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Related Concept Videos

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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G Protein-coupled Receptors01:15

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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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A Protocol for Computer-Based Protein Structure and Function Prediction16:41

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Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and...
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A method for estimating the affinity constant of an agonist for the active state (Kb) of a G protein-coupled receptor is described. The analysis provides absolute or relative measures of Kb depending on whether constitutive receptor activation is measurable. Our method applies to various responses downstream from receptor...
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Measuring G-protein-coupled Receptor Signaling via Radio-labeled GTP Binding10:13

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Guanosine triphosphate (GTP) binding is one of the earliest events in G-Protein-Coupled Receptor (GPCR) activation. This protocol describes how to pharmacologically characterize specific GPCR-ligand interactions by monitoring the binding of the radio-labeled GTP analog, [35S]guanosine-5'-O-(3-thio)triphosphate ([35S]GTPγS), in response to a ligand of...
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Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors

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We genetically-encode the unnatural amino acid, p-azido-L-phenylalanine at various targeted positions in GPCRs and show the versatility of the azido group in different applications. These include a targeted photocrosslinking technology to identify residues in the ligand-binding pocket of a GPCR, and site-specific bioorthogonal modification of GPCRs with a peptide-epitope tag or fluorescent...
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Related Experiment Video

Updated: Jan 20, 2026

Ligand Binding Receptors : G-protein Coupled Receptors
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Function Prediction for G Protein-Coupled Receptors through Text Mining and Induction Matrix Completion.

Jiansheng Wu1, Qin Yin1, Chengxin Zhang2

  • 1School of Geographic and Biological Information and School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

ACS Omega
|August 29, 2019
PubMed
Summary

This study introduces TM-IMC, a novel method combining text mining and inductive matrix completion to automatically predict gene ontology (GO) terms for G protein-coupled receptors (GPCRs). This approach enhances the accuracy of GPCR function annotation using biomedical literature.

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Last Updated: Jan 20, 2026

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Area of Science:

  • Biochemistry and Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • G protein-coupled receptors (GPCRs) are crucial for cellular signal transduction.
  • Accurate annotation of GPCR biological functions is essential for understanding physiological processes.
  • The increasing volume of biomedical literature necessitates efficient methods for extracting functional information.

Purpose of the Study:

  • To develop an automated approach for predicting Gene Ontology (GO) terms for GPCR proteins.
  • To leverage text mining and inductive matrix completion for enhanced GPCR function annotation.
  • To systematically and reliably annotate known GPCRs using literature-derived data.

Main Methods:

  • A novel three-stage approach, TM-IMC, was designed.
  • The method integrates text mining techniques with inductive matrix completion (IMC).
  • Large-scale benchmark tests were conducted to evaluate prediction accuracy.

Main Results:

  • Inductive matrix completion models significantly improved GPCR-GO association predictions for molecular function and biological process.
  • Information extracted from GPCR-associated literature directly contributed to prediction accuracy.
  • TM-IMC demonstrated superior performance compared to baseline methods.

Conclusions:

  • The combination of text mining and inductive matrix completion offers a new avenue for accurate GPCR function annotation.
  • This approach enhances the reliability of GO annotations for GPCRs.
  • The study provides a valuable tool for the critical assessment of protein function annotation algorithms.