ArticlePLoS computational biology2021
Predicting recognition between T cell receptors and epitopes with TCRGP.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 1 of them a synthesis that pooled it.
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Who cites it
83 citing papers in PubMed, 1 synthesis or guideline pooled it, 141 citations in OpenAlex.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models.Cell systems · 2026Article
- Single-cell analysis of adaptive NK and CMV-specific T-cell dynamics after allogeneic HSCT.Blood advances · 2026Article
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Identifying expanding TCR clonotypes with a longitudinal Bayesian mixture model and their associations with cancer patient prognosis, metastasis-directed therapy, and VJ gene enrichment.Bioinformatics (Oxford, England) · 2026Article
- When multimodal fusion fails: contrastive alignment as a necessary stabilizer for TCR-peptide binding prediction.Bioinformatics (Oxford, England) · 2026Article
- DynaTCR: dynamic hard-negative ensemble graph learning improves TCR-epitope binding prediction.Bioinformatics (Oxford, England) · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- Deciphering small sequence differences in T cell receptor-antigen pairing.Nature communications · 2026Article
- A lightweight TcrLM model predicts T cell receptor and epitope binding specificity.Cell reports methods · 2026Article
- Deep peptide recognition profiling decodes TCR specificity and enables disease-associated antigen discovery.Nature biotechnology · 2026Article
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- A Biologically Informed Vision-Guided Framework for Interpretable T Cell Receptor-Epitope Binding Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Assessment of computational methods in predicting TCR-epitope binding recognition.Nature methods · 2026Article
- Predicting antigen-specific T-cell immunity against Wilms tumor 1 in hematologic cancer.Leukemia · 2025Article
- THLANet: A deep learning framework for predicting TCR-pHLA binding in immunotherapy applications.PLoS computational biology · 2025Article
- GRAPE: graph-regularized protein language modeling unlocks TCR-epitope binding specificity.Briefings in bioinformatics · 2025Article
- T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database.Cell systems · 2025Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adaptive immune system uses T cell receptors (TCRs) to recognize pathogens and to consequently initiate immune responses. TCRs can be sequenced from individuals and methods analyzing the specificity of the TCRs can help us better understand individuals' immune status in different disorders. For this task, we have developed TCRGP, a novel Gaussian process method that predicts if TCRs recognize specified epitopes. TCRGP can utilize the amino acid sequences of the complementarity determining regions (CDRs) from TCRα and TCRβ chains and learn which CDRs are important in recognizing different epitopes. Our comprehensive evaluation with epitope-specific TCR sequencing data shows that TCRGP achieves on average higher prediction accuracy in terms of AUROC score than existing state-of-the-art methods in epitope-specificity predictions. We also propose a novel analysis approach for combined single-cell RNA and TCRαβ (scRNA+TCRαβ) sequencing data by quantifying epitope-specific TCRs with TCRGP and identify HBV-epitope specific T cells and their transcriptomic states in hepatocellular carcinoma patients.
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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.