A lightweight TcrLM model predicts T cell receptor and epitope binding specificity.

Immune responses depend on specific interactions between T cell receptors (TCRs) and peptides presented by antigen-presenting cells (APCs). The vast diversity of the TCR repertoire makes accurate prediction of TCR-antigen binding specificity highly challenging. Here, we present a lightweight, masked language model, tcrLM, to address this problem. We pretrain tcrLM on a large-scale TCR CDR3 sequence dataset and use the pretrained encoder to extract informative features for pTCR binding prediction. tcrLM achieves competitive performance on hold-out and external test sets and shows comparatively robust zero-shot generalization relative to several baselines on a large, unseen-COVID-19 peptide set. The model effectively captures biochemical properties and positional preferences of amino acids within TCR sequences. In an exploratory melanoma cohort, the predicted TCR-neoantigen binding scores correlate with immunotherapy response and clinical outcomes. These results highlight the potential of tcrLM for advancing immunotherapy and personalized medicine.