MSRescore: Data-driven rescoring dramatically boosts immunopeptide identification rates.
Fiche publication
Date publication
juillet 2022
Journal
Molecular & cellular proteomics : MCP
Auteurs
Membres identifiés du Cancéropôle Est :
Dr CARAPITO Christine
Tous les auteurs :
Declercq A, Bouwmeester R, Hirschler A, Carapito C, Degroeve S, Martens L, Gabriels R
Lien Pubmed
Résumé
Immunopeptidomics aims to identify Major Histocompatibility Complex-presented peptides on almost all 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 MSPIP and retention time predictions by DeepLC, have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MSPIP was tailored towards tryptic peptides, we have here retrained MSPIP 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 MSPIP models, DeepLC, and Percolator in one software package, MSRescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MSRescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Altogether, MSRescore thus allows substantially improved identification of novel epitopes from existing immunopeptidomics workflows.
Mots clés
bioinformatics, immunopeptidomics, machine learning, mass spectrometry, peptide identification, proteomics
Référence
Mol Cell Proteomics. 2022 07 5;:100266