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ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model

TCR-epitope pair binding is the key component for T cell regulation. The ability to predict whether a given pair binds is fundamental to understanding the underlying biology of the binding mechanism as well as developing T-cell mediated immunotherapy approaches. The advent of large-scale public data...

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Detalles Bibliográficos
Autores principales: Cai, Michael, Bang, Seojin, Zhang, Pengfei, Lee, Heewook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299376/
https://www.ncbi.nlm.nih.gov/pubmed/35874725
http://dx.doi.org/10.3389/fimmu.2022.893247
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author Cai, Michael
Bang, Seojin
Zhang, Pengfei
Lee, Heewook
author_facet Cai, Michael
Bang, Seojin
Zhang, Pengfei
Lee, Heewook
author_sort Cai, Michael
collection PubMed
description TCR-epitope pair binding is the key component for T cell regulation. The ability to predict whether a given pair binds is fundamental to understanding the underlying biology of the binding mechanism as well as developing T-cell mediated immunotherapy approaches. The advent of large-scale public databases containing TCR-epitope binding pairs enabled the recent development of computational prediction methods for TCR-epitope binding. However, the number of epitopes reported along with binding TCRs is far too small, resulting in poor out-of-sample performance for unseen epitopes. In order to address this issue, we present our model ATM-TCR which uses a multi-head self-attention mechanism to capture biological contextual information and improve generalization performance. Additionally, we present a novel application of the attention map from our model to improve out-of-sample performance by demonstrating on recent SARS-CoV-2 data.
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spelling pubmed-92993762022-07-21 ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model Cai, Michael Bang, Seojin Zhang, Pengfei Lee, Heewook Front Immunol Immunology TCR-epitope pair binding is the key component for T cell regulation. The ability to predict whether a given pair binds is fundamental to understanding the underlying biology of the binding mechanism as well as developing T-cell mediated immunotherapy approaches. The advent of large-scale public databases containing TCR-epitope binding pairs enabled the recent development of computational prediction methods for TCR-epitope binding. However, the number of epitopes reported along with binding TCRs is far too small, resulting in poor out-of-sample performance for unseen epitopes. In order to address this issue, we present our model ATM-TCR which uses a multi-head self-attention mechanism to capture biological contextual information and improve generalization performance. Additionally, we present a novel application of the attention map from our model to improve out-of-sample performance by demonstrating on recent SARS-CoV-2 data. Frontiers Media S.A. 2022-07-06 /pmc/articles/PMC9299376/ /pubmed/35874725 http://dx.doi.org/10.3389/fimmu.2022.893247 Text en Copyright © 2022 Cai, Bang, Zhang and Lee 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
Cai, Michael
Bang, Seojin
Zhang, Pengfei
Lee, Heewook
ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model
title ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model
title_full ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model
title_fullStr ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model
title_full_unstemmed ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model
title_short ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model
title_sort atm-tcr: tcr-epitope binding affinity prediction using a multi-head self-attention model
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299376/
https://www.ncbi.nlm.nih.gov/pubmed/35874725
http://dx.doi.org/10.3389/fimmu.2022.893247
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