Predicting recognition between T cell receptors and epitopes using contextualized motifs

Jokinen, E.; Dumitrescu, A.; Huuhtanen, J.; Gligorijevic, V.; Heinonen, M.; Mustjoki, S.; Bonneau, R.; Lähdesmäki, H.

doi:10.1101/2022.05.23.493034

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bioRxiv · 10.1101/2022.05.23.493034

Predicting recognition between T cell receptors and epitopes using contextualized motifs

Jokinen, E.·Dumitrescu, A.·Huuhtanen, J.·Gligorijevic, V.·Heinonen, M.·Mustjoki, S.·Bonneau, R.·Lähdesmäki, H.

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Abstract

We introduce TCRconv, a deep learning model for predicting recognition between T-cell receptors and epitopes. TCRconv uses a deep protein language model and convolutions to extract contextualized motifs and provides state-of-the-art TCR-epitope prediction accuracy. Using TCR repertoires from COVID-19 patients, we demonstrate that TCRconv can provide insight into T-cell dynamics and phenotypes during the disease.

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Jokinen, E., Dumitrescu, A., Huuhtanen, J., Gligorijevic, V., Heinonen, M., Mustjoki, S., Bonneau, R., Lähdesmäki, H.. 2022-05-24. Predicting recognition between T cell receptors and epitopes using contextualized motifs. https://doi.org/10.1101/2022.05.23.493034

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