en
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 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
ISSN of the journal1087-0156
267views
2downloads

Technical informations

Creation03/11/2020 1:37:00 PM
First validation03/11/2020 1:37:00 PM
Update time03/15/2023 9:16:32 PM
Status update03/15/2023 9:16:32 PM
Last indexation01/17/2024 9:13:35 AM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack