gpGrouper: A Peptide Grouping Algorithm for Gene-Centric Inference and Quantitation of Bottom-Up Proteomics Data

Area of Science:

• Proteomics
• Bioinformatics
• Quantitative Mass Spectrometry

Background:

• Peptide grouping significantly impacts quantitative mass spectrometry results.
• Existing methods for protein inference and quantitation have limitations, particularly with complex samples.

Purpose of the Study:

• To introduce gpGrouper, a novel algorithm for protein inference and quantitation.
• To improve the accuracy of pseudo-absolute intensity-based absolute quantification (iBAQ) by weighted distribution of shared peptide areas.
• To enable robust analysis of two-species samples, including patient-derived xenografts (PDXs).

Main Methods:

• Developed gpGrouper, an algorithm for protein group assignment by gene locus.
• Implemented weighted distribution of shared peptide areas based on unique peptide peak ratios.
• Validated the algorithm on two-species samples, including PDXs, and compared results with conventional methods.

Main Results:

• gpGrouper improves quantitation accuracy by distributing shared peptide quantities based on unique peptide peak ratios, outperforming winner-take-all approaches.
• The algorithm effectively handles two-species samples (e.g., PDXs) without excluding host or shared peptides.
• gpGrouper generates iBAQ results comparable across label-free, isotopic, and isobaric proteomics methods.

Conclusions:

• gpGrouper offers an improved method for protein inference and quantitation in mass spectrometry.
• The algorithm enhances accuracy and enables comprehensive analysis of complex biological samples, including PDXs.
• gpGrouper facilitates cross-platform comparability of quantitative proteomics data.