Unifying the analysis of bottom-up proteomics data with CHIMERYS

Area of Science:

• Proteomics
• Analytical Chemistry
• Bioinformatics

Background:

• Proteomic workflows produce complex peptide mixtures analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS).
• Tandem mass spectra are often chimeric, containing fragment ions from multiple peptides.
• Current analysis relies on separate software for different data acquisition strategies, despite unified underlying data.

Purpose of the Study:

• To introduce CHIMERYS, a unified spectrum-centric algorithm for deconvoluting chimeric spectra.
• To enable accurate peptide identification and quantification across diverse proteomic data acquisition methods.

Main Methods:

• CHIMERYS employs a spectrum-centric approach for deconvolution.
• It utilizes accurate predictions of peptide retention time and fragment ion intensities.
• Regularized linear regression is applied to explain fragment ion intensity with minimal peptide explanations.
• Rigorous false discovery rate (FDR) control is implemented.

Main Results:

• CHIMERYS accurately identifies and quantifies multiple peptides within single tandem mass spectra.
• The algorithm demonstrates effectiveness across data-dependent, data-independent, and parallel reaction monitoring experiments.
• It unifies proteomic data analysis by addressing chimeric spectra.

Conclusions:

• CHIMERYS offers a unified and accurate solution for analyzing complex proteomic data.
• The algorithm enhances the identification and quantification of peptides in LC-MS/MS workflows.
• It represents a significant advancement in proteomic data analysis software.