ArticleScience advances2024
Structural characterization and AlphaFold modeling of human T cell receptor recognition of NRAS cancer neoantigens.
Article in Science advances, 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.
- Article
- Artificial intelligence for translational personalized neoantigen cancer vaccine development.Journal of biomedical science · 2026Review
- Structural Basis for the Immunological Paradox of a High-Affinity Yet Non-Immunogenic MHC-I Epitope fromBiology · 2026Article
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- A comparative and exploratory analysis of computational methods for TCR structural prediction and antigen-specific TCR discovery.Briefings in bioinformatics · 2026Article
- Mapping the TCR landscape: computational tools empowering translational immunology and therapy design.Journal for immunotherapy of cancer · 2026Review
- Engineering a Second Interchain Disulfide Bond in the αβ T-Cell Receptor Constant Domain: A Powerful Strategy to Enhance Stability, Pairing Fidelity, and Therapeutic Efficacy in TCR-T Cell Therapy.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The Inclusion of Dietary and Medicinal Mushrooms into Translational Oncology: Pros and Cons at the Molecular Level.International journal of molecular sciences · 2026Review
- Clonally expanded HSP-specific T cells contribute to glaucomatous neurodegeneration via the mTORC1 pathway.Journal of neuroinflammation · 2026Article
- Structural quality-tier assessment for TCR-pMHC functional enrichment.Frontiers in immunology · 2026Article
- Structural insights into clonal restriction and diversity in T cell recognition of two immunodominant SARS-CoV-2 nucleocapsid epitopes.Nature communications · 2025Article
- Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- SageTCR: a structure-based model integrating residue- and atom-level representations for enhanced TCR-pMHC binding prediction.Briefings in bioinformatics · 2025Article
- The future of pharmaceuticals: Artificial intelligence in drug discovery and development.Journal of pharmaceutical analysis · 2025Review
- Three-Dimensional Modeling ofAntibodies (Basel, Switzerland) · 2025Article
- AlphaFold3: An Overview of Applications and Performance Insights.International journal of molecular sciences · 2025Review
- AI/ML-empowered approaches for predicting T Cell-mediated immunity and beyond.Frontiers in immunology · 2025Article
- SARS-CoV-2 spike does not interact with the T cell receptor or directly activate T cells.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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8 authors.
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Abstract
T cell receptors (TCRs) that recognize cancer neoantigens are important for anticancer immune responses and immunotherapy. Understanding the structural basis of TCR recognition of neoantigens provides insights into their exquisite specificity and can enable design of optimized TCRs. We determined crystal structures of a human TCR in complex with NRAS Q61K and Q61R neoantigen peptides and HLA-A1 major histocompatibility complex (MHC), revealing the molecular underpinnings for dual recognition and specificity versus wild-type NRAS peptide. We then used multiple versions of AlphaFold to model the corresponding complex structures, given the challenge of immune recognition for such methods. One implementation of AlphaFold2 (TCRmodel2) with additional sampling was able to generate accurate models of the complexes, while AlphaFold3 also showed strong performance, although success was lower for other complexes. This study provides insights into TCR recognition of a shared cancer neoantigen as well as the utility and practical considerations for using AlphaFold to model TCR-peptide-MHC complexes.
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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.