algorithm; data-independent acquisition; differential analysis; machine learning; network; protein complex; protein correlation profiling; protein-protein interaction; proteomics; size-exclusion chromatography
Abstract :
[en] Protein-protein interactions (PPIs) play critical functional and regulatory roles in cellular processes. They are essential for macromolecular complex formation, which in turn constitutes the basis for protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we extend co-fractionation and mass spectrometric data analysis to perform quantitative, network-based studies of proteome organization, via the size-exclusion chromatography algorithmic toolkit (SECAT). This framework explicitly accounts for the dynamic nature and rewiring of protein complexes across multiple cell states and samples, thus, elucidating molecular mechanisms that are differentially implemented across different experimental settings. Systematic analysis of multiple datasets shows that SECAT represents a highly scalable and effective methodology to assess condition/state-specific protein-network state. A record of this paper's transparent peer review process is included in the Supplemental Information.
Disciplines :
Biochemistry, biophysics & molecular biology
Author, co-author :
Rosenberger, George
Heusel, Moritz
Bludau, Isabell
Collins, Ben C.
Martelli, Claudia
WILLIAMS, Evan ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Gene Expression and Metabolism
Xue, Peng
Liu, Yansheng
Aebersold, Ruedi
Califano, Andrea
External co-authors :
yes
Language :
English
Title :
SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles.
Publication date :
2020
Journal title :
Cell Systems
ISSN :
2405-4712
eISSN :
2405-4720
Publisher :
Elsevier, Riverport Lane, United States - Maryland