Maximizing immunopeptidomics-based bacterial epitope discovery by rescoring and integrating the results of multiple search engines

Discovery of human leukocyte antigen (HLA) class I presented peptides of bacterial origin is essential to discover bacterial antigen targets as putative vaccine constituents. However, reliable identification of such HLA-presented bacterial epitopes are extremely low abundant compared to the host proteome. Here, we describe an upgraded bioinformatical workflow to enhance the confident detection of bacterial immunopeptides. Re-analysis of Listeria monocytogenes (Listeria)-infected cell cultures was searched by four search engines in parallel with follow-up by rescoring by MS2Rescore and integration of all search results. Boosting identification by both rescoring and integration, this delivered an additional 18 Listeria peptides (+26.5%) matching 15 different proteins (+35.7%) compared to the initial analysis. Moreover, conflicts between search engine results uncovered ambiguities in spectra-to-peptide assignments, in some cases with spectra assigned to human and Listeria peptides. Finally, we demonstrate how our workflow is compatible with timsTOF data acquisition, incorporating rescoring of ion mobility features using TIMS2Rescore.