Conditional generation of real antigen-specific T cell receptor sequences by Dhuvarakesh Karthikeyan & Sarah N. Bennett & Amy G. Reynolds & Benjamin G. Vincent & Alex Rubinsteyn instant download

Conditional generation of real antigen-specific T cell receptor sequences by Dhuvarakesh Karthikeyan & Sarah N. Bennett & Amy G. Reynolds & Benjamin G. Vincent & Alex Rubinsteyn instant download

Despite recent advances in T cell receptor (TCR) engineering, designing functional TCRs against arbitrary targets remains challenging due to complex rules governing cross-reactivity and limited paired data. Here we present TCR-TRANSLATE, a sequence-to-sequence framework that adapts low-resource machine translation techniques to generate antigen-specifc TCR sequences against unseen epitopes. By evaluating 12 model variants of the BART and T5 model architectures, we identifed key factors afecting performance and utility, revealing discordances between these objectives. Our fagship model, TCRT5, outperforms existing approaches on computational benchmarks, prioritizing functionally relevant sequences at higher ranks. Most signifcantly, we experimentally validated a computationally designed TCR against Wilms’ tumour antigen, a therapeutically relevant target in leukaemia, excluded from our training and validation sets. Although the identifed TCR shows cross-reactivity with pathogen-derived peptides, highlighting limitations in specifcity, our work represents the successful computational design of a functional TCR construct against a non-viral epitope from the target sequence alone. Our fndings establish a foundation for computational TCR design and reveal current limitations in data availability and methodology, providing a framework for accelerating personalized immunotherapy by reducing the search space for novel targets.