Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities
Wenzhen Li, Yu Pan, Xu Wu, Yuting Song, Yin Wang, Lu Xie · 2026
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Description
Wenzhen Li and five colleagues review in 2026 how computers can predict T-cell receptor cross-reactivity. That term means one immune receptor can recognize several different targets. The trait helps the immune system cover many threats. It also risks harmful off-target reactions when receptors are built into cancer therapies.
The review surveys the data banks open for modeling. It then compares methods based on sequence, on structure, on machine learning, and on mixed inputs. Each approach is weighed for how well it explains itself, generalizes, and scales. Known weak points include few confirmed cross-reactive examples and bias toward viral targets. Trustworthy negative cases are also scarce. The authors call for dedicated datasets and stricter testing before predictions guide treatment design.
Catalog record
| CREATOR | Wenzhen Li, Yu Pan, Xu Wu, Yuting Song, Yin Wang, Lu Xie |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 63713 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |