Finding coexisting combinations of posttranslational modifications with HomMTM spectra

Area of Science:

• Biochemistry
• Proteomics
• Computational Biology

Background:

• Posttranslational modifications (PTMs) regulate protein function, with phosphorylation being a key example involved in cellular signaling.
• Current peptide identification tools typically report only one isoform per spectrum, overlooking the biological significance of coexisting isoforms.

Purpose of the Study:

• To develop a computational workflow for identifying multiple coexisting peptide isoforms within a single tandem mass spectrum.
• To enable a deeper understanding of how different combinations of PTMs function and compete within cells.

Main Methods:

• An algorithm was designed to identify up to two coexisting isoforms in a query spectrum.
• The workflow was applied to real phosphopeptide datasets (U2OS and UCEC) and evaluated using simulated data.

Main Results:

• Coexisting phosphopeptide isoforms were detected in 2.16% of the U2OS dataset and 7.19% of the UCEC dataset.
• The developed algorithm achieved an 85.4% accuracy in isoform identification on simulated datasets.

Conclusions:

• The developed workflow successfully identifies coexisting peptide isoforms, offering new insights into PTM dynamics.
• This approach is crucial for understanding the functional diversity arising from simultaneous PTMs.