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- W2896667872 abstract "Identifying immunodominant T cell epitopes remains a significant challenge in the context of infectious disease, autoimmunity, and immuno-oncology. To address the challenge of antigen discovery, we developed a quantitative proteomic approach that enabled unbiased identification of major histocompatibility complex class II (MHCII)–associated peptide epitopes and biochemical features of antigenicity. On the basis of these data, we trained a deep neural network model for genome-scale predictions of immunodominant MHCII-restricted epitopes. We named this model bacteria originated T cell antigen (BOTA) predictor. In validation studies, BOTA accurately predicted novel CD4 T cell epitopes derived from the model pathogen Listeria monocytogenes and the commensal microorganism Muribaculum intestinale. To conclusively define immunodominant T cell epitopes predicted by BOTA, we developed a high-throughput approach to screen DNA-encoded peptide–MHCII libraries for functional recognition by T cell receptors identified from single-cell RNA sequencing. Collectively, these studies provide a framework for defining the immunodominance landscape across a broad range of immune pathologies. A quantitative proteomic approach overcomes a major bottleneck in translational immunology, namely the identification of autologous and bacterial immunodominant major histocompatibility complex class II epitopes based on genomic sequences." @default.
- W2896667872 created "2018-10-26" @default.
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- W2896667872 date "2018-10-22" @default.
- W2896667872 modified "2023-10-15" @default.
- W2896667872 title "Antigen discovery and specification of immunodominance hierarchies for MHCII-restricted epitopes" @default.
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- W2896667872 doi "https://doi.org/10.1038/s41591-018-0203-7" @default.
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