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Related Concept Videos

Porin Insertion in the Outer Mitochondrial Membrane01:12

Porin Insertion in the Outer Mitochondrial Membrane

Porins are beta-barrel proteins translocated to the mitochondrial outer membrane through the TOM complex into the intermembrane space. Porin precursors bind TIM chaperones within the intermembrane space and are guided to the Sorting and Assembly Machinery complex or SAM complex on the outer mitochondrial membrane.
Three models describe the assembly of porins by the SAM complex and their insertion into the outer membrane. Model 1 suggests that porins are assembled outside the SAM channel as the...
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
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Single-pass Transmembrane Proteins01:25

Single-pass Transmembrane Proteins

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...
Insertion of Multi-pass Transmembrane Proteins in the RER01:29

Insertion of Multi-pass Transmembrane Proteins in the RER

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Updated: Jul 3, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

[Prediction of outer membrane proteins using support vector machine with combined features].

Lingyun Zou1, Zhengzhi Wang, Yongxian Wang

  • 1School of Mechatronics and Automatization, National University of Defence Technology, Changsha 410073, China. lyzou@nudt.edu.cn

Sheng Wu Gong Cheng Xue Bao = Chinese Journal of Biotechnology
|July 12, 2008
PubMed
Summary

This study introduces a powerful bioinformatics tool for identifying outer membrane proteins (OMPs) using combined sequence features and a support vector machine. The method achieves high accuracy in distinguishing OMPs from other protein types.

Related Experiment Videos

Last Updated: Jul 3, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Context:

  • Outer membrane proteins (OMPs) are crucial components of Gram-negative bacteria, mitochondria, and chloroplasts.
  • Their diverse functions and cellular locations highlight their importance in biological systems.
  • Accurate identification of OMPs is essential for understanding cellular processes and disease mechanisms.

Purpose:

  • To develop and validate a robust bioinformatics method for the accurate prediction of outer membrane proteins (OMPs).
  • To combine multiple sequence-derived features for enhanced discrimination of OMPs from other protein classes.
  • To assess the performance of a support vector machine (SVM) based predictor using these combined features.

Summary:

  • Three feature classes—amino acid composition, dipeptide composition, and weighted amino acid index correlation coefficients—were extracted from protein sequences.
  • These features were integrated and utilized in an SVM-based predictor to differentiate OMPs from other protein folding types.
  • The developed method achieved high accuracy (96.96% in cross-validation, 97.33% in independent tests) on a diverse dataset of 1087 proteins.

Impact:

  • The method demonstrates superior performance compared to existing approaches for OMP discrimination.
  • It shows high specificity in identifying OMPs across five bacterial genomes.
  • The predictor correctly identifies over 99% of known OMPs in the Protein Data Bank (PDB), proving its utility for genomic analysis.