OmniTCR: a foundation model unifying T cell receptor recognition prediction and conditional sequence generation

Authors

Zeng, F., Feng, D., Song, D., Ding, L., Tan, Z., Lei, Q., Lei, W., Guo, A.-Y.

Abstract

T cell receptor (TCR) recognition prediction and receptor generation are traditionally modelled separately, leaving vast TCR sequence collections disconnected from smaller TCR-peptide-MHC datasets. Here we present OmniTCR, a 113-million-parameter autoregressive foundation model pretrained on 328 million formatted human immune-sequence records. Sequence-type tokens and complementary component orders enable joint learning from individual TCR chains and partial or complete TCR-pMHC associations. On unseen epitopes, OmniTCR achieved AUPRCs of 0.7009 for peptide-TCR{beta}; recognition and 0.8235 for TCR-pMHC interaction prediction, exceeding the strongest evaluated comparators by 0.3396 and 0.3451, respectively. It distinguishes cancer from healthy repertoires across 11 independent pan-cancer cohorts (mean AUROC, 0.9436). The model achieved the highest sequence recovery on internal and external generation benchmarks. Structural modelling supported the plausibility of selected pMHC-conditioned CDR3{beta}; candidates. OmniTCR bridges heterogeneous immune sequence data, providing a foundation for computational immunology and receptor design.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 14 Sep 2026.

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