Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes

Bruno Alvarez, Carolina Barra, Morten Nielsen, Massimo Andreatta*

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

    162 Downloads (Pure)


    Recent advances in proteomics and mass-spectrometry have widely expanded the detectable peptide repertoire presented by major histocompatibility complex (MHC) molecules on the cell surface, collectively known as the immunopeptidome. Finely characterizing the immunopeptidome brings about important basic insights into the mechanisms of antigen presentation, but can also reveal promising targets for vaccine development and cancer immunotherapy. This report describes a number of practical and efficient approaches to analyze immunopeptidomics data, discussing the identification of meaningful sequence motifs in various scenarios and considering current limitations. Guidelines are provided for the filtering of false hits and contaminants, and to address the problem of motif deconvolution in cell lines expressing multiple MHC alleles, both for the MHC class I and class II systems. Finally, it is demonstrated how machine learning can be readily employed by non-expert users to generate accurate prediction models directly from mass-spectrometry eluted ligand data sets.
    Original languageEnglish
    Article number1700252
    Issue number12
    Number of pages10
    Publication statusPublished - 2018

    Fingerprint Dive into the research topics of 'Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes'. Together they form a unique fingerprint.

    Cite this