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Quantitative approaches for decoding the specificity of the human T cell repertoire
T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539903/ https://www.ncbi.nlm.nih.gov/pubmed/37781387 http://dx.doi.org/10.3389/fimmu.2023.1228873 |
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author | Ghoreyshi, Zahra S. George, Jason T. |
author_facet | Ghoreyshi, Zahra S. George, Jason T. |
author_sort | Ghoreyshi, Zahra S. |
collection | PubMed |
description | T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method’s mathematical approach, predictive performance, and limitations. |
format | Online Article Text |
id | pubmed-10539903 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105399032023-09-30 Quantitative approaches for decoding the specificity of the human T cell repertoire Ghoreyshi, Zahra S. George, Jason T. Front Immunol Immunology T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method’s mathematical approach, predictive performance, and limitations. Frontiers Media S.A. 2023-09-07 /pmc/articles/PMC10539903/ /pubmed/37781387 http://dx.doi.org/10.3389/fimmu.2023.1228873 Text en Copyright © 2023 Ghoreyshi and George https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Ghoreyshi, Zahra S. George, Jason T. Quantitative approaches for decoding the specificity of the human T cell repertoire |
title | Quantitative approaches for decoding the specificity of the human T cell repertoire |
title_full | Quantitative approaches for decoding the specificity of the human T cell repertoire |
title_fullStr | Quantitative approaches for decoding the specificity of the human T cell repertoire |
title_full_unstemmed | Quantitative approaches for decoding the specificity of the human T cell repertoire |
title_short | Quantitative approaches for decoding the specificity of the human T cell repertoire |
title_sort | quantitative approaches for decoding the specificity of the human t cell repertoire |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539903/ https://www.ncbi.nlm.nih.gov/pubmed/37781387 http://dx.doi.org/10.3389/fimmu.2023.1228873 |
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