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

Giancarlo Croce, Sara Bobisse, Dana Léa Moreno, Julien Schmidt, Philippe Guillame, Alexandre Harari and David Gfeller ()
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Giancarlo Croce: University of Lausanne
Sara Bobisse: Agora Cancer Research Centre
Dana Léa Moreno: University of Lausanne
Julien Schmidt: University of Lausanne
Philippe Guillame: Swiss Cancer Center Leman (SCCL)
Alexandre Harari: University of Lausanne
David Gfeller: University of Lausanne

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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 α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific 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 α T cells demonstrates that MixTCRpred can identify α 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.

Date: 2024
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DOI: 10.1038/s41467-024-47461-8

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