ArticleImmunogenetics2018
On the feasibility of mining CD8+ T cell receptor patterns underlying immunogenic peptide recognition.
Article in Immunogenetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Mitigating negative data bias to enhance TCR-epitope binding and residue interaction prediction.Briefings in bioinformatics · 2026Article
- LANTERN: TCR-peptide binding predictionPeerJ · 2026Article
- Machine Learning Approaches for Interpretable Antibody Property Prediction Using Structural Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- LightCTL: lightweight contrastive TCR-pMHC specificity learning with context-aware prompt.Briefings in bioinformatics · 2025Article
- The Evolving T Cell Receptor Recognition Code: The Rules Are More Like Guidelines.Immunological reviews · 2025Review
- [Prediction of MHC II antigen peptide-T cell receptors binding based on foundation model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Predicting T cell receptor functionality against mutant epitopes.Cell genomics · 2024Article
- HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.Communications biology · 2024Article
- Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.Nature communications · 2024Article
- Review
- In Silico Tools for Predicting Novel Epitopes.Methods in molecular biology (Clifton, N.J.) · 2024Article
- TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.Protein science : a publication of the Protein Society · 2024Article
- Neoantigen-targeted TCR-engineered T cell immunotherapy: current advances and challenges.Biomarker research · 2023Review
- A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.eLife · 2023Article
- Dynamic kernel matching for non-conforming data: A case study of T cell receptor datasets.PloS one · 2023Article
- Attentive Variational Information Bottleneck for TCR-peptide interaction prediction.Bioinformatics (Oxford, England) · 2023Article
- Antigen-specificity measurements are the key to understanding T cell responses.Frontiers in immunology · 2023Review
- Leveraging T-cell receptor - epitope recognition models to disentangle unique and cross-reactive T-cell response to SARS-CoV-2 during COVID-19 progression/resolution.Frontiers in immunology · 2023Article
- DECODE: a computational pipeline to discover T cell receptor binding rules.Bioinformatics (Oxford, England) · 2022Article
- Preexisting memory CD4 T cells in naïve individuals confer robust immunity upon hepatitis B vaccination.eLife · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Current T cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T cell receptor (TCR) is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T cell receptor and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of TCRs that each bind to a known peptide and (2) retrieving TCRs that bind to a given peptide from a large pool of TCRs. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particular importance as they show that prediction of T cell epitope and T cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T cell epitopes but also paves the way for more general and high-performing models.
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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.