CoExpresso: Assess the quantitative behavior of protein complexes in human cells

Morteza H. Chalabi, Vasileios Tsiamis, Lukas Käll, Fabio Vandin, Veit Schwämmle*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Background: 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. Results: 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. Conclusions: 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.

Original languageEnglish
Article number17
JournalBMC Bioinformatics
Volume20
Issue number1
Pages (from-to)1-10
ISSN1471-2105
DOIs
Publication statusPublished - 9. Jan 2019

Keywords

  • Co-regulation
  • Protein complex
  • Statistics
  • Proteins/metabolism
  • Algorithms
  • Humans
  • Proteomics/methods

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