SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles.
algorithm
data-independent acquisition
differential analysis
machine learning
network
protein complex
protein correlation profiling
protein-protein interaction
proteomics
size-exclusion chromatography
Journal
Cell systems
ISSN: 2405-4720
Titre abrégé: Cell Syst
Pays: United States
ID NLM: 101656080
Informations de publication
Date de publication:
16 12 2020
16 12 2020
Historique:
received:
30
04
2020
revised:
25
08
2020
accepted:
17
11
2020
entrez:
17
12
2020
pubmed:
18
12
2020
medline:
29
9
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33333029
pii: S2405-4712(20)30457-9
doi: 10.1016/j.cels.2020.11.006
pmc: PMC8034988
mid: NIHMS1684599
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
589-607.e8Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM137031
Pays : United States
Organisme : NIH HHS
ID : S10 OD012351
Pays : United States
Organisme : NIH HHS
ID : S10 OD021764
Pays : United States
Organisme : NCI NIH HHS
ID : U54 CA209997
Pays : United States
Informations de copyright
Copyright © 2020. Published by Elsevier Inc.
Déclaration de conflit d'intérêts
Declaration of Interests A.C. is founder, equity holder, and consultant of DarwinHealth Inc, a company that has licensed some of the algorithms used in this manuscript from Columbia University. Columbia University is also an equity holder in DarwinHealth Inc and assignee of patent US10,790,040, which covers some components of the algorithms used in this manuscript. The other authors declare no competing interests.
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