Peptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation.

Area of Science:

• Proteomics
• Biochemistry
• Bioinformatics

Background:

• Shotgun proteomics quantifies proteins using peptide measurements.
• Assumptions of uniform peptide behavior can be violated by proteoforms and technical artifacts.
• Accurate protein quantification is crucial for biological discovery.

Purpose of the Study:

• To develop a computational strategy, peptide correlation analysis (PeCorA), to identify quantitative disagreements among peptides from the same protein.
• To assess the utility of PeCorA in detecting regulated post-translational modifications (PTMs) and poorly quantified peptides.
• To improve the accuracy of protein quantification by excluding unreliable peptides.

Main Methods:

• PeCorA employs linear models to compare the quantitative behavior of individual peptides against others from the same protein across treatment groups.
• The strategy was applied to a mouse microglia stress dataset and a benchmark dataset.
• Analysis focused on identifying discordant peptides and their implications for protein quantification and biological interpretation.

Main Results:

• Approximately 15% of proteins in the mouse microglia dataset exhibited discordant peptides.
• PeCorA effectively detected regulated PTMs and identified poorly quantified peptides.
• Excluding poorly quantified peptides reduced false-positives in a benchmark dataset.
• Analysis suggested increased inactive prothrombin in COVID-19 patient plasma.

Conclusions:

• PeCorA is a valuable tool for identifying quantitative inconsistencies in shotgun proteomics data.
• The method enhances the detection of PTMs and improves protein quantification reliability.
• PeCorA has potential applications in biomarker discovery, exemplified by prothrombin in COVID-19.