ArticleCell genomics2024
Predicting T cell receptor functionality against mutant epitopes.
Article in Cell genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Calibrating T cell responsiveness through interactions with self.Nature reviews. Immunology · 2026Review
- Central T cell tolerance from sparse peptide sampling.Science advances · 2026Article
- What Does the Shape Space of T Cell Epitopes Look Like?Immunological reviews · 2026Review
- Ensembles ofbioRxiv : the preprint server for biology · 2026Article
- The T Cell Receptor: Molecular Sensor, Therapeutic Mediator and Probabilistic Driver of Adaptive Immunity.Immunological reviews · 2026Review
- Deep peptide recognition profiling decodes TCR specificity and enables disease-associated antigen discovery.Nature biotechnology · 2026Article
- Advances in predicting T cell epitope recognition for cancer immunotherapy.Nature cancer · 2026Review
- T Cell Receptor Repertoires Across the Continuum of Vascular, Myocardial, and Age-Related Diseases.Immunological reviews · 2026Review
- Generation of T cells with reduced off-target cross-reactivities by engineering co-signalling receptors.Nature biomedical engineering · 2026Article
- TCRcloud: a global visualization tool for T-cell and B-cell receptor transcripts.Journal of translational medicine · 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
- T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database.Cell systems · 2025Article
- Benchmarking of T cell receptor-epitope predictors with ePytope-TCR.Cell genomics · 2025Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Comprehensive epitope mutational scan database enables accurate T cell receptor cross-reactivity prediction.bioRxiv : the preprint server for biology · 2025Article
- Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.Nucleic acids research · 2025Article
- Quantitative approaches for decoding the specificity of the human T cell repertoire.Frontiers in immunology · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer cells and pathogens can evade T cell receptors (TCRs) via mutations in immunogenic epitopes. TCR cross-reactivity (i.e., recognition of multiple epitopes with sequence similarities) can counteract such escape but may cause severe side effects in cell-based immunotherapies through targeting self-antigens. To predict the effect of epitope point mutations on T cell functionality, we here present the random forest-based model Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM). P-TEAM was trained and tested on three datasets with TCR responses to single-amino-acid mutations of the model epitope SIINFEKL, the tumor neo-epitope VPSVWRSSL, and the human cytomegalovirus antigen NLVPMVATV, totaling 9,690 unique TCR-epitope interactions. P-TEAM was able to accurately classify T cell reactivities and quantitatively predict T cell functionalities for unobserved single-point mutations and unseen TCRs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.
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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.