Maximizing Immunopeptidomics-Based Bacterial Epitope Discovery by Multiple Search Engines and Rescoring

Area of Science:

• Immunology
• Bioinformatics
• Vaccine Development

Background:

• Mass spectrometry enables discovery of bacterial antigens for vaccines.
• Identifying low-abundance bacterial epitopes remains a significant challenge.

Purpose of the Study:

• To develop and validate an optimized bioinformatic framework for enhanced identification of bacterial immunopeptides.
• To improve the confident detection of bacterial epitopes for vaccine candidate discovery.

Main Methods:

• Integrated immunopeptidomics data analysis using four search engines (PEAKS, Comet, Sage, MSFragger).
• Data-driven rescoring with MS2Rescore, incorporating ion mobility features.
• Validation with timsTOF SCP and Q Exactive HF data acquisition.

Main Results:

• The integrated workflow increased bacterial immunopeptide identification by 27% compared to individual search engines.
• 18 additional bacterial peptides and 15 new Listeria protein matches were identified with high confidence.
• Rescoring with ion mobility features identified 76% more peptides on timsTOF SCP compared to Q Exactive HF.

Conclusions:

• Integration of multiple search engines and data-driven rescoring maximizes bacterial immunopeptide identification.
• This enhanced approach significantly boosts the detection of high-confidence bacterial epitopes for vaccine development.
• The optimized framework improves the discovery pipeline for novel vaccine candidates.