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Protein Complex Identification and quantitative complexome by CN-PAGE

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Gorka,  M.
Small-Molecule Signalling, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Swart,  C.
Plant Proteomics, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Siemiatkowska,  B.
Plant Proteomics, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Martínez-Jaime,  S.
Plant Proteomics, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Skirycz,  A.
Small-Molecule Signalling, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Graf,  A.
Plant Proteomics, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Citation

Gorka, M., Swart, C., Siemiatkowska, B., Martínez-Jaime, S., Skirycz, A., Streb, S., et al. (2019). Protein Complex Identification and quantitative complexome by CN-PAGE. Scientific Reports, 9(1): 11523. doi:10.1038/s41598-019-47829-7.


Cite as: http://hdl.handle.net/21.11116/0000-0004-706B-6
Abstract
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.