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

By Jennifer G. Abelin, D. Keskin, Siranush Sarkizova, Christina R. Hartigan, Wandi Zhang, John Sidney, Jonathan D. Stevens, W. Lane, G. Zhang, T. Eisenhaure and 5 more. Immunity, 2017

600 citations recorded by the source corpus.

SUMMARY 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. Graphical Abstract Figure. No Caption available. Highlights24,000 HLA class I peptides were identified through a scalable MS‐based pipeline.Mono‐allelic data revealed binding motifs that were validated biochemically.Comprehensive analyses provide an updated portrait of antigen processing rules.Neural networks were trained for 16 alleles and outperform standard by 2‐fold. &NA; HLA class I binding prediction has traditionally been based on biochemical binding experiments. Abelin and colleagues present an LC‐MS/MS‐based workflow and analytical framework that greatly accelerates gains in prediction performance. Key advances include the discovery of sequence motifs and improved quantification of the roles of gene expression and proteasomal processing.

Subfields: Immunology, Microbiology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3c67d2a232bbc8c488cb43c06a57a5877fc29e26

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Citations in the corpus

MHCflurry: open-source class I MHC binding affinity prediction (2017, methodology): “The ABELIN dataset was derived from 20,451 sequences of MHC-displayed ligands eluted from a B cell line and identified by mass spec by [20].”
NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data (2020, methodology): “Even though one of the main contributions to the improved performance of the prediction methods proposed here (and other recently published methods) is the integration of MS derived EL data, MS data itself contains an inherent bias imposed resulting in for instance overrepresentation of ‘flyable’ (2”