dc.creator | Barra, Carolina M. | |
dc.creator | Alvarez, Bruno | |
dc.creator | Paul, Sinu | |
dc.creator | Sette, Alessandro | |
dc.creator | Peters, Bjoern | |
dc.creator | Andreatta, Massimo | |
dc.creator | Buus, Søren | |
dc.creator | Nielsen, Morten | |
dc.date.accessioned | 2020-04-16T17:58:44Z | |
dc.date.accessioned | 2022-10-15T13:02:53Z | |
dc.date.available | 2020-04-16T17:58:44Z | |
dc.date.available | 2022-10-15T13:02:53Z | |
dc.date.created | 2020-04-16T17:58:44Z | |
dc.date.issued | 2018-11 | |
dc.identifier | 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 | |
dc.identifier | 1756-994X | |
dc.identifier | http://hdl.handle.net/11336/102765 | |
dc.identifier | CONICET Digital | |
dc.identifier | CONICET | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4389083 | |
dc.description.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. | |
dc.language | eng | |
dc.publisher | Springer Nature | |
dc.relation | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1186/s13073-018-0594-6 | |
dc.relation | info:eu-repo/semantics/altIdentifier/url/https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-018-0594-6 | |
dc.rights | https://creativecommons.org/licenses/by/2.5/ar/ | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | ANTIGEN PROCESSING | |
dc.subject | BINDING PREDICTIONS | |
dc.subject | ELUTED LIGANDS | |
dc.subject | MACHINE LEARNING | |
dc.subject | MASS SPECTROMETRY | |
dc.subject | MHC-II | |
dc.subject | NEURAL NETWORKS | |
dc.subject | T CELL EPITOPE | |
dc.title | Footprints of antigen processing boost MHC class II natural ligand predictions | |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:ar-repo/semantics/artículo | |
dc.type | info:eu-repo/semantics/publishedVersion | |