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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...

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Autores principales: Jokinen, Emmi, Dumitrescu, Alexandru, Huuhtanen, Jani, Gligorijević, Vladimir, Mustjoki, Satu, Bonneau, Richard, Heinonen, Markus, Lähdesmäki, Harri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825763/
https://www.ncbi.nlm.nih.gov/pubmed/36477794
http://dx.doi.org/10.1093/bioinformatics/btac788
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author Jokinen, Emmi
Dumitrescu, Alexandru
Huuhtanen, Jani
Gligorijević, Vladimir
Mustjoki, Satu
Bonneau, Richard
Heinonen, Markus
Lähdesmäki, Harri
author_facet Jokinen, Emmi
Dumitrescu, Alexandru
Huuhtanen, Jani
Gligorijević, Vladimir
Mustjoki, Satu
Bonneau, Richard
Heinonen, Markus
Lähdesmäki, Harri
author_sort Jokinen, Emmi
collection PubMed
description 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. AVAILABILITY AND IMPLEMENTATION: TCRconv is available at https://github.com/emmijokinen/tcrconv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-98257632023-01-10 TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs Jokinen, Emmi Dumitrescu, Alexandru Huuhtanen, Jani Gligorijević, Vladimir Mustjoki, Satu Bonneau, Richard Heinonen, Markus Lähdesmäki, Harri Bioinformatics Original Paper 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. AVAILABILITY AND IMPLEMENTATION: TCRconv is available at https://github.com/emmijokinen/tcrconv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-12-07 /pmc/articles/PMC9825763/ /pubmed/36477794 http://dx.doi.org/10.1093/bioinformatics/btac788 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Jokinen, Emmi
Dumitrescu, Alexandru
Huuhtanen, Jani
Gligorijević, Vladimir
Mustjoki, Satu
Bonneau, Richard
Heinonen, Markus
Lähdesmäki, Harri
TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs
title TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs
title_full TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs
title_fullStr TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs
title_full_unstemmed TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs
title_short TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs
title_sort tcrconv: predicting recognition between t cell receptors and epitopes using contextualized motifs
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825763/
https://www.ncbi.nlm.nih.gov/pubmed/36477794
http://dx.doi.org/10.1093/bioinformatics/btac788
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