Development of a computational framework for the analysis of protein correlation profiling and spatial proteomics...

Area of Science:

• Proteomics
• Bioinformatics
• Systems Biology

Background:

• Traditional interactome studies lack conditional experimental capabilities.
• Co-fractionation/co-migration methods offer sensitive, specific, and cost-effective interactome assessment.
• Existing bioinformatics tools struggle to analyze co-fractionation data, especially with biological replicates.

Purpose of the Study:

• To develop an integrated, freely available bioinformatics solution for analyzing protein correlation profiling (PCP) SILAC data.
• To enable robust analysis of co-fractionation data, incorporating biological replicates for accurate interactome mapping.
• To simplify the process of interactome analysis, making it accessible without specialized bioinformatics knowledge.

Main Methods:

• Developed a modular bioinformatics solution for PCP SILAC data analysis.
• Implemented deconvolution of protein chromatograms into Gaussian curves for replicate alignment.
• Utilized chromatography features to build a consensus map and quantify interaction changes.
• Applied the workflow to a HeLa cell infection model with Salmonella enterica serovar Typhimurium.

Main Results:

• Successfully analyzed PCP SILAC data, integrating biological replicates.
• Generated a consensus map of protein features across replicates.
• Quantified changes in protein interactions, identifying infection-specific alterations.
• Demonstrated the ability to construct an interactome map affected by external perturbations.

Conclusions:

• A set of software tools for co-migration/co-fractionation data analysis has been developed.
• The tools integrate multiple replicates to generate interactomes and assess perturbation impacts.
• This approach simplifies interactome measurement, requiring no specialized knowledge.