚基共纯化，基于质谱的蛋白质相关性分析和生物信息学分析的组合已被用于预测蛋白质复合物亚基。由于生物信息学数据为不同物种的蛋白质复合物预测提供信息的能力或可用性存在不确定性，因此仅基于洗脱谱数据预测组成将非常有利。在这里，我们描述了一种基于质谱的蛋白质相关性分析方法来预测数百种蛋白质复合物的组成的基础上的生化数据。从完整器官中获得浸提液，并在非变性条件下按大小和电荷平行分离。对所有重复中具有可再现洗脱曲线的超过1000种蛋白质进行聚类分析。由此产生的树状图被用来预测已知和新型蛋白质复合物的组成，包括许多可能通过自我相互作用组装的蛋白质复合物。一系列验证实验表明，这种新方法可以推动蛋白质复合物的发现，指导假设检验，并使任何具有测序基因组的生物体中蛋白质复合物动态的系统级分析成为可能。
At least one third of soluble proteins are predicted to exist in a stable oligomeric state. However, the compositions of the vast majority are unknown. This paper describes a biochemical method to predict protein complex composition based on orthogonal chromatographic separations and label-free protein correlation profiling. The validated method predicts hundreds of novel homo- and heterooligomeric complexes, and provides a new way to analyze protein complexes in any organism with a well-annotated proteome. Graphical Abstract Highlights Endogenous protein complex composition was predicted using orthogonal protein separations, protein correlation profiling, and novel data filtering scripts. The validated method accurately identifies homo- and heterooligomeric complexes. Profiling of the AIMP1 mutant validated the discovery of a t-RNA synthetase-clustering complex. Information on the composition of protein complexes can accelerate mechanistic analyses of cellular systems. Protein complex composition identifies genes that function together and provides clues about regulation within and between cellular pathways. Cytosolic protein complexes control metabolic flux, signal transduction, protein abundance, and the activities of cytoskeletal and endomembrane systems. It has been estimated that one third of all cytosolic proteins in leaves exist in an oligomeric state, yet the composition of nearly all remain unknown. Subunits of stable protein complexes copurify, and combinations of mass-spectrometry-based protein correlation profiling and bioinformatic analyses have been used to predict protein complex subunits. Because of uncertainty regarding the power or availability of bioinformatic data to inform protein complex predictions across diverse species, it would be highly advantageous to predict composition based on elution profile data alone. Here we describe a mass spectrometry-based protein correlation profiling approach to predict the composition of hundreds of protein complexes based on biochemical data. Extracts were obtained from an intact organ and separated in parallel by size and charge under nondenaturing conditions. More than 1000 proteins with reproducible elution profiles across all replicates were subjected to clustering analyses. The resulting dendrograms were used to predict the composition of known and novel protein complexes, including many that are likely to assemble through self-interaction. An array of validation experiments demonstrated that this new method can drive protein complex discovery, guide hypothesis testing, and enable systems-level analyses of protein complex dynamics in any organism with a sequenced genome.
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影响因子：
--
作者：
Oughtred R;Chatr-aryamontri A;Breitkreutz BJ;Chang CS;Rust JM;Theesfeld CL;Heinicke S;Breitkreutz A;Chen D;Hirschman J;Kolas N;Livstone MS;Nixon J;O'Donnell L;Ramage L;Winter A;Reguly T;Sellam A;Stark C;Boucher L;Dolinski K;Tyers M
通讯作者：
Tyers M
影响因子：
4.1
作者：
Liu, Xiuping;Yang, Wen-chu;Regnier, Fred
通讯作者：
Regnier, Fred
影响因子：
2.9
作者：
Katebi AR;Jernigan RL
通讯作者：
Jernigan RL
影响因子：
2.9
作者：
Chang, L;Loranger, SS;Annunziato, AT
通讯作者：
Annunziato, AT
影响因子：
16
作者：
Ofir-Birin, Yifat;Fang, Pengfei;Bennett, Steven P.;Zhang, Hui-Min;Wang, Jing;Rachmin, Inbal;Shapiro, Ryan;Song, Jing;Dagan, Arie;Pozo, Jorge;Kim, Sunghoon;Marshall, Alan G.;Schimmel, Paul;Yang, Xiang-Lei;Nechushtan, Hovav;Razin, Ehud;Guo, Min
通讯作者：
Guo, Min