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Related Concept Videos

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 Receptors01:15

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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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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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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Activation and Inactivation of G Proteins01:22

Activation and Inactivation of G Proteins

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Heterotrimeric G proteins are guanine nucleotide-binding proteins. As the name suggests, heterotrimeric G proteins are composed of three subunits: alpha, beta, and gamma. They remain GDP-bound or GTP-bound inside the cells and switch between inactive/active states. The Gα subunit possesses the nucleotide-binding pocket that binds guanine nucleotides and switches between GDP or GTP-bound states. In contrast, the Gꞵ and Gγ subunits are always bound together with high...
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¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

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At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
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Visualizing the Conformational Dynamics of Membrane Receptors Using Single-Molecule FRET
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Characterizing conformational states in GPCR structures using machine learning.

Ilya Buyanov1, Petr Popov2

  • 1iMolecule, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia.

Scientific Reports
|January 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method to classify G protein-coupled receptor (GPCR) conformations. The approach accurately distinguishes active and inactive states, aiding drug discovery efforts.

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • G protein-coupled receptors (GPCRs) are crucial for cellular signal transduction.
  • GPCRs are key targets for pharmaceutical development.
  • Understanding GPCR conformational states is vital for drug discovery.

Purpose of the Study:

  • To develop a machine learning approach for annotating GPCR conformational states.
  • To differentiate between inactive-like and active-like GPCR conformations.

Main Methods:

  • Representing GPCR conformations using high-dimensional feature vectors.
  • Incorporating amino acid residue pair information related to activation pathways.
  • Training machine learning models on molecular dynamics simulation data of GPCRs.

Main Results:

  • Successfully trained machine learning models to distinguish GPCR conformational states.
  • The model provides interpretable predictions for GPCR activity.
  • The method enables large-scale analysis of GPCR molecular dynamics trajectories.

Conclusions:

  • Machine learning offers a powerful tool for GPCR conformational analysis.
  • Accurate annotation of GPCR states can accelerate structure-based drug discovery.
  • This approach facilitates deeper understanding of GPCR signaling mechanisms.