ArticleCommunications biology2024
HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- DUET: a graph-based workflow for TCR-epitope prioritization and tumor-reactive T-cell identification.Briefings in bioinformatics · 2026Article
- Semantic-enhanced heterogeneous graph learning for identifying ncRNAs associated with drug resistance.Bioinformatics (Oxford, England) · 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
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development.NPJ vaccines · 2025Review
- T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database.Cell systems · 2025Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- ToxiPep: Peptide toxicity prediction via fusion of context-aware representation and atomic-level graph.Computational and structural biotechnology journal · 2025Article
- Targeting cancer with precision: strategical insights into TCR-engineered T cell therapies.Theranostics · 2025Review
- 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
3 authors.
Funding
Abstract
Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.
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Registered trials
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