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Related Concept Videos

Single-pass Transmembrane Proteins01:25

Single-pass Transmembrane Proteins

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Integral membrane proteins are tightly associated with the cell membrane and play a crucial role in cell communication, signaling, adhesion, and transport of the molecules. Some integral membrane proteins are present only in the membrane monolayer. For example, the enzyme fatty acid amide hydrolase is present in the cytoplasmic side of the membrane monolayer. In contrast, another type of integral membrane protein, also known as a transmembrane protein, spans across the membrane. Transmembrane...
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Introduction to Membrane Proteins01:16

Introduction to Membrane Proteins

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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...
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Insertion of Single-pass Transmembrane Proteins in the RER01:26

Insertion of Single-pass Transmembrane Proteins in the RER

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Integral membrane proteins are proteins adhered to the lipid bilayer of a cell organelle or membrane. They can be of two types: transmembrane integral proteins that span the lipid bilayer and monotopic proteins that are attached to either side of the membrane but do not pass through it.
Integral transmembrane proteins possess transmembrane and extra membrane domains. The transmembrane domains are primarily made of 20-25 hydrophobic amino acids arranged in a helical secondary confirmation. These...
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Membrane Proteins01:30

Membrane Proteins

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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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Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

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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...
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Classification of Neurotransmitters01:30

Classification of Neurotransmitters

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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Related Experiment Video

Updated: Jan 1, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning.

Lei Guo1, Shunfang Wang2, Mingyuan Li1

  • 1Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, People's Republic of China.

BMC Bioinformatics
|December 26, 2019
PubMed
Summary

We developed advanced deep learning models for predicting membrane protein types, significantly improving accuracy over traditional methods. Our novel approach enhances the identification of these crucial biological molecules.

Keywords:
Deep learningMembrane protein type predictionVector representation

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Membrane proteins are vital for organismal functions.
  • Understanding membrane protein types is key to their structure-function analysis.
  • Current computational prediction methods require improvement.

Purpose of the Study:

  • To develop novel computational models for accurate membrane protein type prediction.
  • To enhance the performance of existing prediction techniques.

Main Methods:

  • Proposed two deep learning models utilizing sequence and evolutionary information.
  • Introduced a new vector representation method to replace one-hot encoding for sequence data.
  • Fused the two deep learning models for a comprehensive approach.

Main Results:

  • Both deep learning models outperformed traditional machine learning models.
  • The new vector representation improved success rates by 3.81% and 6.55% on two datasets.
  • The fused model achieved high success rates of 95.68% and 92.98%.

Conclusions:

  • The developed method is superior to existing approaches for membrane protein type prediction.
  • This advancement aids researchers in identifying novel membrane proteins.