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Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction

ABSTRACT: Abelin JG, Keskin DB, Sarkizova S, Hartigan CR, Zhang W, Sidney J, Stevens J, Lane W, Zhang GL, Eisenhaure T, Clauser KR, Hacohen N, Rooney MS, Carr SA, and Wu, CJ. Immunity, 2017. 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.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Steven A. Carr

PROVIDER: MSV000080527 | MassIVE | Wed Feb 01 08:09:00 GMT 2017

REPOSITORIES: MassIVE

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Improved prediction of endogenous HLA-associated epitopes based on mono-allelic mass spectrometry profiling

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Patient datasets for: A large peptidome dataset improves HLA class I epitope prediction across most of the human population

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Mono-allelic datasets for: A large peptidome dataset improves HLA class I epitope prediction across most of the human population

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