Network-centric analysis of co-fractionated protein complex profiles using SECAT

Area of Science:

• Proteomics
• Systems Biology
• Bioinformatics

Background:

• Understanding protein complex dynamics is crucial for elucidating cellular functions.
• Co-fractionated bottom-up mass spectrometry (CF-MS) generates complex datasets for protein interaction analysis.
• Existing tools require enhancement for comprehensive network-centric interpretation of CF-MS data.

Purpose of the Study:

• To present a detailed protocol for network-centric analysis and interpretation of CF-MS data using the Size-Exclusion Chromatography Analysis Toolkit (SECAT).
• To guide researchers in utilizing SECAT for discovering dysregulated proteins and interactions.
• To support the generation of new hypotheses and biological insights from CF-MS experiments.

Main Methods:

• Network-centric analysis of CF-MS profiles using the SECAT software.
• Detailed protocol covering data preprocessing, scoring, semi-supervised machine learning, and quantification.
• Guidance on data export, visualization, and interpretation of SECAT results.

Main Results:

• Established a comprehensive protocol for CF-MS data analysis with SECAT.
• Addressed common pitfalls and provided solutions for robust data interpretation.
• Demonstrated the utility of SECAT in identifying dysregulated proteins and protein interactions.

Conclusions:

• SECAT provides a powerful framework for dissecting protein complex dynamics from CF-MS data.
• The presented protocol facilitates the discovery of novel biological insights and potential therapeutic targets.
• SECAT empowers researchers to advance systems biology studies through advanced proteomic data interpretation.