Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements

Area of Science:

• Proteomics and Bioinformatics
• Computational Biology
• Biomarker Discovery

Background:

• Mass spectrometry-based proteomics enables systems-level protein expression analysis.
• Accurate protein quantification is challenged by computational demands and unaddressed protein variations like proteoforms.
• Ignoring proteoform variations limits statistical inference and downstream analyses for biomarker discovery.

Purpose of the Study:

• To introduce BP-Quant, a Bayesian model for improved relative protein abundance estimation.
• To address limitations in current methods by incorporating peptide signatures to identify variations.
• To enhance the power of statistical inference and downstream analyses in proteomics.

Main Methods:

• Developed a Bayesian model (BP-Quant) utilizing statistically derived peptide signatures.
• Identified peptides outside dominant patterns or multiple over-expressed patterns.
• Applied a hypothesis-driven approach to identify peptides with similar statistical behavior for a given protein.

Main Results:

• BP-Quant improves relative protein abundance estimates by considering protein variations.
• The model achieves accuracy comparable to state-of-the-art methods in proteoform identification.
• BP-Quant demonstrates significantly improved specificity in proteoform identification compared to existing methods.

Conclusions:

• BP-Quant offers a robust approach to protein quantification by accounting for proteoform diversity.
• The method enhances biomarker discovery potential by improving the accuracy and specificity of protein abundance estimation.
• BP-Quant provides a valuable tool for the proteomics community, available as open-source packages.