Footprints of antigen processing boost MHC class II natural ligand predictions

Carolina Barra, Bruno Alvarez, Sinu Paul, Alessandro Sette, Bjoern Peters, Massimo Andreatta, Søren Buus, Morten Nielsen*

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

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    Abstract

    BACKGROUND: Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing. METHODS: We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets. 

    RESULTS: We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand. 

    CONCLUSIONS: The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.

    Original languageEnglish
    Article number84
    JournalGenome Medicine
    Volume10
    Issue number1
    Number of pages15
    ISSN1756-994X
    DOIs
    Publication statusPublished - 2018

    Keywords

    • Antigen processing
    • Binding predictions
    • Eluted ligands
    • Machine learning
    • Mass spectrometry
    • MHC-II
    • Neural networks
    • T cell epitope

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