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Related Concept Videos

G-protein Coupled Receptors01:21

G-protein Coupled Receptors

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.
G-protein Coupled Receptors01:21

G-protein Coupled Receptors

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.
G Protein-coupled Receptors01:15

G Protein-coupled Receptors

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...
G Protein-coupled Receptors01:15

G Protein-coupled Receptors

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...
Transducer Mechanism: G Protein–Coupled Receptors01:30

Transducer Mechanism: G Protein–Coupled Receptors

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, 7TM, or...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...

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Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors
16:16

Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors

Published on: September 13, 2013

Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery.

Jason Chung1,2, Hyunggu Hahn1,2, Emmanuel Flores-Espinoza1,2

  • 1Department of Molecular Pathobiology, New York University College of Dentistry, New York, NY 10010, USA.

Biomolecules
|March 28, 2025
PubMed
Summary

Artificial intelligence (AI) revolutionizes protein structure prediction, but accuracy for drug discovery details like binding pockets remains a challenge. Further research is needed to fully leverage AI for structure-based drug development.

Keywords:
AlphaFoldGPCRsRoseTTAFoldcomputational dockingstructure-based drug discoveryvirtual screening

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Structure-Guided Design and Development of Novel Cyclophilin A Inhibitors and Ganoderiol-F Derivatives: An In-Silico Approach
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Structure-Guided Design and Development of Novel Cyclophilin A Inhibitors and Ganoderiol-F Derivatives: An In-Silico Approach

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Genetically-encoded Molecular Probes to Study G Protein-coupled Receptors
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Structure-Guided Design and Development of Novel Cyclophilin A Inhibitors and Ganoderiol-F Derivatives: An In-Silico Approach
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Structure-Guided Design and Development of Novel Cyclophilin A Inhibitors and Ganoderiol-F Derivatives: An In-Silico Approach

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Area of Science:

  • Computational biology
  • Artificial intelligence
  • Drug discovery

Background:

  • Traditional experimental methods (X-ray crystallography, NMR, cryo-EM) for protein structure determination are resource-intensive and time-consuming.
  • Recent AI advancements like AlphaFold and RoseTTAFold offer rapid and accurate protein structure predictions from amino acid sequences.

Purpose of the Study:

  • To review the latest AI developments in protein structure prediction.
  • To assess the potential of AI approaches in structure-based drug discovery, focusing on GPCRs.
  • To identify limitations of current AI methods for drug discovery applications.

Main Methods:

  • Review of recent literature on AI-driven protein structure prediction.
  • Analysis of AI model performance in predicting protein structures.
  • Evaluation of AI's accuracy for specific drug discovery tasks, such as ligand docking.

Main Results:

  • AI models demonstrate high accuracy in predicting overall protein structures.
  • Essential details for ligand docking, like side-chain positioning in binding pockets, are not yet accurately predicted by AI.
  • Current docking methodologies generate numerous false positives, limiting their precision.

Conclusions:

  • AI significantly accelerates protein structure prediction but requires further refinement for precise drug discovery applications.
  • The accuracy of AI predictions for critical drug target details needs improvement.
  • AI's role in structure-based drug discovery, particularly for GPCRs, is promising but not yet fully realized.