ArticleFrontiers in physiology2021
Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Systematic analysis of CDR contacts and pairing constraints between T cell receptor αβ chains.Bioinformatics (Oxford, England) · 2026Article
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- Determining T-cell receptor binding orientation and Peptide-HLA interactions using cross-linking mass spectrometry.The Journal of biological chemistry · 2025Article
- Limits on inferring T cell specificity from partial information.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?Nature methods · 2024Review
- Review
- An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator.PLoS computational biology · 2023Article
- A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.eLife · 2023Article
- Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities.Cell systems · 2023Article
- TCR sequencing: applications in immuno-oncology research.Immuno-oncology technology · 2023Review
- iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features.Frontiers in genetics · 2023Article
- Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes.Frontiers in physiology · 2021Article
- TCR-pMHC Binding Specificity Prediction From Structure Using Graph Neural Networks.IEEE transactions on computational biology and bioinformaticsArticle
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.
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