MS²Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates

Immunopeptidomics aims to identify Major Histocompatibility Complex-presented peptides on every cell that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the non-tryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS²PIP and retention time predictions by DeepLC, have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS²PIP was tailored towards tryptic peptides, we have here retrained MS²PIP to include non-tryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides, but also yield further improvements for tryptic peptides. We show that the integration of new MS²PIP models, DeepLC, and Percolator in one software package, MS²Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS²Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Integration of immunopeptide MS²PIP models, DeepLC, and Percolator into MS²Rescore thus allows substantial improved identification of novel epitopes from existing immunopeptidomics workflows.