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

ABSTRACT: 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.

SUBMITTER: Cai M

PROVIDER: S-EPMC9299376 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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

Cai Michael M Bang Seojin S Zhang Pengfei P Lee Heewook H

Frontiers in immunology 20220706

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 ...[more]

PMID: 35874725

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