Evidence map›Paper›PMID 31792443›Full record

ArticleNature chemical biology2020

Computational design of G Protein-Coupled Receptor allosteric signal transductions.

Kuang-Yui Michael Chen, Daniel Keri, Patrick Barth

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Nature chemical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed
3.5field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 79 citations in OpenAlex.

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  6. Deep learning-guided design of dynamic proteins.Science (New York, N.Y.) · 2025
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  19. Exploiting conformational dynamics to modulate the function of designed proteins.Proceedings of the National Academy of Sciences of the United States of America · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Kuang-Yui Michael Chen *Swiss Federal Institute of Technology (EPFL), Institute of Bioengineering, Lausanne, Switzerland.ORCID http://orcid.org/0000-0003-0551-744X
Daniel Keri *Swiss Federal Institute of Technology (EPFL), Institute of Bioengineering, Lausanne, Switzerland.
Patrick BarthSwiss Federal Institute of Technology (EPFL), Institute of Bioengineering, Lausanne, Switzerland. patrick.barth@epfl.ch.ORCID http://orcid.org/0000-0002-0744-6844
Baylor College of Medicine · USÉcole Polytechnique Fédérale de Lausanne · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Membrane receptors sense and transduce extracellular stimuli into intracellular signaling responses but the molecular underpinnings remain poorly understood. We report a computational approach for designing protein allosteric signaling functions. By combining molecular dynamics simulations and design calculations, the method engineers amino-acid 'microswitches' at allosteric sites that modulate receptor stability or long-range coupling, to reprogram specific signaling properties. We designed 36 dopamine D2 receptor variants, whose constitutive and ligand-induced signaling agreed well with our predictions, repurposed the D2 receptor into a serotonin biosensor and predicted the signaling effects of more than 100 known G-protein-coupled receptor (GPCR) mutations. Our results reveal the existence of distinct classes of allosteric microswitches and pathways that define an unforeseen molecular mechanism of regulation and evolution of GPCR signaling. Our approach enables the rational design of allosteric receptors with enhanced stability and function to facilitate structural characterization, and reprogram cellular signaling in synthetic biology and cell engineering applications.

Indexed as

Protein EngineeringSignal TransductionAllosteric RegulationAllosteric SiteAmino Acid MotifsBiosensing TechniquesComputer SimulationDNA Mutational AnalysisHEK293 CellsHumansInhibitory Concentration 50KineticsLigandsMolecular Dynamics SimulationMutagenesisReceptors, Dopamine D2DRD2 protein, humanLigandsReceptors, Dopamine D2Serotonin

Identifiers

PMID31792443
OpenAlexW2991478808

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.