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Fully unsupervised identification of HLA-I motifs

ABSTRACT: The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across publicly available as well as ten newly generated high quality HLA peptidomics datasets, we show that we can rapidly and accurately identify HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. This fully unsupervised approach uncovers new motifs for several alleles without known ligands and significantly improves neo-epitope predictions in three melanoma patients.

INSTRUMENT(S): Orbitrap Fusion, Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): B Cell, T Cell

SUBMITTER: Michal Bassani-Sternberg

LAB HEAD: Michal Bassani-Sternberg

PROVIDER: PXD005231 | Pride | 2017-08-18

REPOSITORIES: Pride

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