You are here

Immunity DOI:10.1016/j.immuni.2017.02.007

Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction.

Publication TypeJournal Article
Year of Publication2017
AuthorsAbelin, JG, Keskin, DB, Sarkizova, S, Hartigan, CR, Zhang, W, Sidney, J, Stevens, J, Lane, W, Zhang, GLan, Eisenhaure, TM, Clauser, KR, Hacohen, N, Rooney, MS, Carr, SA, Wu, CJ
JournalImmunity
Volume46
Issue2
Pages315-326
Date Published2017 Feb 21
ISSN1097-4180
Abstract

Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural-network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.

DOI10.1016/j.immuni.2017.02.007
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/28228285?dopt=Abstract

Alternate JournalImmunity
PubMed ID28228285
PubMed Central IDPMC5405381
Grant ListP50 CA101942 / CA / NCI NIH HHS / United States
R01 CA155010 / CA / NCI NIH HHS / United States
T32 HG002295 / HG / NHGRI NIH HHS / United States
U24 CA160034 / CA / NCI NIH HHS / United States