Protein Complex Identification and quantitative complexome by CN-PAGE

The majority of cellular processes are carried out by protein complexes. Various size fractionation methods have previously been combined with mass spectrometry to identify protein complexes. However, most of these approaches lack the quantitative information which is required to understand how chan...

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Bibliographic Details
Published in:Scientific reports 2019-08, Vol.9 (1), p.11523-14, Article 11523
Main Authors: Gorka, Michal, Swart, Corné, Siemiatkowska, Beata, Martínez-Jaime, Silvia, Skirycz, Aleksandra, Streb, Sebastian, Graf, Alexander
Format: Article
Language:English
Subjects:
631/449
631/45/475/2290
631/45/612/1248
82/29
82/47
Arabidopsis - genetics
Arabidopsis - growth & development
Arabidopsis Proteins - classification
Arabidopsis Proteins - genetics
Electrophoresis, Polyacrylamide Gel
Fractionation
Gene Expression Regulation, Plant - genetics
Humanities and Social Sciences
Mass Spectrometry
Mass spectroscopy
Mitochondria - genetics
Molecular weight
multidisciplinary
Multiprotein Complexes - genetics
Multiprotein Complexes - isolation & purification
Native Polyacrylamide Gel Electrophoresis - methods
Protein composition
Proteins
Proteomics
Science
Science (multidisciplinary)
Scientific imaging
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Summary:The majority of cellular processes are carried out by protein complexes. Various size fractionation methods have previously been combined with mass spectrometry to identify protein complexes. However, most of these approaches lack the quantitative information which is required to understand how changes of protein complex abundance and composition affect metabolic fluxes. In this paper we present a proof of concept approach to quantitatively study the complexome in the model plant Arabidopsis thaliana at the end of the day (ED) and the end of the night (EN). We show that size-fractionation of native protein complexes by Clear-Native-PAGE (CN-PAGE), coupled with mass spectrometry can be used to establish abundance profiles along the molecular weight gradient. Furthermore, by deconvoluting complex protein abundance profiles, we were able to drastically improve the clustering of protein profiles. To identify putative interaction partners, and ultimately protein complexes, our approach calculates the Euclidian distance between protein profile pairs. Acceptable threshold values are based on a cut-off that is optimized by a receiver-operator characteristic (ROC) curve analysis. Our approach shows low technical variation and can easily be adapted to study in the complexome in any biological system.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-47829-7