Systematic detection of functional proteoform groups from bottom-up proteomic datasets.

Area of Science:

• Proteomics
• Molecular Biology
• Bioinformatics

Background:

• Cellular functional diversity arises from molecular complexity exceeding the genome.
• Multiple related proteins, or proteoforms, are generated from single genes, enhancing cellular function.
• Analyzing proteoforms using traditional bottom-up proteomics is challenging due to peptide-level measurements.

Purpose of the Study:

• To develop a computational tool for assessing proteoforms from bottom-up proteomics data.
• To enable the systematic assignment of peptides to co-varying proteoform groups.
• To facilitate the functional assessment of proteoforms directly from standard proteomics datasets.

Main Methods:

• Development of COPF (COrrelation-based functional ProteoForm assessment) tool.
• Utilizing peptide correlation analysis to group co-varying peptides.
• Application to protein complex co-fractionation and protein abundance datasets.

Main Results:

• COPF systematically assigns peptides to co-varying proteoform groups.
• Demonstrated detection of assembly-specific proteoform groups in co-fractionation data.
• Showcased detection of tissue-specific proteoform groups in abundance data.

Conclusions:

• COPF provides a method for systematic proteoform assessment in bottom-up proteomics.
• The tool enables the discovery of functional proteoform variations.
• This approach lays groundwork for understanding proteoform implications directly from proteomics data.