ArticleIEEE transactions on computational biology and bioinformatics
TCR-pMHC Binding Specificity Prediction From Structure Using Graph Neural Networks.
Article in IEEE transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- 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
- Mapping the TCR landscape: computational tools empowering translational immunology and therapy design.Journal for immunotherapy of cancer · 2026Review
- General prediction of T cell receptor antigen specificity from sequence using AlphaFold 3.bioRxiv : the preprint server for biology · 2026Article
- A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions.bioRxiv : the preprint server for biology · 2026Article
- Decoding adaptive immunity: advanced strategies in T and B cell repertoire analysis.Journal of translational medicine · 2026Review
- TCRLens: structure-aware equivariant graph learning for TCR-pMHC-I recognition and immunogenic epitope discovery.Bioinformatics advances · 2026Article
- Structural quality-tier assessment for TCR-pMHC functional enrichment.Frontiers in immunology · 2026Article
- STAG-LLM: Predicting TCR-pHLA binding with protein language models and computationally generated 3D structures.Computational and structural biotechnology journal · 2025Article
- NetTCR-struc, a structure driven approach for prediction of TCR-pMHC interactions.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The mapping of T-cell-receptors (TCRs) to their cognate peptides is crucial to improving cancer immunotherapy. Numerous computational methods and machine learning tools have been proposed to aid in the task. Yet, accurately constructing this map computationally remains a difficult problem. Most prior work has sought to predict TCR-peptide-MHC (TCR-pMHC) binding specificity by analyzing the amino acid sequences of the TCRs and peptides. However, recent advancements in crystallography, cryo-EM, and in silico protein modeling have provided researchers with the necessary data to analyze the 3D structures of TCRs, peptides, and MHCs. Current research suggests that information contained in the 3D structure of the TCRs and pMHCs can explain instances of TCR specificity that are not explained by sequence alone. As protein structure data continues to become more accurate and easier to obtain, structure-based methodologies for predicting TCR-pMHC binding will become increasingly important. We present STAG, a novel graph-based machine learning architecture for predicting TCR-pMHC binding specificity using 3D structure data. We show that STAG achieves comparable or better performance than existing methods while utilizing only spatial and physicochemical features from modeled protein structures.
Identifiers
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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.