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

Clicks: 7
ID: 329552
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #1,164 of 1,229 articles by views in Nucleic Acids Research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,229 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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.
Reference Key
openalex_W7214067842 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Daniil V. Luppov, Anna E. Koneva, Dmitry V. Bagaev, Anastasiia V Alexandrova, Elizaveta K Vlasova, Dmitriy M. Chudakov, Chihiro Motozono, Andrew Kelvin Sewell, Mikhail Shugay
Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag904
URL
https://doi.org/10.1093/nar/gkag904
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.