TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs

Motivation: T cells use T cell receptors (TCRs) to recognize small parts of antigens, called epitopes, presented by major histocompatibility complexes. Once an epitope is recognized, an immune response is initiated and T cell activation and proliferation by clonal expansion begin. Clonal populations of T cells with identical TCRs can remain in the body for years, thus forming immunological memory and potentially mappable immunological signatures, which could have implications in clinical applications including infectious diseases, autoimmunity and tumor immunology.Results: We introduce TCRconv, a deep learning model for predicting recognition between TCRs 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.

DOI

10.1093/bioinformatics/btac788

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Jokinen, E, Dumitrescu, A, Huuhtanen, J, Gligorijevic, V, Mustjoki, S, Bonneau, R, Heinonen, M & Lähdesmäki, H 2023, ' TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs ', Bioinformatics, vol. 39, no. 1, btac788 . https://doi.org/10.1093/bioinformatics/btac788

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https://urn.fi/URN:NBN:fi:aalto-202301251527

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[article-cris] Perustieteiden korkeakoulu / SCI

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TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs

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