ArticleProtein science : a publication of the Protein Society2024
TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.
Article in Protein science : a publication of the Protein Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- MHCXGraph: a graph-based approach to detecting T-cell receptor cross-reactivity.Briefings in bioinformatics · 2026Article
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- A lightweight TcrLM model predicts T cell receptor and epitope binding specificity.Cell reports methods · 2026Article
- Single-Cell Multi-Tissue T Cell Clonal Dynamics Reveal Distinct Immune Coercion Landscapes in MSI and MSS Colorectal Cancer.International journal of molecular sciences · 2026Article
- Article
- Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding.ArXiv · 2026Article
- Decoding TCR recognition via geometric deep learning of immunological fingerprints.Briefings in bioinformatics · 2026Article
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- ProTCR: a protein language model-driven framework for decoding TCR-antigen recognition toward precision immunotherapies.Briefings in bioinformatics · 2026Article
- 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
- 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
- Disrupting explicit encoding paradigms: property-interactive transformers decode T-cell receptor specificity beyond dataset biases.Briefings in bioinformatics · 2025Article
- Article
- Neoantigen-driven personalized tumor therapy: An update from discovery to clinical application.Chinese medical journal · 2025Review
- SageTCR: a structure-based model integrating residue- and atom-level representations for enhanced TCR-pMHC binding prediction.Briefings in bioinformatics · 2025Article
- A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight.Briefings in bioinformatics · 2025Review
- A free energy perturbation-assisted machine learning strategy for mimotope screening in neoantigen-based vaccine design.Briefings in bioinformatics · 2025Article
- T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity.Communications biology · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
The recognition of T-cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to trigger the immune response. Identifying the binding rules of TCR-epitope pair is crucial for developing immunotherapies, including neoantigen vaccine and drugs. Accurate prediction of TCR-epitope binding specificity via deep learning remains challenging, especially in test cases which are unseen in the training set. Here, we propose TEPCAM (TCR-EPitope identification based on Cross-Attention and Multi-channel convolution), a deep learning model that incorporates self-attention, cross-attention mechanism, and multi-channel convolution to improve the generalizability and enhance the model interpretability. Experimental results demonstrate that our model outperformed several state-of-the-art models on two challenging tasks including a strictly split dataset and an external dataset. Furthermore, the model can learn some interaction patterns between TCR and epitope by extracting the interpretable matrix from cross-attention layer and mapping them to the three-dimensional structures. The source code and data are freely available at https://github.com/Chenjw99/TEPCAM.
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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.