Relative quantification of proteins and post-translational modifications in proteomic experiments with shared...

Area of Science:

• Proteomics
• Computational Biology
• Biostatistics

Background:

• Bottom-up mass spectrometry proteomics quantifies protein changes using peptides.
• Inferring protein-level changes from peptide data is challenging, particularly with shared peptides.
• Existing methods often rely on unique peptides, limiting analysis of complex proteomes.

Purpose of the Study:

• To develop a statistical approach for estimating protein abundances and post-translational modification site occupancies using shared peptide quantitative information.
• To improve the precision of quantitative proteomics analyses, especially in complex biological samples.

Main Methods:

• A novel statistical method treating shared peptide patterns as convex combinations of source abundances.
• Estimation of individual protein abundances and modification site occupancies alongside combination weights.
• Implementation in an open-source R package, MSstatsWeightedSummary.

Main Results:

• The proposed approach enhances the precision of estimated fold changes between conditions.
• Demonstrated utility in diverse applications including protein degradation, thermal proteome stability, and PTM analysis.
• Accurate quantification of protein abundances and PTM site occupancies from shared peptides.

Conclusions:

• The method effectively leverages shared peptide data for more accurate proteomic quantification.
• Provides a robust statistical framework for analyzing complex proteomic datasets.
• Facilitates deeper insights into biological processes through improved protein and PTM quantification.