Authors
Daniil V Luppov, Anna E Koneva, Dmitry V Bagaev, Anastasiia V Alexandrova, Elizaveta K Vlasova, Dmitry M Chudakov, Chihiro Motozono, Andrew K Sewell, Mikhail Shugay
Published in
Nucleic acids research. Sep 23, 2026. Epub Sep 23, 2026.
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.
PMID:
42773777
Bibliographic data and abstract were imported from PubMed on 23 Sep 2026.
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