Footprints of antigen processing boost MHC class II natural ligand binding predictions
Posted 20 Mar 2018
bioRxiv DOI: 10.1101/285767 (published DOI: 10.1186/s13073-018-0594-6)
Posted 20 Mar 2018
Background: Major Histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide: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:MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity datasets. 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:MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.
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