Predicting TCR sequences for unseen antigen epitopes using structural and sequence features

Abstract

Abstract T-cell receptor (TCR) recognition of antigens is fundamental to the adaptive immune response. With the expansion of experimental techniques, a substantial database of matched TCR-antigen pairs has emerged, presenting opportunities for computational prediction models. However, the accurate forecasting of binding affinities for unseen antigen-TCR pairs remains a major challenge. Here, we present Convolutional-Self-Attention TCR (CATCR), a novel framework tailored to enhance the prediction of epitope and TCR interactions. Our approach integrates an encoder that concurrently processes structural and sequential data, utilizing convolutional neural networks (CNNs) to extract peptide features from residue contact matrices, as generated by OpenFold, and a Transformer to encode segment-based coded sequence. We further introduce CATCR-D, a discriminator equipped to assess binding by analyzing structural and sequence features of epitopes and CDR3-β regions. Additionally, the framework comprises CATCR-G, a generative module designed for CDR3-β sequences, which applies the pretrained encoder to deduce epitope characteristics and a Transformer decoder for predicting matching CDR3-β sequences. CATCR-D has shown exemplary feature extraction and generalization, achieving an AUROC of 0.89 on previously unseen epitope-TCR pairs and outperforming four benchmark models by a margin of 17.4%. CATCR-G has demonstrated high precision, recall, and F1 scores, surpassing 95% in BERT-score assessments. Our results indicate that CATCR is an effective tool for the prediction of unseen epitope-TCR interactions, and that incorporating structural insights significantly enhances our understanding of the general rules governing TCR-epitope recognition. The prediction of TCRs for novel epitopes using structural and sequence information is promising, and broadening the repository of experimental TCR-epitope data stands to further improve the precision of epitope-TCR binding predictions.