Scientific article
English

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

Published inNature Biotechnology, vol. 37, no. 11, p. 1283-1286
Publication date2019
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
  • Algorithms
  • Cell Line
  • Computational Biology/methods
  • Epitopes/metabolism
  • Histocompatibility Antigens Class II/chemistry/metabolism
  • Humans
  • Mass Spectrometry
  • Peptides/analysis/immunology
Affiliation entities Not a UNIGE publication
Citation (ISO format)
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
Main files (1)
Article (Published version)
accessLevelRestricted
Identifiers
Journal ISSN1087-0156
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Creation11/03/2020 13:37:00
First validation11/03/2020 13:37:00
Update time15/03/2023 21:16:32
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