ArticleProteins2009
Structure-based design of a T-cell receptor leads to nearly 100-fold improvement in binding affinity for pepMHC.
Article in Proteins, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed, 72 citations in OpenAlex.
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Targeting peptide-MHC complexes with designed T cell receptors and antibodies.bioRxiv : the preprint server for biology · 2025Article
- Identification and affinity enhancement of T-cell receptor targeting a KRASCommunications biology · 2024Article
- Strategies and rules for tuning TCR-derived therapy.Expert reviews in molecular medicine · 2023Review
- Structural analysis of cancer-relevant TCR-CD3 and peptide-MHC complexes by cryoEM.Nature communications · 2023Article
- Engineering the T cell receptor for fun and profit: Uncovering complex biology, interrogating the immune system, and targeting disease.Current opinion in structural biology · 2022Review
- ReliableJournal of chemical information and modeling · 2022Article
- Engineering soluble T-cell receptors for therapy.The FEBS journal · 2021Review
- An HLA-A*11:01-Binding Neoantigen from Mutated NPM1 as Target for TCR Gene Therapy in AML.Cancers · 2021Article
- Molecular Rules Underpinning Enhanced Affinity Binding of Human T Cell Receptors Engineered for Immunotherapy.Molecular therapy oncolytics · 2020Article
- Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors.Frontiers in immunology · 2020Review
- Understanding TCR affinity, antigen specificity, and cross-reactivity to improve TCR gene-modified T cells for cancer immunotherapy.Cancer immunology, immunotherapy : CII · 2019Review
- MHC binding affects the dynamics of different T-cell receptors in different ways.PLoS computational biology · 2019Article
- The intersection of affinity and specificity in the development and optimization of T cell receptor based therapeutics.Seminars in cell & developmental biology · 2018Review
- A humanized TCR retaining authentic specificity and affinity conferred potent anti-tumour cytotoxicity.Immunology · 2018Article
- DisruPPI: structure-based computational redesign algorithm for protein binding disruption.Bioinformatics (Oxford, England) · 2018Article
- Molecular Basis for Necitumumab Inhibition of EGFR Variants Associated with Acquired Cetuximab Resistance.Molecular cancer therapeutics · 2018Article
- STCRDab: the structural T-cell receptor database.Nucleic acids research · 2018Article
- DynaDom: structure-based prediction of T cell receptor inter-domain and T cell receptor-peptide-MHC (class I) association angles.BMC structural biology · 2017Article
- Using Global Analysis to Extend the Accuracy and Precision of Binding Measurements with T cell Receptors and Their Peptide/MHC Ligands.Frontiers in molecular biosciences · 2017Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
T-cell receptors (TCRs) are proteins that recognize peptides from foreign proteins bound to the major histocompatibility complex (MHC) on the surface of an antigen-presenting cell. This interaction enables the T cells to initiate a cell-mediated immune response to terminate cells displaying the foreign peptide on their MHC. Naturally occurring TCRs have high specificity but low affinity toward the peptide-MHC (pepMHC) complex. This prevents the usage of solubilized TCRs for diagnosis and treatment of viral infections or cancers. Efforts to enhance the binding affinity of several TCRs have been reported in recent years, through randomized libraries and in vitro selection. However, there have been no reported efforts to enhance the affinity via structure-based design, which allows more control and understanding of the mechanism of improvement. Here, we have applied structure-based design to a human TCR to improve its pepMHC binding. Our design method evolved based on iterative steps of prediction, testing, and generating more predictions based on the new data. The final design function, named ZAFFI, has a correlation of 0.77 and average error of 0.35 kcal/mol with the binding free energies of 26 point mutations for this system that we measured by surface plasmon resonance (SPR). Applying the filter that we developed to remove nonbinding predictions, this correlation increases to 0.85, and the average error decreases to 0.3 kcal/mol. Using this algorithm, we predicted and tested several point mutations that improved binding, with one giving over sixfold binding improvement. Four of the point mutations that improved binding were then combined to give a mutant TCR that binds the pepMHC 99 times more strongly than the wild-type TCR.
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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.