Footprints of antigen processing boost MHC class II natural ligand predictions
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Footprints of antigen processing boost MHC class II natural ligand predictions. / Barra, Carolina; Alvarez, Bruno; Paul, Sinu; Sette, Alessandro; Peters, Bjoern; Andreatta, Massimo; Buus, Søren; Nielsen, Morten.
I: Genome Medicine, Bind 10, 84, 2018.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Footprints of antigen processing boost MHC class II natural ligand predictions
AU - Barra, Carolina
AU - Alvarez, Bruno
AU - Paul, Sinu
AU - Sette, Alessandro
AU - Peters, Bjoern
AU - Andreatta, Massimo
AU - Buus, Søren
AU - Nielsen, Morten
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Antigen processing
KW - Binding predictions
KW - Eluted ligands
KW - Machine learning
KW - Mass spectrometry
KW - MHC-II
KW - Neural networks
KW - T cell epitope
U2 - 10.1186/s13073-018-0594-6
DO - 10.1186/s13073-018-0594-6
M3 - Journal article
C2 - 30446001
AN - SCOPUS:85056738224
VL - 10
JO - Genome Medicine
JF - Genome Medicine
SN - 1756-994X
M1 - 84
ER -
ID: 210064011