ComplexBrowser: A Tool for Identification and Quantification of Protein Complexes in Large-scale Proteomics Datasets.

Area of Science:

• Proteomics
• Systems Biology
• Bioinformatics

Background:

• Quantitative proteomic data analysis often focuses on individual proteins, overlooking protein complex dynamics.
• Understanding protein complex regulation is crucial for deciphering cellular functions and disease mechanisms.

Purpose of the Study:

• To develop and validate ComplexBrowser, an open-source platform for supervised analysis of quantitative proteomic data focused on protein complexes.
• To introduce a novel metric, Complex Fold Change (CFC), for identifying coordinated regulation of protein complex subunits.

Main Methods:

• ComplexBrowser utilizes curated data from CORUM and Complex Portal databases for protein complex identification.
• The platform incorporates normalization, statistical analysis (limma package), and interactive visualization for exploratory data analysis.
• Quantitative proteomic data (label-free and isobaric mass tag based) is analyzed to assess protein complex expression and regulation.

Main Results:

• ComplexBrowser was applied to human adenocarcinoma and mouse T-cell activation studies.
• Analysis identified 1519 and 332 protein complexes, with 233 and 41 found to be coordinately regulated, respectively.
• The study revealed a metabolic shift in adenocarcinoma and identified chromatin remodeling complexes in activated T-cells.

Conclusions:

• ComplexBrowser automates quantitative protein complex analysis, offering novel biological insights into complex regulation.
• The platform provides rapid analysis of high-throughput proteomic studies, facilitating the discovery of affected protein complexes.
• The findings highlight the utility of ComplexBrowser in uncovering complex-level changes related to disease and cellular processes.