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Related Concept Videos

Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as G-protein-linked receptors (GPCRs) and...
Membrane Proteins01:30

Membrane Proteins

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...
Membrane Proteins01:30

Membrane Proteins

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...
Introduction to Membrane Proteins01:16

Introduction to Membrane Proteins

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...
Membrane Asymmetry Regulating Transporters01:19

Membrane Asymmetry Regulating Transporters

Enzymes like flippase, floppase, and scramblase transfer phospholipids from one layer to another in the membrane, thereby affecting membrane asymmetry.
Flippase
Eukaryotic flippases are type-IV P-type ATPases or P4-ATPases belonging to P-type ATPase family proteins that are membrane-bound pumps involved in the ATP-mediated transport of ions and molecules across the membrane. Flippases flip specific phospholipids from the outer to the inner leaflet of a membrane. All P4-ATPases have one...
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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Related Experiment Video

Updated: May 7, 2026

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

SVM ensemble based transfer learning for large-scale membrane proteins discrimination.

Suyu Mei1

  • 1Software College, Shenyang Normal University, Shenyang, China.

Journal of Theoretical Biology
|September 21, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational model for identifying membrane proteins. The SVM-TLM method efficiently transfers knowledge from related proteins, improving accuracy in membrane protein discrimination.

Keywords:
Ensemble learningLarge data analysisPerformance overestimationProtein subcellular localizationTransfer learning

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Last Updated: May 7, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Published on: June 20, 2025

Method to Visualize and Analyze Membrane Interacting Proteins by Transmission Electron Microscopy
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Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies
07:31

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Published on: September 1, 2023

Area of Science:

  • Biochemistry and Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Membrane proteins are crucial for cellular functions including transport, signaling, and immunity.
  • Accurate identification of membrane proteins is challenging for both experimental and computational methods.
  • Existing computational models face limitations with data constraints and efficiency for large datasets.

Purpose of the Study:

  • To develop an efficient computational model for discriminating membrane proteins.
  • To leverage transfer learning for overcoming data limitations in membrane protein analysis.
  • To enhance the accuracy and computational efficiency of membrane protein identification.

Main Methods:

  • Developed a Support Vector Machine (SVM) ensemble based transfer learning model (SVM-TLM).
  • Employed probability weighted ensemble learning to transfer homologous protein knowledge.
  • Utilized sparseness-based SVM optimization for computational efficiency on large datasets.

Main Results:

  • SVM-TLM demonstrated superior cross-validation performance compared to a baseline model.
  • The model effectively transferred knowledge from homologous proteins to improve discrimination.
  • Achieved significantly better results on a large membrane protein benchmark dataset.

Conclusions:

  • The SVM-TLM model offers an effective and computationally efficient approach for membrane protein discrimination.
  • Transfer learning is a viable strategy to mitigate data constraints in computational protein modeling.
  • This method advances the computational analysis of membrane proteins, aiding biological research.