Epitope-conditioned T cell receptor generator that writes its own in-context examples, so receptors can be designed for targets with no known binders.
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Engineered T cell therapies need receptors that recognise a specific epitope, and the epitopes that matter most clinically — a newly emerged viral strain, a patient's private neoantigen — are exactly the ones with no catalogued cognate TCRs. A generative model must therefore produce binders for a target it has never seen, with no examples to imitate.
TCRGen, from Heewook Lee's group at Arizona State University with Seojin Bang at Google DeepMind, imports in-context learning from large language models and adapts it in two steps. First, in-context training (ICT): instead of fine-tuning on isolated epitope-TCR pairs, the model is trained on instances that bundle an epitope with several of its cognate receptors, so the prompt format it learns from matches the format it will be given at inference. Second, self-contemplation prompting (SCP): because a novel epitope has no known binders to supply as context, the model generates its own candidates from the epitope alone, scores them, and reuses the best as the in-context examples for a final generation pass.
The paper's central finding is that the two steps are inseparable. A conventionally fine-tuned model handed context receptors does not merely fail to benefit — its success rate collapses from 80% to 3%, because the prompt shape is unlike anything it saw in training. With training and inference distributions aligned, self-generated context works about as well as ground-truth context, and with scored selection it works better.
The generator is a fine-tune of RITA_m, a 300-million-parameter, 24-layer autoregressive protein language model pretrained on over 280 million sequences. Instances are serialised as an epitope, a delimiter, and one or more receptor sequences; vanilla fine-tuning used 96.7K unique TCR-epitope pairs, while in-context training grouped receptors sharing an epitope, with a context width of k = 4 for the main model and up to 10 in the longer-context variants. Evaluation rests on three metrics: BAP, a catELMo-based binding predictor that separates known binders from non-binders at AUC 0.97; TCRMatch, a k-mer similarity score against ground-truth receptors; and GPT-LL, a protein language model fine-tuned on four million healthy-repertoire receptors from ImmunoSeq whose log-likelihood scores authenticity at AUC 0.87.
Across 29 novel epitopes with 300 generated receptors each, the strongest configuration — in-context training plus scored self-contemplation prompting at 10 shots — reaches a 91.91% good-TCR rate under BAP, against 87.17% for the oracle that is handed real cognate receptors and 88.14% for zero-shot generation. Under the authenticity metric it reaches 99.17%, and under TCRMatch 99.77%. Gains saturate quickly: BAP performance plateaus after about six context receptors, and the similarity-based metrics after one.
The intended workflow is pre-screening for personalised immunotherapy. Given an epitope from a new pathogen or a tumour neoantigen, the model proposes receptor sequences and filters them to a short list whose members are both predicted to bind and statistically indistinguishable from natural repertoire sequences, shrinking what goes to wet-lab binding assays and T cell engineering. The recipe generalises beyond immunology wherever a generative model must extrapolate to targets absent from a small training set: train in the shape you will prompt in, and manufacture the context you lack. Catalogued epitope-conditioned generators such as TCRDiff approach the same design problem from a diffusion rather than a prompting angle.
TCRGen isolates a failure mode that is easy to miss — in-context learning is not a free capability of a pretrained language model, but something a fine-tune destroys when its training format diverges from its inference format — and shows that closing that gap is what makes self-generated context viable. That synthesised context matches or exceeds ground-truth context is the load-bearing claim, since it is what makes the method applicable to the novel-epitope case it was built for. The limits are acknowledged by the authors: every result is scored by computational proxies, no generated receptor has been expressed or assayed, and the binding predictor that ranks candidates also grades them. The repository publishes training code, generation and evaluation scripts, and the k-shot data splits under CC BY 4.0; pretrained weights are announced but not yet released. The work remains a preprint.
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