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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 Publication2020
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
Volume38
Issue2
Pages199-209
Date Published2020 02
ISSN1546-1696
KeywordsAlgorithms, Alleles, Amino Acid Motifs, Cell Line, Databases, Protein, Epitopes, Genetic Loci, Histocompatibility Antigens Class I, Humans, Ligands, Peptide Hydrolases, Peptides, Proteasome Endopeptidase Complex, Proteome
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
PubMed Central IDPMC7008090
Grant ListP50 CA101942 / CA / NCI NIH HHS / United States
T32 HG002295 / HG / NHGRI NIH HHS / United States
T32 CA009172 / CA / NCI NIH HHS / United States
U24 CA224331 / CA / NCI NIH HHS / United States
R21 CA216772 / CA / NCI NIH HHS / United States
R01 CA155010 / CA / NCI NIH HHS / United States
U01 CA214125 / CA / NCI NIH HHS / United States
T32 CA207021 / CA / NCI NIH HHS / United States
R01 HL103532 / HL / NHLBI NIH HHS / United States
U24 CA210986 / CA / NCI NIH HHS / United States