ArticleFrontiers in immunology2025
NetTCR-struc, a structure driven approach for prediction of TCR-pMHC interactions.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- MHCXGraph: a graph-based approach to detecting T-cell receptor cross-reactivity.Briefings in bioinformatics · 2026Article
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Structure-based TCR-pMHC binding prediction and generalization to unseen peptides.npj drug discovery · 2026Article
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- Mapping the TCR landscape: computational tools empowering translational immunology and therapy design.Journal for immunotherapy of cancer · 2026Review
- Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026Article
- Advances in predicting T cell epitope recognition for cancer immunotherapy.Nature cancer · 2026Review
- Structural quality-tier assessment for TCR-pMHC functional enrichment.Frontiers in immunology · 2026Article
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- STAG-LLM: Predicting TCR-pHLA binding with protein language models and computationally generated 3D structures.Computational and structural biotechnology journal · 2025Article
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Authors and funding
2 authors.
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Abstract
Accurate modeling of T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions is critical for understanding immune recognition. In this study, we present advances in structural modeling of TCR-pMHC class I complexes focusing on improving docking quality scoring and structural model selection using graph neural networks (GNN). We find that AlphaFold-Multimer's confidence score in certain cases correlates poorly with DockQ quality scores, leading to overestimation of model accuracy. Our proposed GNN solution achieves a 25% increase in Spearman's correlation between predicted quality and DockQ (from 0.681 to 0.855) and improves docking candidate ranking. Additionally, the GNN completely avoids selection of failed structures. Additionally, we assess the ability of our models to distinguish binding from non-binding TCR-pMHC interactions based on their predicted quality. Here, we demonstrate that our proposed model, particularly for high-quality structural models, is capable of discriminating between binding and non-binding complexes in a zero-shot setting. However, our findings also underlined that the structural pipeline struggled to generate sufficiently accurate TCR-pMHC models for reliable binding classification, highlighting the need for further improvements in modeling accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.