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Title

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

Authors
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
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PMID: 31611696
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Article (Published version) (5.6 MB) - document accessible for UNIGE members only Limited access to UNIGE
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 https://archive-ouverte.unige.ch/unige:132569

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Deposited on : 2020-03-19

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