Phage-display profiling of CDR3ß loops enables machine learning predictions of NY-ESO-1 specific TCRs

T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. T-cell epitope recognition is mediated by the binding of the T-Cell Receptor (TCR) and TCRs recognizing clinically relevant epitopes are promising for T-cell based therapies. Starting from one of the few known TCRs targeting the cancer-testis antigen NY-ESO-1157–165 epitope, we built large phage-display libraries of TCRs with randomized Complementary Determining Region 3 of the ß chain. The TCR libraries were panned against the NY-ESO-1 epitope, which enabled us to collect 29,688 epitope-specific TCR sequences. We then trained a machine learning TCR-epitope interaction predictor with this data and could identify several epitope-specific TCRs directly from TCR repertoires. Cellular binding and activation assays revealed that the predicted TCRs displayed functionality towards the NY-ESO-1 epitope and no detectable cross-reactivity to self-peptides.