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.

我们提出了一种名为 UnifyImmun 的统一交互注意力变换器模型，它同时预测抗原与 HLA 和 TCR 分子的结合，从而更全面评估抗原免疫原性，并且我们的方法比现有方法在两个任务上表现更好。