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bioRxiv · 10.1101/2023.09.13.557561

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells

Croce, G.·Bobisse, S.·Moreno, D. L.·Schmidt, J.·Guillame, P.·Harari, A.·Gfeller, D.
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Abstract

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T-cell activation is elicited by the binding of the T-cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its and {beta} chains. Here, we collected and curated a dataset of 17,715 {beta}TCRs interacting with dozens of class I and class II epitopes. We used this curated data to develop MixTCRpred, a deep learning TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual T cells demonstrates that MixTCRpred can identify chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.

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Croce, G., Bobisse, S., Moreno, D. L., Schmidt, J., Guillame, P., Harari, A., Gfeller, D.. 2023-09-16. Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells. https://doi.org/10.1101/2023.09.13.557561

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