ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model.
Front Immunol
; 13: 893247, 2022.
Article
in English
| MEDLINE | ID: covidwho-1957158
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Receptors, Antigen, T-Cell
/
Epitopes, T-Lymphocyte
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Front Immunol
Year:
2022
Document Type:
Article
Affiliation country:
Fimmu.2022.893247
Similar
MEDLINE
...
LILACS
LIS