Liquid chromatography tandem mass spectrometry (LC-MS/MS) has become the standard platform for the characterization of immunopeptidomes, the collection of peptides naturally presented by MHC molecules to the cell surface. The protocols and algorithms used for immunopeptidomics data analysis are based on tools developed for traditional bottom-up proteomics, that address the identification of peptides generated by tryptic digestion. Such algorithms are generally not tailored to the specific requirements of MHC ligand identification and, as a consequence, immunopeptidomics datasets suffer from dismissal of informative spectral information and high false discovery rates. Here, we propose a new pipeline for the refinement of peptide-spectrum matches (PSM), based on the assumption that immunopeptidomes contain a limited number of recurring peptide motifs, corresponding to MHC specificities. Sequence motifs are learned directly from the individual peptidome by training a prediction model on high-confidence PSMs. The model is then applied to PSM candidates with lower confidence, and sequences that score significantly higher than random peptides are rescued as likely true ligands. We applied the pipeline to MHC class I immunopeptidomes from three different species, and showed that it can increase the number of identified ligands by up to 20-30%, while effectively removing false positives and products of co-precipitation. Spectral validation using synthetic peptides confirmed the identity of a large proportion of rescued ligands in the experimental peptidome. This article is protected by copyright. All rights reserved.
MHC, machine learning, mass spectrometry, peptidome, sequence motif