ArticleNature communications2024
Designing meaningful continuous representations of T cell receptor sequences with deep generative models.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- TransTCR: Integrating TCRs and Transcriptomes Through Optimal Transport for Antigen Specificity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- The Role of the T Cell Receptor Sequence in Shaping T Cell Functional Fate.Immunological reviews · 2026Review
- A cell-based kinetic framework enables TCR specificity prediction.Signal transduction and targeted therapy · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- NeoTCRseek: an integrated platform for high-sensitivity identification of neoantigen-specific TCR clonotypes to track clinical T-cell dynamics.Frontiers in immunology · 2026Article
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
- TCRdesign: an antigen-specific generative language model for de novo design of T-cell receptors.Briefings in bioinformatics · 2025Article
- TCR-epiDiff: solving dual challenges of TCR generation and binding prediction.Bioinformatics (Oxford, England) · 2025Article
- TCRCluster: a novel approach to T-cell receptor latent featurization and clustering using contrastive learning-guided two-stage variational autoencoders.NAR genomics and bioinformatics · 2025Article
- High-fidelity in silico generation and augmentation of TCR repertoire data using generative adversarial networks.Scientific reports · 2025Article
- Role of artificial intelligence in advancing immunology.Immunologic research · 2025Review
- Temporal TCR dynamics and epitope diversity mark recovery in severe COVID-19 patients.Frontiers in immunology · 2025Article
- Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
T Cell Receptor (TCR) antigen binding underlies a key mechanism of the adaptive immune response yet the vast diversity of TCRs and the complexity of protein interactions limits our ability to build useful low dimensional representations of TCRs. To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. We design TCR-VALID such that the model representations are low-dimensional, continuous, disentangled, and sufficiently informative to provide high-quality TCR sequence de novo generation. We thoroughly quantify these properties of the representations, providing a framework for future protein representation learning in low dimensions. The continuity of TCR-VALID representations allows fast and accurate TCR clustering and is benchmarked against other state-of-the-art TCR clustering tools and pre-trained language models.
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