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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

15.0K
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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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Related Experiment Video

Updated: Mar 18, 2026

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

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An improved method for predicting interactions between virus and human proteins.

Byungmin Kim1, Saud Alguwaizani1, Xiang Zhou1

  • 1* Department of Computer Science and Engineering, Inha University, Incheon 22212, South Korea.

Journal of Bioinformatics and Computational Biology
|July 12, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method to predict virus-host protein-protein interactions (PPIs). The new approach significantly outperforms existing methods for human papillomaviruses (HPV) and hepatitis C virus (HCV) PPI prediction.

Keywords:
Protein–protein interactionshepatitis C virushuman papillomaviruses

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Area of Science:

  • Computational Biology
  • Virology
  • Bioinformatics

Background:

  • Virus-host protein-protein interactions (PPIs) are crucial for viral pathogenesis.
  • Existing computational methods primarily focus on intra-species PPIs, with limited effectiveness for virus-host interactions.

Purpose of the Study:

  • To develop and evaluate a novel computational method for predicting virus-host PPIs.
  • To improve the accuracy of predicting interactions between viral and human proteins.

Main Methods:

  • Developed a method to represent variable-length viral and human proteins into fixed-length feature vectors.
  • Utilized key features including relative amino acid triplet frequency (RFAT), frequency difference (FDAT), and amino acid composition (AC).
  • Employed support vector machine (SVM) models for prediction and comparison.

Main Results:

  • The proposed method demonstrated significantly higher performance compared to existing methods on human papillomavirus (HPV)-human and hepatitis C virus (HCV)-human PPI datasets.
  • SVM models incorporating gene ontology (GO) annotations were used to predict novel HPV-human PPIs.

Conclusions:

  • The developed method offers a superior approach for predicting heterogeneous PPIs, specifically virus-host interactions.
  • This method has the potential to advance our understanding of viral infections and disease mechanisms.