DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics.

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Abstract

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, we developed DeepRescore, a post-processing tool that combines peptide features derived from deep learning predictions, namely accurate retention time and MS/MS spectra predictions, with previously used features to rescore peptide-spectrum matches. Using two public immunopeptidomics datasets, we showed that rescoring by DeepRescore increased both the sensitivity and reliability of MHC-binding peptide and neoantigen identifications compared to existing methods. We also showed that the performance improvement was, to a large extent, driven by the deep learning-derived features. DeepRescore was developed using NextFlow and Docker and is available at https://github.com/bzhanglab/DeepRescore. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.