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.
Bibliographical noteFunding Information:
We thank H. Vlamakis, T. Reimels, and I. Latorre for scientific input, J. Gracias for technical assistance, and P. Rogers for the FACS work. This work was supported by funding from The Leona M. and Harry B. Helmsley Charitable Trust, National Institutes of Health grants DK043351, AI109725, AT009708, and DK092405, and the Juvenile Diabetes Research Fund to R.J.X.
© 2018, The Author(s), under exclusive licence to Springer Nature America, Inc.