ArticleNature chemical biology2017
Computational design of ligand-binding membrane receptors with high selectivity.
Article in Nature chemical biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 39 citations in OpenAlex.
- Assessing structural prediction accuracy for nanobody-small molecule complexes.Protein engineering, design & selection : PEDS · 2026Article
- Computational design of highly signalling-active membrane receptors through solvent-mediated allosteric networks.Nature chemistry · 2025Article
- Computational design of dynamic receptor-peptide signaling complexes applied to chemotaxis.Nature communications · 2023Article
- Prediction of dynamic allostery for the transmembrane domain of the sweet taste receptor subunit, TAS1R3.Communications biology · 2023Article
- Computationally designed GPCR quaternary structures bias signaling pathway activation.Nature communications · 2022Article
- VARIDT 2.0: structural variability of drug transporter.Nucleic acids research · 2022Article
- The Many Faces of G Protein-Coupled Receptor 143, an Atypical Intracellular Receptor.Frontiers in molecular biosciences · 2022Review
- Class A and C GPCR Dimers in Neurodegenerative Diseases.Current neuropharmacology · 2022Review
- A study of deregulated MMR pathways and anticancer potential of curcuma derivatives using computational approach.Scientific reports · 2021Article
- Structural insights into emergent signaling modes of G protein-coupled receptors.The Journal of biological chemistry · 2020Review
- A simple open source bioinformatic methodology for initial exploration of GPCR ligands' agonistic/antagonistic properties.Pharmacology research & perspectives · 2020Article
- Article
- Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane.Biophysical journal · 2020Article
- A Bright and Colorful Future for G-Protein Coupled Receptor Sensors.Frontiers in cellular neuroscience · 2020Article
- Computational design of G Protein-Coupled Receptor allosteric signal transductions.Nature chemical biology · 2020Article
- Common activation mechanism of class A GPCRs.eLife · 2019Article
- A lipophilicity-based energy function for membrane-protein modelling and design.PLoS computational biology · 2019Article
- Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets.Journal of medicinal chemistry · 2018Article
- Reprogramming G protein coupled receptor structure and function.Current opinion in structural biology · 2018Review
- Computational design of orthogonal membrane receptor-effector switches for rewiring signaling pathways.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Accurate modeling and design of protein-ligand interactions have broad applications in cell biology, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein-homology-modeling, ligand-docking protein-design approach that reconstructs protein-ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced-fit modes of ligand binding. In structure modeling tests, we blindly predicted, with near-atomic accuracy, ligand conformations bound to G-protein-coupled receptors (GPCRs) that have rarely been identified using traditional approaches. We also quantitatively predicted the binding selectivity of diverse ligands to structurally uncharacterized GPCRs. We then applied this technique to design functional human dopamine receptors with novel ligand-binding selectivity. Most blindly predicted ligand-binding specificities closely agreed with experimental validations. Our method should prove useful in ligand discovery approaches and in reprogramming the ligand-binding profile of membrane receptors that remain difficult to crystallize.
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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.