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Published on: January 26, 2024
ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model
Michael Cai1,2, Seojin Bang2, Pengfei Zhang1,2
1School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States.
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.
Insights
Predicting T cell receptor (TCR) and epitope binding is crucial for T cell regulation and immunotherapy. Our ATM-TCR model enhances prediction accuracy for new epitopes using a multi-head self-attention mechanism.
Area of Science:
- Immunology
- Computational Biology
- Bioinformatics
Background:
- T cell receptor (TCR)-epitope binding is central to T cell regulation and immunotherapy development.
- Current computational methods for predicting TCR-epitope binding struggle with limited epitope data, leading to poor generalization.
- Accurate prediction is essential for understanding T cell interactions and advancing therapeutic strategies.
Purpose of the Study:
- To develop a novel computational model, ATM-TCR, for predicting TCR-epitope binding.
- To improve the generalization performance of TCR-epitope binding prediction models, especially for unseen epitopes.
- To leverage attention mechanisms for enhanced biological contextual understanding and improved predictive power.
Main Methods:
- Utilized a multi-head self-attention mechanism within the ATM-TCR model.
- Captured biological contextual information from TCR and epitope sequence data.
- Applied attention maps for improved out-of-sample performance analysis.
Main Results:
- ATM-TCR demonstrated improved generalization performance compared to existing methods.
- The model effectively captured biological contextual information relevant to TCR-epitope binding.
- Demonstrated successful application on SARS-CoV-2 data, highlighting potential for real-world impact.
Conclusions:
- The ATM-TCR model offers a promising approach for accurate TCR-epitope binding prediction.
- The multi-head self-attention mechanism enhances model generalization, addressing limitations of current methods.
- Novel application of attention maps provides insights and improves prediction for novel epitopes, particularly relevant for infectious diseases like COVID-19.
