The human immune response depends on the binding of T-cell receptors (TCRs)
to antigens (pTCR), which elicits the T cells to eliminate viruses, tumor
cells, and other pathogens. The ability of human immunity system responding to
unknown viruses and bacteria stems from the TCR diversity. However, this vast
diversity poses challenges on the TCR-antigen binding prediction methods. In
this study, we propose a Masked Language Model (MLM), referred to as tcrLM, to
overcome limitations in model generalization. Specifically, we randomly masked
sequence segments and train tcrLM to infer the masked segment, thereby extract
expressive feature from TCR sequences. Meanwhile, we introduced virtual
adversarial training techniques to enhance the model's robustness. We built the
largest TCR CDR3 sequence dataset to date (comprising 2,277,773,840 residuals),
and pre-trained tcrLM on this dataset. Our extensive experimental results
demonstrate that tcrLM achieved AUC values of 0.937 and 0.933 on independent
test sets and external validation sets, respectively, which remarkably
outperformed four previously published prediction methods. On a large-scale
COVID-19 pTCR binding test set, our method outperforms the current
state-of-the-art method by at least 8%, highlighting the generalizability of
our method. Furthermore, we validated that our approach effectively predicts
immunotherapy response and clinical outcomes on a clinical cohorts. These
findings clearly indicate that tcrLM exhibits significant potential in
predicting antigenic immunogenicity.

我们提出了一种蒙版语言模型（tcrLM），用于预测 T 细胞受体（TCR）与抗原的结合，通过训练 tcrLM 来推断被屏蔽的片段，从 TCR 序列中提取表达特征，并在大规模 COVID-19 pTCR 结合测试集上超过当前最先进的方法，有效预测免疫疗法反应和临床结果。