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Nat Biotechnol DOI:10.1038/s41587-019-0322-9

A large peptidome dataset improves HLA class I epitope prediction across most of the human population.

Publication TypeJournal Article
Year of Publication2019
AuthorsSarkizova, S, Klaeger, S, Le, PM, Li, LW, Oliveira, G, Keshishian, H, Hartigan, CR, Zhang, W, Braun, DA, Ligon, KL, Bachireddy, P, Zervantonakis, IK, Rosenbluth, JM, Ouspenskaia, T, Law, T, Justesen, S, Stevens, J, Lane, WJ, Eisenhaure, T, Zhang, GLan, Clauser, KR, Hacohen, N, Carr, SA, Wu, CJ, Keskin, DB
JournalNat Biotechnol
Date Published2019 Dec 16
ISSN1546-1696
Abstract

Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.

DOI10.1038/s41587-019-0322-9
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/31844290?dopt=Abstract

Alternate JournalNat. Biotechnol.
PubMed ID31844290
Grant ListT32HG002295 / / U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) /