Footprints of antigen processing boost MHC class II natural ligand predictions

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

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  • Author: Barra, Carolina M.

    Universidad Nacional de San Martin, Argentina

  • Author: Alvarez, Bruno

    Universidad Nacional de San Martin, Argentina

  • Author: Paul, Sinu

    La Jolla Institute for Allergy and Immunology, United States

  • Author: Sette, Alessandro

    La Jolla Institute for Allergy and Immunology, United States

  • Author: Peters, Bjoern

    La Jolla Institute for Allergy and Immunology, United States

  • Author: Andreatta, Massimo

    Universidad Nacional de San Martin, Argentina

  • Author: Buus, Søren

    University of Copenhagen, Denmark

  • Author: Nielsen, Morten

    Department of Bio and Health Informatics, Technical University of Denmark

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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
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

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

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