Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark.
KTH - Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology, Solna, Sweden.
BMC Bioinformatics. 2019 Jan 9;20(1):17. doi: 10.1186/s12859-018-2573-8.
Translational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. Better understanding and prediction of these functional states demands methods for the characterization of complex composition, behavior, and abundance across multiple cell states. Mass spectrometry provides an unbiased approach to directly determine protein abundances across different cell populations and thus to profile a comprehensive abundance map of proteins.
We provide a tool to investigate the behavior of protein subunits in known complexes by comparing their abundance profiles across up to 140 cell types available in ProteomicsDB. Thorough assessment of different randomization methods and statistical scoring algorithms allows determining the significance of concurrent profiles within a complex, therefore providing insights into the conservation of their composition across human cell types as well as the identification of intrinsic structures in complex behavior to determine which proteins orchestrate complex function. This analysis can be extended to investigate common profiles within arbitrary protein groups. CoExpresso can be accessed through http://computproteomics.bmb.sdu.dk/Apps/CoExpresso .
With the CoExpresso web service, we offer a potent scoring scheme to assess proteins for their co-regulation and thereby offer insight into their potential for forming functional groups like protein complexes.
细胞中的翻译和翻译后调控机制导致测量的基因转录和蛋白质丰度之间存在广泛的可观察差异。在此,蛋白质复合物是通过其单个蛋白质的选择性降解而受到最严格控制的实体之一。它们还作为控制枢纽,调节细胞中非常重要的过程,并由于其改变组成和结构的能力而表现出高度的功能多样性。更好地理解和预测这些功能状态需要用于表征多个细胞状态下的复杂组成、行为和丰度的方法。质谱法提供了一种直接在不同细胞群体中确定蛋白质丰度的无偏方法,从而可以对蛋白质进行全面的丰度图谱分析。
我们提供了一种通过在 ProteomicsDB 中可用的多达 140 种细胞类型中比较其丰度谱来研究已知复合物中蛋白质亚基行为的工具。对不同随机化方法和统计评分算法的彻底评估允许确定复合物内并发谱的显著性,从而深入了解其在人类细胞类型中的组成保守性,以及确定哪些蛋白质协调复合物功能的固有结构。这种分析可以扩展到研究任意蛋白质组中的常见谱。CoExpresso 可通过 http://computproteomics.bmb.sdu.dk/Apps/CoExpresso 访问。
通过 CoExpresso 网络服务,我们提供了一种强大的评分方案,用于评估蛋白质的共调控性,从而深入了解它们形成功能组(如蛋白质复合物)的潜力。