DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics

Proteomics. 2020 Nov;20(21-22):e1900334. doi: 10.1002/pmic.201900334. Epub 2020 Sep 27.


The identification of major histocompatibility complex (MHC)-binding peptides in mass spectrometry (MS)-based immunopeptideomics relies largely on database search engines developed for proteomics data analysis. However, because immunopeptidomics experiments do not involve enzymatic digestion at specific residues, an inflated search space leads to a high false positive rate and low sensitivity in peptide identification. In order to improve the sensitivity and reliability of peptide identification, a post-processing tool named DeepRescore is developed. DeepRescore combines peptide features derived from deep learning predictions, namely accurate retention timeand MS/MS spectra predictions, with previously used features to rescore peptide-spectrum matches. Using two public immunopeptidomics datasets, it is shown that rescoring by DeepRescore increases both the sensitivity and reliability of MHC-binding peptide and neoantigen identifications compared to existing methods. It is also shown that the performance improvement is, to a large extent, driven by the deep learning-derived features. DeepRescore is developed using NextFlow and Docker and is available at

Keywords: bioinformatics; deep learning; immunopeptidomics; proteomics; retention time.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Databases, Protein
  • Deep Learning*
  • Humans
  • Peptides
  • Reproducibility of Results
  • Tandem Mass Spectrometry*


  • Peptides