Complex‐centric proteome profiling by SEC ‐ SWATH ‐ MS

Abstract

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework CCprofiler to detect and quantify protein complexes by error‐controlled, complex‐centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub‐complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.

Dosage compensation plans: protein aggregation provides additional insurance against aneuploidy

“…Ribosomes (the major components aggregating in the present study) are especially sensitive to this, as shown by a recent study in turquoise killifish, where ribosomal subunits were enriched in aggregates from aged brains (Sacramento et al 2019). Recent advances in global protein complex profiling techniques (e.g., size exclusion chromatography [SEC]-sequential window acquisition of all theoretical mass spectra [SWATH]-MS) (Heusel et al 2019) will undoubtedly prove fruitful for characterizing protein stoichiometry changes under such conditions. In a similar vein, centrifugation or gel filtration-based proteomics (Geladaki et al 2019) could be used to address whether proteins resident in certain subcellular compartments are especially prone to aggregation.…”

Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study

“…There are many excellent bioinformatics methods developed for proteomics data, including the techniques for normalization and preprocessing (e.g. spectral counts modeling with edgeR [70], MetaMass [71], reviewed in [72]), detecting and quantifying protein complexes (CCprofiler [73]), protein-protein interaction networks analysis and visualization (Cytoscape [74]) as well as dedicated software platforms with a set of statistical tools for high-dimensional proteomics data analysis (e.g. Perseus [75]).…”

Emerging mass spectrometry-based proteomics methodologies for novel biomedical applications

“…A third method uses a semi-supervised support vector machine classification to determine protein complexes, taking as input co-fractionation data and additional information, such as published PPI datasets, co-evolution based on the assumption that true protein complexes will have conserved sequence and function, and other metrics that describe protein complexes [103]. A third example, CCprofiler [104], introduces a novel 'complex centric' analysis, which is the first to control error rates for protein complex analysis by using a statistical target-decoy method and also makes use of SWATH acquisition (discussed above) to improve the quantitative accuracy of SEC-MS.…”