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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Bibliographic Details
Published in:Frontiers in immunology 2022-07, Vol.13, p.893247-893247
Main Authors: Cai, Michael, Bang, Seojin, Zhang, Pengfei, Lee, Heewook
Format: Article
Language:English
Subjects:
adaptive immunotherapy
antigen
binding affinity
epitope
Immunology
machine learning
TCR
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Summary: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.
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2022.893247