Multi-positive contrastive learning-based cross-attention model for T cell receptor-antigen binding prediction.

BACKGROUND AND OBJECTIVE

T cells play a vital role in the immune system by recognizing and eliminating infected or cancerous cells, thus driving adaptive immune responses. Their activation is triggered by the binding of T cell receptors (TCRs) to epitopes presented on Major Histocompatibility Complex (MHC) molecules. However, experimentally identifying antigens that could be recognizable by T cells and possess immunogenic properties is resource-intensive, with most candidates proving non-immunogenic, underscoring the need for computational tools to predict peptide-MHC (pMHC) and TCR binding. Despite extensive efforts, accurately predicting TCR-antigen binding pairs remains challenging due to the vast diversity of TCRs.

METHODS

In this study, we propose a Contrastive Cross-attention model for TCR (ConTCR) and pMHC binding prediction. Firstly, the pMHC and TCR sequences are transformed into high-level embedding by pretrained encoders as feature representations. Then, we employ the multi-modal cross-attention to combine the features between pMHC sequences and TCR sequences. Next, based on the contrastive learning strategy, we pretrained the backbone of ConTCR to boost the model's feature extraction ability for pMHC and TCR sequences. Finally, the model is fine-tuned for classification between positive and negative samples.

RESULTS

Based on this advanced strategy, our proposed model could effectively capture the critical information on TCR-pMHC interactions, and the model is visualized by the attention score heatmap for interpretability. ConTCR demonstrates strong generalization in predicting binding specificity for unseen epitopes and diverse TCR repertoires. On independent non-zero-shot test sets, the model achieved AUC-ROC scores of 0.849 and 0.950; on zero-shot test sets, it obtained AUC-ROC scores of 0.830 and 0.938.

CONCLUSION

Our framework offers a promising solution for improving pMHC-TCR binding prediction and model interpretability. By leveraging the ConTCR model and pMHC-TCR features, we achieve more precise precision than recently advanced models. Overall, ConTCR is a robust tool for predicting pMHC-TCR binding and holds significant promise to advance TCR-based immunotherapies as a valuable artificial intelligence tool. The codes and data used in this study are available at this website.