Reference : SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Ana...
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
http://hdl.handle.net/10993/48047
SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles.
English
Rosenberger, George [> >]
Heusel, Moritz [> >]
Bludau, Isabell [> >]
Collins, Ben C. [> >]
Martelli, Claudia [> >]
Williams, Evan mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Gene Expression and Metabolism]
Xue, Peng [> >]
Liu, Yansheng [> >]
Aebersold, Ruedi [> >]
Califano, Andrea [> >]
2020
Cell systems
11
6
589-607.e8
Yes
2405-4712
2405-4720
[en] algorithm ; data-independent acquisition ; differential analysis ; machine learning ; network ; protein complex ; protein correlation profiling ; protein-protein interaction ; proteomics ; size-exclusion chromatography
[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.
http://hdl.handle.net/10993/48047
Copyright © 2020. Published by Elsevier Inc.

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