LoFT-TCR: A LoRA-based Fine-tuning Framework for TCR-Antigen Binding Prediction

T cells recognize and eliminate diseased cells by binding their T cell receptors (TCRs) to short endogenous peptides (antigens) presented on the cell surface. Such interactions are central to adaptive immunity, yet current experimental approaches to identify TCR-antigen binding pairs remain labor-intensive and constrained by limited reagents. Here, we propose LoFT-TCR, a low-rank adaptation (LoRA)-based fine-tuning framework designed for TCR-antigen binding prediction. To capture precise and informative sequence representations, we first fine-tuned the protein large language model ESM-2 on antigen-specific TCR datasets using LoRA. Subsequently, we constructed a heterogeneous interaction graph where nodes encode sequence features and edges indicate interaction relationships. By leveraging a graph learning framework, LoFT-TCR effectively integrates sequence and topological information to enhance prediction capability. Systematic experiments validated that fine-tuning ESM-2 significantly enhanced the model’s capability to extract discriminative sequence representations, which are critical for accurate TCR specificity prediction. Moreover, LoFT-TCR consistently achieved superior performance compared to state-of-the-art methods on both TCR-antigen binding prediction and TCR specificity discrimination tasks. Experimental results demonstrate that LoFT-TCR achieves substantial improvements in predictive accuracy and holds potential for advancing personalized T cell-based immunotherapy.