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Related Concept Videos

Ion Channels01:19

Ion Channels

91.2K
The movement of ions like sodium, potassium, and calcium into and out of the cell is essential to maintain the electrochemical gradient in living cells. The ion channels—a class of membrane transport proteins—help maintain this ionic gradient for the smooth functioning of physiological activities such as maintaining cell size and volume, conducting nerve impulses, and gas and nutrient exchange.
Ion channels are specialized integral membrane proteins on the plasma membrane that allow...
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Non-gated Ion Channels01:24

Non-gated Ion Channels

8.1K
Ion channels are specialized proteins on the plasma membrane that allow charged ions to pass down their electrochemical gradient. Their main function is to maintain the membrane potential which is critical for cell viability. These channels are either gated or non-gated and can transport more than a thousand ions within milliseconds for the cellular event to occur.
Compared to the gated ion channels, the non-gated channels, also known as leakage or passive channels, have no gating mechanism....
8.1K
G-Protein Gated Ion Channels01:21

G-Protein Gated Ion Channels

5.6K
GPCRs are primarily responsible for our sense of smell, taste, and vision.  The binding of a sensory stimulus activates GPCR to stimulate effector proteins, many of which are ion channels in the sensory organs. GPCRs modulate the opening and closing of the target ion channels either directly by binding them, or by releasing second messengers that activate these channels. As ions move across the membrane, the membrane potential is altered, which induces an appropriate response.
Sensory...
5.6K
Ligand-gated Ion Channels01:19

Ligand-gated Ion Channels

14.0K
Ligand-gated ion channels are transmembrane proteins with a channel for ions to pass through and a binding site for a ligand. The channel opens only when a ligand attaches to the binding site.
Three Subfamilies of Ligand-gated Ion Channels
Ligand-gated ion channels fall into three subfamilies. The 'Cys-loop' includes the nicotinic acetylcholine receptors, γ-aminobutyric acid (GABA), glycine, and 5-hydroxytryptamine receptors. The second one is the 'Pore-loop' channels that...
14.0K
Voltage-gated Ion Channels01:26

Voltage-gated Ion Channels

10.6K
Voltage-gated ion channels are transmembrane proteins that open and close in response to changes in the membrane potential. They are present on the membranes of all electrically excitable cells such as neurons, heart, and muscle cells.
Generally, all voltage-gated ion channels have a 'voltage-sensing domain' that spans the lipid bilayer. The charged residues in the sensor move in response to the membrane potential changes that open the channel allowing ions movement. There are several types of...
10.6K
Mechanically-gated Ion Channels01:12

Mechanically-gated Ion Channels

7.6K
Mechanically-gated ion channels are proteins found in eukaryotic and prokaryotic cell membranes that open in response to mechanical stress. Tension, compression, swelling, and shear stress can alter the conformation of the protein, opening a transmembrane channel that allows the passage of ions for signal transmission. In eukaryotes, mechanically-gated channels are distributed in several regions like the neurons, lungs, skin, bladder, and heart, where they play critical roles in numerous...
7.6K

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.

Ke Han1,2, Miao Wang3, Lei Zhang3

  • 1School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China.

Frontiers in Genetics
|May 28, 2019
PubMed
Summary
This summary is machine-generated.

Computational methods predict ion channel types from protein sequences. This study developed effective feature extraction and dimensionality reduction techniques, improving ion channel classification for drug discovery.

Keywords:
SVMfeature selectionion channelmachine learningrandom forest

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • The rapid increase in known ion channels, many linked to diseases, highlights their importance as drug targets.
  • Accurate prediction of ion channel types from protein sequences is crucial for drug discovery and development.

Purpose of the Study:

  • To develop and evaluate computational methods for predicting ion channel types using protein sequence data.
  • To enhance the accuracy of ion channel classification through advanced feature extraction and dimensionality reduction techniques.

Main Methods:

  • Utilized SVMProt and k-skip-n-gram methods for feature vector extraction (188- and 400-dimensional features).
  • Combined features to create a 588-dimensional vector, then reduced dimensions using the maximum-relevance-maximum-distance method.
  • Employed Support Vector Machine (SVM) and Random Forest classifiers to build and evaluate prediction models.

Main Results:

  • Successfully extracted and reduced feature vectors from ion channel data sourced from UniProt and LGICdb.
  • Validated the performance of SVM and Random Forest classifiers on the processed ion channel data.
  • Demonstrated the effectiveness of the developed computational approach for ion channel classification.

Conclusions:

  • The study presents a robust computational framework for classifying ion channels based on protein sequences.
  • The findings can guide the identification of novel ion channel targets and inform the development of new therapeutic drugs.
  • Improved ion channel prediction accuracy contributes to accelerating drug discovery pipelines.