ProtyQuant: Comparing label-free shotgun proteomics datasets using accumulated peptide probabilities.

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Comparing label-free shotgun proteomics datasets involves complex data processing, including peptide-spectrum matching, protein inference, and quantification.
• Existing workflows can be overwhelming for non-expert researchers, hindering the integration and comparison of results from multiple experiments.

Purpose of the Study:

• To develop and evaluate ProtyQuant, a software tool designed to streamline the analysis and comparison of label-free shotgun proteomics data.
• To improve protein identification and quantification accuracy in comparative proteomics studies.

Main Methods:

• ProtyQuant integrates protein inference and quantification using a modified PIPQ program, summing peptide probabilities for 'accumulated peptide probabilities' (app).
• It supports three peptide-to-protein assignment algorithms: Multiple Counting, Equal Division, and Linear Programming.
• The software processes validated pepXML files and is compatible with various search engines, offering both a GUI and console-based processing.

Main Results:

• ProtyQuant detected up to 15% more proteins than ProteinProphet at an equal false positive rate when tested on a reference dataset.
• The 'accumulated peptide probabilities' (app) values demonstrated suitable sensitivity and linearity for label-free quantification.
• App values provide a realistic measure of 'Protein Presence,' integrating protein probability and quantity.

Conclusions:

• ProtyQuant effectively aids in comparing proteins across multiple shotgun proteomics samples, simplifying data integration for researchers.
• The 'accumulated peptide probability' (app) serves as a reliable, holistic measure for both protein identification and quantification.
• The software facilitates the compilation of reports for comparative proteomics by using a single, integrated measure.