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

Mass spectrometry-based
discovery of bacterial immunopeptides presented
by infected cells allows untargeted discovery of bacterial antigens
that can serve as vaccine candidates. However, reliable identification
of bacterial epitopes is challenged by their extremely low abundance.
Here, we describe an optimized bioinformatic framework to enhance
the confident identification of bacterial immunopeptides. Immunopeptidomics
data of cell cultures infected with Listeria monocytogenes were searched by four different search engines, PEAKS, Comet, Sage
and MSFragger, followed by data-driven rescoring with MS2Rescore. Compared with individual search engine results, this integrated
workflow boosted immunopeptide identification by an average of 27%
and led to the high-confidence detection of 18 additional bacterial
peptides (+27%) matching 15 different Listeria proteins
(+36%). Despite the strong agreement between the search engines, a
small number of spectra (<1%) had ambiguous matches to multiple
peptides and were excluded to ensure high-confidence identifications.
Finally, we demonstrate our workflow with sensitive timsTOF SCP data
acquisition and find that rescoring, now with inclusion of ion mobility
features, identifies 76% more peptides compared to Q Exactive HF acquisition.
Together, our results demonstrate how integration of multiple search
engine results along with data-driven rescoring maximizes immunopeptide
identification, boosting the detection of high-confidence bacterial
epitopes for vaccine development.