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Related Concept Videos

G Protein-coupled Receptors01:15

G Protein-coupled Receptors

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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
GPCRs are also called heptahelical, 7TM, or serpentine receptors, and consist of seven (H1-H7) transmembrane alpha-helices that span the bilayer to form a cylindrical core. The transmembrane helices are connected by three extracellular loops and three...
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Transducer Mechanism: G Protein–Coupled Receptors01:30

Transducer Mechanism: G Protein–Coupled Receptors

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G Protein–Coupled Receptors (GPCRs) are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to various stimuli. GPCRs regulate critical physiological pathways and are excellent drug targets for treating diseases such as diabetes, cancer, obesity, depression, or Alzheimer's. Nearly 35% of approved drugs implement their therapeutic effects by selectively interacting with specific GPCRs.
GPCRs are also called heptahelical,...
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G-protein Coupled Receptors01:21

G-protein Coupled Receptors

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G-protein coupled receptors are ligand binding receptors that indirectly affect changes in the cell. The actual receptor is a single polypeptide that transverses the cell membrane seven times creating intracellular and extracellular loops. The extracellular loops create a ligand specific pocket which binds to neurotransmitters or hormones. The intracellular loops holds onto the G-protein.
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as...
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Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
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Related Experiment Video

Updated: Jun 29, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Predicting associations between drugs and G protein-coupled receptors using a multi-graph convolutional network.

Yuxun Luo1, Shasha Li2, Li Peng1

  • 1School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China.

Computational Biology and Chemistry
|April 5, 2024
PubMed
Summary

This study introduces a novel deep learning model for drug repurposing, enhancing the discovery of new drug-G protein-coupled receptor (GPCR) interactions. The multi-graph convolutional network model effectively integrates diverse data sources for improved prediction accuracy.

Keywords:
Deep learningDrugsG Protein-Coupled ReceptorsGraph convolutional network

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Area of Science:

  • Pharmacology and Cheminformatics
  • Computational Biology
  • Drug Discovery

Background:

  • Drug development is costly and time-consuming, with safety concerns.
  • Drug repurposing offers a faster, more economical alternative by finding new uses for existing drugs.
  • G protein-coupled receptors (GPCRs) are a major drug target class, making them crucial for drug repurposing strategies.

Purpose of the Study:

  • To develop an advanced computational model for predicting novel drug-GPCR interactions.
  • To overcome limitations of existing methods that fail to integrate multiple data types.
  • To accelerate the drug repurposing process through precise interaction prediction.

Main Methods:

  • Development of an end-to-end deep learning model utilizing a multi-graph convolutional network (MGCN).
  • Integration of multi-source data, including drug structure, drug-drug interactions, GPCR sequences, and subfamily information.
  • Comparative analysis against existing deep learning and non-deep learning models.

Main Results:

  • The proposed MGCN model demonstrated superior performance in inferring drug-GPCR relationships compared to existing methods.
  • The model successfully integrated diverse data sources for enhanced prediction accuracy.
  • Multi-source data integration proved crucial for advancing drug-GPCR relationship detection.

Conclusions:

  • The developed multi-graph convolutional network model offers an efficient and precise approach for drug repurposing.
  • Integrating multi-source data significantly improves the prediction of novel drug-GPCR associations.
  • This computational strategy holds promise for accelerating the identification of new therapeutic applications for existing drugs.