ArticleNature communications2026
Deciphering small sequence differences in T cell receptor-antigen pairing.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
38 authors.
Funding
Abstract
T cells have important functions in development and disease processes through T cell receptor (TCR)-dependent activities. Many tools were developed to predict the binding between TCRs and antigens. However, one of the uncertainties is whether such tools can decipher how small changes in the TCRs or antigenic peptides contribute to binding. We develop a deep learning model, pMTnet-omni, which not only predicts the binding vs. non-binding of TCRs towards pMHCs, but also distinguishes the stronger vs. weaker binding of TCRs similar in sequence. We leverage this capability to interpret the biological rules that govern TCR-antigen pairing. This also enables pMTnet-omni to accurately predict variant TCRs with desired stronger or weaker binding to the antigen, in conjunction with a Lab-in-the-Loop (LiL) mechanism. We show that pMTnet-omni can also predict binding of TCRs towards similar pMHCs. Overall, we provide a flexible toolkit for research and translational applications involving antigens and TCRs.
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Registered trials
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