期刊
IMMUNITY
卷 46, 期 2, 页码 315-326出版社
CELL PRESS
DOI: 10.1016/j.immuni.2017.02.007
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类别
资金
- Blavatnik Family Foundation
- National Cancer Institute (NCI) [R01 CA155010]
- NCI CPTAC [U24 CA160034]
- NHGRI [T32 HG002295]
- Dana-Farber/Harvard Cancer Center Kidney Cancer SPORE [2P50CA101942-11A1]
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 monoallelic 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.
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