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Related Concept Videos

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...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
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.
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

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Related Experiment Video

Updated: Jul 5, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Proximity based GPCRs prediction in transform domain.

Asifullah Khan1, M F Khan, Tae-Sun Choi

  • 1Department of Mechatronics, Gwangju Institute of Science and Technology, 1 Oryong-Dong, Buk-Gu, Gwangju 500-712, Republic of Korea.

Biochemical and Biophysical Research Communications
|April 29, 2008
PubMed
Summary
This summary is machine-generated.

Predicting G-protein coupled receptors (GPCRs) is improved by using amino acid hydrophobicity and Fast Fourier Transform. A simple nearest neighbor approach effectively classifies GPCR subfamilies, outperforming complex methods.

Related Experiment Videos

Last Updated: Jul 5, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • G-protein coupled receptors (GPCRs) are crucial membrane proteins involved in numerous physiological processes.
  • Accurate classification of GPCR subfamilies is essential for understanding their diverse functions and for drug discovery.
  • Current classification methods may not fully exploit the information embedded within amino acid sequences.

Purpose of the Study:

  • To develop and evaluate a novel method for predicting and classifying GPCR subfamilies.
  • To investigate the impact of feature space exploitation on classification strategy effectiveness.
  • To compare the performance of a proximity-based classifier with a margin-based classifier for GPCRs.

Main Methods:

  • Utilizing amino acid sequence hydrophobicity for feature generation.
  • Applying Fast Fourier Transform (FFT) for extracting sequence patterns in the frequency domain.
  • Implementing a nearest neighbor (NN) classifier for GPCR subfamily classification.
  • Comparing NN performance against a support vector machine (SVM) one-against-all approach.

Main Results:

  • Sequence pattern information is effectively exploited in the frequency domain using proximity.
  • The nearest neighbor (NN) classifier achieved superior performance in classifying 17 GPCR subfamilies.
  • NN outperformed the SVM approach on both Jackknife and independent datasets.
  • The study highlights the significance of efficient feature space exploitation.

Conclusions:

  • A simple, proximity-based nearest neighbor approach offers an effective strategy for GPCR subfamily classification.
  • Efficient exploitation of the feature space, particularly in the frequency domain, is key to successful classification.
  • Simple classification strategies can outperform complex ones when feature space utilization is optimized.