TridentTCR: Predicting T Cell Receptor Specificity for Antigens and Autoimmune-Related Targets via a Topology-Aware Graph and Large Language Model

T cells recognize and eliminate diseased cells by binding their T cell receptors (TCRs) to short endogenous peptides presented on the cell surface, commonly referred to as antigens. Accurate prediction of antigen–TCR specificity is essential for advancing T cell-based immunotherapies while minimizing autoimmune toxicity. However, current computational methods struggle to generalize across diverse TCR repertoires and reliably differentiate therapeutic antigens from autoimmune-related antigens (arAgs). Here, we introduce TridentTCR, a computational framework integrating a pretrained large language model (LLM) for high-fidelity sequence embedding with a topology-aware graph neural network to capture structural insights from the antigen–TCR interaction network. TridentTCR significantly advances TCR specificity prediction by enabling robust trinary classification, distinguishing TCR interactions involving general antigens, arAgs, and non-binding. Additionally, TridentTCR outperforms existing state-of-the-art models in binary classification (binding vs. non-binding). Validation on independent clinical datasets demonstrated strong generalization capability for unseen TCRs and antigens. In a disease-specific context, TridentTCR identifies notable cross-reactivity between ZnT8 _186-194 -specific TCRs and a mimotope from Bacteroides stercoris , suggesting that T cell cross-reactivity may contribute to the initiation of type 1 diabetes. Furthermore, we introduced a quantitative metric, antigenic immune response entropy (AIRE), which leverages TridentTCR predictions alongside clonotype frequencies and repertoire diversity to precisely quantify antigen-specific immune responses from single-cell profiling data. Collectively, TridentTCR provides an interpretable and clinically relevant tool, enabling improved understanding of TCR specificity, cross-reactivity, and off-target autoimmune risks in clinical immunotherapy.