ArticlePLoS computational biology2014
Computational design of the affinity and specificity of a therapeutic T cell receptor.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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Who cites it
65 citing papers in PubMed.
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- TCR-FramePose: a local-frame representation for decomposing global docking and CDR3 loop geometry in TCR-pMHC recognition.bioRxiv : the preprint server for biology · 2026Article
- 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
- Benchmarking TCR-pMHC structure prediction: a unified evaluation and CDR3-based functional insights.Briefings in bioinformatics · 2026Article
- Targeting peptide-MHC complexes with designed T cell receptors and antibodies.bioRxiv : the preprint server for biology · 2025Article
- Exploring the potential of structure-based deep learning approaches for T cell receptor design.PLoS computational biology · 2024Article
- Polymorphic residues in HLA-B that mediate HIV control distinctly modulate peptide interactions with both TCR and KIR molecules.Structure (London, England : 1993) · 2024Article
- Proscan: a structure-based proline design web server.Nucleic acids research · 2024Article
- RACER-m leverages structural features for sparse T cell specificity prediction.Science advances · 2024Article
- Identification and affinity enhancement of T-cell receptor targeting a KRASCommunications biology · 2024Article
- Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design.bioRxiv : the preprint server for biology · 2024Article
- Combinatorially restricted computational design of protein-protein interfaces to produce IgG heterodimers.Science advances · 2024Article
- Strategies and rules for tuning TCR-derived therapy.Expert reviews in molecular medicine · 2023Review
- TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.Nucleic acids research · 2023Article
- TCR-engineered T cell therapy in solid tumors: State of the art and perspectives.Science advances · 2023Review
- Validation and promise of a TCR mimic antibody for cancer immunotherapy of hepatocellular carcinoma.Scientific reports · 2022Article
- 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
- An extended motif in the SARS-CoV-2 spike modulates binding and release of host coatomer in retrograde trafficking.Communications biology · 2022Article
5 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
T cell receptors (TCRs) are key to antigen-specific immunity and are increasingly being explored as therapeutics, most visibly in cancer immunotherapy. As TCRs typically possess only low-to-moderate affinity for their peptide/MHC (pMHC) ligands, there is a recognized need to develop affinity-enhanced TCR variants. Previous in vitro engineering efforts have yielded remarkable improvements in TCR affinity, yet concerns exist about the maintenance of peptide specificity and the biological impacts of ultra-high affinity. As opposed to in vitro engineering, computational design can directly address these issues, in theory permitting the rational control of peptide specificity together with relatively controlled increments in affinity. Here we explored the efficacy of computational design with the clinically relevant TCR DMF5, which recognizes nonameric and decameric epitopes from the melanoma-associated Melan-A/MART-1 protein presented by the class I MHC HLA-A2. We tested multiple mutations selected by flexible and rigid modeling protocols, assessed impacts on affinity and specificity, and utilized the data to examine and improve algorithmic performance. We identified multiple mutations that improved binding affinity, and characterized the structure, affinity, and binding kinetics of a previously reported double mutant that exhibits an impressive 400-fold affinity improvement for the decameric pMHC ligand without detectable binding to non-cognate ligands. The structure of this high affinity mutant indicated very little conformational consequences and emphasized the high fidelity of our modeling procedure. Overall, our work showcases the capability of computational design to generate TCRs with improved pMHC affinities while explicitly accounting for peptide specificity, as well as its potential for generating TCRs with customized antigen targeting capabilities.
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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.