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Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes

Racle, Julien
Michaux, Justine
Rockinger, Georg Alexander
Arnaud, Marion
Bobisse, Sara
Chong, Chloe
Guillaume, Philippe
Coukos, George
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Published in Nature Biotechnology. 2019, vol. 37, no. 11, p. 1283-1286
Abstract Predictions of epitopes presented by class II human leukocyte antigen molecules (HLA-II) have limited accuracy, restricting vaccine and therapy design. Here we combined unbiased mass spectrometry with a motif deconvolution algorithm to profile and analyze a total of 99,265 unique peptides eluted from HLA-II molecules. We then trained an epitope prediction algorithm with these data and improved prediction of pathogen and tumor-associated class II neoepitopes.
Keywords AlgorithmsCell LineComputational Biology/methodsEpitopes/metabolismHistocompatibility Antigens Class II/chemistry/metabolismHumansMass SpectrometryPeptides/analysis/immunology
PMID: 31611696
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Research group Ciblage des lymphocytes sécrétant des cytokines (1015)
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RACLE, Julien et al. Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. In: Nature Biotechnology, 2019, vol. 37, n° 11, p. 1283-1286. doi: 10.1038/s41587-019-0289-6

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Deposited on : 2020-03-19

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