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Related Concept Videos

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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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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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Single-pass Transmembrane Proteins01:25

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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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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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Related Experiment Video

Updated: Oct 3, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning.

Alexios Chatzigoulas1,2, Zoe Cournia1

  • 1Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece.

Briefings in Bioinformatics
|February 13, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method to predict how proteins attach to cell membranes, aiding drug discovery for diseases linked to abnormal protein interactions. The algorithm accurately identifies membrane-penetrating amino acids, making previously "undruggable" targets accessible.

Keywords:
machine learningmembrane-penetrating amino acidsperipheral membrane proteinsprotein–membrane interfaceprotein–membrane regions

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Abnormal protein-membrane interactions are implicated in various diseases.
  • Peripheral membrane proteins, crucial in cellular processes, are often considered difficult therapeutic targets due to unknown binding domains.
  • Modulating these interactions offers a novel therapeutic avenue.

Purpose of the Study:

  • To develop a computational tool for predicting protein-membrane interfaces.
  • To identify membrane-penetrating amino acids for peripheral membrane proteins.
  • To facilitate drug design targeting these previously undruggable proteins.

Main Methods:

  • An ensemble machine learning methodology was developed.
  • Twenty-one machine learning classifiers and meta-classifiers were trained using experimental data.
  • The algorithm predicts membrane-penetrating amino acids in proteins.

Main Results:

  • The best ensemble model achieved a macro-averaged F1 score of 0.92.
  • A Matthews correlation coefficient of 0.84 was obtained for predicting membrane-penetrating amino acids.
  • The method demonstrates high accuracy on an independent validation set.

Conclusions:

  • The developed algorithm accurately predicts protein-membrane interfaces.
  • This tool enhances the druggability of peripheral membrane proteins.
  • The computational approach opens new possibilities for therapeutic strategies against diseases involving protein-membrane dysregulation.