ArticleThe Journal of biological chemistry2016
Deep Mutational Scans as a Guide to Engineering High Affinity T Cell Receptor Interactions with Peptide-bound Major Histocompatibility Complex.
Article in The Journal of biological chemistry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 47 citations in OpenAlex.
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- A multi-ethnic reference map of T cell receptor germline diversity reveals evidence of natural selection on alpha chain genes.Nature communications · 2026Article
- CD8+ T cells cross-restricted by HLA-B*57 and HLA-E*01 recognize HIV Gag with different functional profiles.JCI insight · 2026Article
- Deep Mutational Scanning in Immunology: Techniques and Applications.Pathogens (Basel, Switzerland) · 2025Review
- Development of an innovative approach for early diagnosis of cervical cancer using TCR‑like antibodies targeting HPV18 E6 and E7 peptides.Molecular medicine reports · 2025Article
- Enhanced T cell receptor specificity through framework engineering.Frontiers in immunology · 2024Article
- Strategies and rules for tuning TCR-derived therapy.Expert reviews in molecular medicine · 2023Review
- 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
- Rapid screening of TCR-pMHC interactions by the YAMTAD system.Cell discovery · 2022Article
- Computational and experimental methods for classifying variants of unknown clinical significance.Cold Spring Harbor molecular case studies · 2022Article
- Immortalization and functional screening of natively paired human T cell receptor repertoires.Protein engineering, design & selection : PEDS · 2022Article
- ReliableJournal of chemical information and modeling · 2022Article
- Yeast Surface Display: New Opportunities for a Time-Tested Protein Engineering System.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Engineering Proteins by Combining Deep Mutational Scanning and Yeast Display.Methods in molecular biology (Clifton, N.J.) · 2022Article
- High throughput and quantitative enzymology in the genomic era.Current opinion in structural biology · 2021Review
- Engineering soluble T-cell receptors for therapy.The FEBS journal · 2021Review
- Linking genome variants to disease: scalable approaches to test the functional impact of human mutations.Human molecular genetics · 2021Review
- An Engineered T Cell Receptor Variant Realizes the Limits of Functional Binding Modes.Biochemistry · 2020Article
- Molecular Rules Underpinning Enhanced Affinity Binding of Human T Cell Receptors Engineered for Immunotherapy.Molecular therapy oncolytics · 2020Article
- Predicting antigen specificity of single T cells based on TCR CDR3 regions.Molecular systems biology · 2020Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Proteins are often engineered to have higher affinity for their ligands to achieve therapeutic benefit. For example, many studies have used phage or yeast display libraries of mutants within complementarity-determining regions to affinity mature antibodies and T cell receptors (TCRs). However, these approaches do not allow rapid assessment or evolution across the entire interface. By combining directed evolution with deep sequencing, it is now possible to generate sequence fitness landscapes that survey the impact of every amino acid substitution across the entire protein-protein interface. Here we used the results of deep mutational scans of a TCR-peptide-MHC interaction to guide mutational strategies. The approach yielded stable TCRs with affinity increases of >200-fold. The substitutions with the greatest enrichments based on the deep sequencing were validated to have higher affinity and could be combined to yield additional improvements. We also conducted in silico binding analyses for every substitution to compare them with the fitness landscape. Computational modeling did not effectively predict the impacts of mutations distal to the interface and did not account for yeast display results that depended on combinations of affinity and protein stability. However, computation accurately predicted affinity changes for mutations within or near the interface, highlighting the complementary strengths of computational modeling and yeast surface display coupled with deep mutational scanning for engineering high affinity TCRs.
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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.