info:eu-repo/semantics/article
Footprints of antigen processing boost MHC class II natural ligand predictions
Fecha
2018-11Registro en:
Barra, Carolina M.; Alvarez, Bruno; Paul, Sinu; Sette, Alessandro; Peters, Bjoern; et al.; Footprints of antigen processing boost MHC class II natural ligand predictions; Springer Nature; Genome Medicine; 10; 1; 11-2018
1756-994X
CONICET Digital
CONICET
Autor
Barra, Carolina M.
Alvarez, Bruno
Paul, Sinu
Sette, Alessandro
Peters, Bjoern
Andreatta, Massimo
Buus, Søren
Nielsen, Morten
Resumen
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.