ArticleCell systems2026
Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models.
Article in Cell systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Structure-based TCR-pMHC binding prediction and generalization to unseen peptides.npj drug discovery · 2026Article
- Highlight of 2025: From binding prediction to molecular design-computational advances in TCR-pMHC prediction and targeting.Immunology and cell biology · 2026Review
- A cell-based kinetic framework enables TCR specificity prediction.Signal transduction and targeted therapy · 2026Article
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- Transfer learning for T-cell response prediction.BMC bioinformatics · 2026Article
- Assessment of computational methods in predicting TCR-epitope binding recognition.Nature methods · 2026Article
- Structural quality-tier assessment for TCR-pMHC functional enrichment.Frontiers in immunology · 2026Article
- Solution mapping of MHC-I:TCR interactions using a minimalistic protein system.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A functionally validated TCR-pMHC database for TCR specificity model development.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Understanding T cell receptor (TCR) discrimination of MHC-presented epitope peptides (pMHCs) remains challenging. While machine-learning (ML)-based predictions of TCR specificity have gained attention, their capacity to generalize to unseen peptides is often misinterpreted. Using a proprietary cancer patient dataset, we show that ML methods succeed in predicting TCR specificity for known peptides but fail to generalize to novel peptides. Conversely, physics-based methods outperform ML methods on novel peptides but underperform on known peptides. In light of these observations, we develop a new ML method that leverages protein foundation models to achieve better or comparable performance than existing ML and biophysical methods on both in- and out-of-distribution TCR-pMHC specificity prediction. We furthermore characterize method performance as a function of distance of TCR sequence specificity between training and test sets. Our analysis elucidates the current limitations of modeling TCR-pMHC interactions and outlines new avenues for method development and data acquisition.
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Registered trials
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