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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Plasma membranes have integral transmembrane proteins involved in facilitated transport. These proteins are collectively referred to as transport proteins, and they function as either channels for the material or as carriers themselves. Channel proteins have hydrophilic domains exposed to the intracellular and extracellular fluids and a hydrophilic channel through their core that provides a hydrated opening for solutes to pass through the membrane layers. Passage through the channel allows...
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The cell membrane, or plasma membrane, is an ever-changing landscape. It is described as a fluid mosaic where various macromolecules are embedded in the phospholipid bilayer. Among the macromolecules are proteins. The protein content varies across cell types. For example, mitochondrial inner membranes contain ~76% protein content, while myelin contains ~18% protein content. Individual cells contain many types of membrane proteins—red blood cells contain over 50—and different cell types have...
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Determining Membrane Protein Topology Using Fluorescence Protease Protection (FPP)
08:14

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Published on: April 20, 2015

Functional discrimination of membrane proteins using machine learning techniques.

M Michael Gromiha1, Yukimitsu Yabuki

  • 1Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. michael-gromiha@aist.go.jp

BMC Bioinformatics
|March 4, 2008
PubMed
Summary
This summary is machine-generated.

This study identifies distinct amino acid patterns in membrane proteins, improving the classification of channels, transporters, and other proteins using machine learning. Amino acid occurrence enhances discrimination accuracy for functional annotation.

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Genomics

Background:

  • Functional discrimination of membrane proteins is crucial for genome annotation.
  • Analysis focuses on characteristic amino acid features in key functional classes: channels/pores, electrochemical potential-driven transporters, and primary active transporters.

Purpose of the Study:

  • To analyze and differentiate membrane proteins based on their functional roles using amino acid composition and occurrence.
  • To evaluate the effectiveness of various machine learning algorithms for this classification task.

Main Methods:

  • Utilized machine learning algorithms including Bayes rule, logistic function, neural networks, support vector machines, and decision trees.
  • Employed amino acid composition and occurrence as features for classification.
  • Applied k-nearest neighbor method for discriminating transporters from other membrane protein types.

Main Results:

  • Neural networks achieved 64% accuracy in classifying channels/pores, electrochemical, and active transporters; accuracy improved to 68% with amino acid occurrence.
  • K-nearest neighbor method achieved 85% accuracy in discriminating transporters from other alpha-helical and beta-barrel membrane proteins.
  • Transporter classification against all other proteins (globular and membrane) yielded 82% accuracy.

Conclusions:

  • Amino acid occurrence provides superior discrimination performance compared to amino acid composition.
  • The proposed method effectively distinguishes transporters from other globular and membrane proteins.
  • This approach facilitates accurate classification of transporters into functional subclasses: channels/pores, electrochemical, and active transporters.