A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules
<|reference_start|>A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules: The immune checkpoint inhibitors have demonstrated promising clinical efficacy across various tumor types, yet the percentage of patients who benefit from them remains low. The binding affinity between antigens and HLA-I/TCR molecules plays a critical role in antigen presentation and T-cell activation. Some computational methods have been developed to predict antigen-HLA or antigen-TCR binding specificity, but they focus solely on one task at a time. In this paper, we propose UnifyImmun, a unified cross-attention transformer model designed to simultaneously predicts the binding of antigens to both HLA and TCR molecules, thereby providing more comprehensive evaluation of antigen immunogenicity. We devise a two-phase progressive training strategy that enables these two tasks to mutually reinforce each other, by compelling the encoders to extract more expressive features. To further enhance the model generalizability, we incorporate virtual adversarial training. Compared to over ten existing methods for predicting antigen-HLA and antigen-TCR binding, our method demonstrates better performance in both tasks. Notably, on a large-scale COVID-19 antigen-TCR binding test set, our method improves performance by at least 9% compared to the current state-of-the-art methods. The validation experiments on three clinical cohorts confirm that our approach effectively predicts immunotherapy response and clinical outcomes. Furthermore, the cross-attention scores reveal the amino acids sites critical for antigen binding to receptors. In essence, our approach marks a significant step towards comprehensive evaluation of antigen immunogenicity.<|reference_end|>
@article{yu2024a,
    title={A unified cross-attention model for predicting antigen binding
  specificity to both HLA and TCR molecules},
    author={Chenpeng Yu, Xing Fang, Hui Liu},
    journal={arXiv preprint arXiv:2405.06653},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06653},
    primaryClass={q-bio.BM cs.LG}
}