Abstract
AbstractThe adaptive immune response to foreign antigens is initiated by T-cell receptor (TCR) recognition on the antigens. Recent experimental advances have enabled the generation of a large amount of TCR data and their cognate antigenic targets, allowing machine learning models to predict the binding specificity of TCRs. In this work, we present TEINet, a deep learning framework that utilizes transfer learning to address this prediction problem. TEINet employs two separately trained encoders to transform TCR and epitope sequences into numerical vectors, which are subsequently fed into a fully connected neural network to predict their binding specificities. A major challenge for binding specificity prediction is the lack of a unified approach to sample negative data. Here, we first assess the current negative sampling approaches comprehensively and suggest that theUnified Epitopeis the most suitable one. Subsequently, we compare TEINet with three baseline methods and observe that TEINet achieves an AUROC of 0.760, which outperforms baseline methods by 6.4-26%. Furthermore, we investigate the impacts of the pretraining step and notice that excessive pretraining can adversely affect model performance. Our results and analysis show that TEINet can make an accurate prediction using only the TCR sequence (CDR3β) and the epitope sequence, providing novel insights to understand the interactions between TCRs and epitopes. TEINet is available athttps://github.com/jiangdada1221/TEINet.
Publisher
Cold Spring Harbor Laboratory
Reference37 articles.
1. Estimating t-cell repertoire diversity: Limitations of classical estimators and a new approach;Philosophical Transactions of the Royal Society B: Biological Sciences,2015
2. High-throughput determination of the antigen specificities of t cell receptors in single cells;Nature biotechnology,2018
3. Phenotypic Analysis of Antigen-Specific T Lymphocytes
4. T-Scan: A Genome-wide Method for the Systematic Discovery of T Cell Epitopes
5. Vdjdb: A curated database of t-cell receptor sequences with known antigen specificity;Nucleic acids research,2018
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