Proteomics

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Deep motif deconvolution of HLA-II peptidomes

ABSTRACT: CD4 T cells are key for priming and regulating immune recognition of infected and cancer cells, but predictions of class II epitopes have limited accuracy. We profiled over 100’000 HLA-II ligands by Mass Spectrometry and developed a novel motif deconvolution algorithm to analyze these data. Our work demonstrates substantial improvements in the definition of HLA-II binding motifs and enhanced accuracy in HLA-II ligand and class II epitope predictions.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Michal Bassani-Sternberg  

PROVIDER: MSV000096130 | MassIVE | Fri Oct 18 10:18:00 BST 2024

SECONDARY ACCESSION(S): PXD012308

REPOSITORIES: MassIVE

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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. ...[more]

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