A large language model for predicting T cell receptor-antigen binding specificity

• URL: http://arxiv.org/abs/2406.16995v1
• Date: Mon, 24 Jun 2024 08:36:40 GMT
• Title: A large language model for predicting T cell receptor-antigen binding specificity
• Authors: Xing Fang, Chenpeng Yu, Shiye Tian, Hui Liu,
• Abstract summary: We propose a Masked Language Model (MLM) 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. Our extensive experimental results demonstrate that tcrLM achieved AUC values of 0.937 and 0.933 on independent test sets and external validation sets.
• Score: 4.120928123714289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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