VDJdb in 2026: boosting T-cell receptor recognition evidence using paratope embeddings and AI-based structure prediction

Luppov, Daniil V, Koneva, Anna E, Bagaev, Dmitry V, Alexandrova, Anastasiia V, Vlasova, Elizaveta K, Chudakov, Dmitry M, Motozono, Chihiro, Sewell, Andrew K ORCID: https://orcid.org/0000-0003-3194-3135 and Shugay, Mikhail 2026. VDJdb in 2026: boosting T-cell receptor recognition evidence using paratope embeddings and AI-based structure prediction. Nucleic Acids Research 10.1093/nar/gkag904

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Abstract

We present a significant update to VDJdb, introducing substantial enhancements to both the data content and the technical infrastructure. The integration of steadily accumulating T-cell receptor (TCR):epitope recognition data, together with advances in high-throughput experimental techniques, has expanded the landscape for machine learning-based prediction of TCR specificity, covering foreign antigens, neoantigens, and autoimmunity-associated epitopes. However, these advances have introduced new challenges, most notably in data quality—large-scale assays have heightened concerns about measurement reliability and exacerbated existing issues such as HLA and epitope coverage biases. To address these issues, we have adopted state-of-the-art artificial intelligence approaches, including protein sequence embeddings and AI-driven structure prediction. The updated VDJdb resource now features TCR specificity records annotated with embedding-derived paratope features, advanced noise filtering capabilities, and predicted three-dimensional protein structures. These improvements enable more comprehensive interrogation of TCR–epitope recognition and provide novel lines of evidence to support the reliability of high-throughput assay records, which are frequently limited by insufficient independent validation. VDJdb can be accessed at https://vdjdb.com and https://github.com/antigenomics/vdjdb-db.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Medicine
Publisher:	Oxford University Press
ISSN:	0305-1048
Date of First Compliant Deposit:	29 September 2026
Date of Acceptance:	21 August 2026
Last Modified:	29 Sep 2026 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/189869

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