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Related Concept Videos

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.
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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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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Predicting functional co-occurrence probability in PPI networks via multi-level participation expectation.

Peng Wang1

  • 1Chongqing Key Laboratory of Intelligent Perception and Blockchain Technology, School of Artificial Intelligence, Chongqing Technology and Business University, Chongqing 400067, P. R. China.

Journal of Bioinformatics and Computational Biology
|April 21, 2026
PubMed
Summary

This study introduces the Functional co-Occurrence Probability Estimation (FOPE) framework, integrating protein-protein interaction networks and domain data. FOPE enhances protein function prediction accuracy across diverse biological categories and organisms.

Keywords:
Protein function annotationdomain informationmulti-source information fusionprotein–protein interaction network

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Accurate protein function annotation is crucial for understanding biological systems.
  • Existing methods often struggle to integrate diverse biological data effectively for comprehensive prediction.

Purpose of the Study:

  • To develop a novel multi-source biological information-fusion framework, Functional co-Occurrence Probability Estimation (FOPE).
  • To estimate functional co-occurrence probabilities by integrating Protein-Protein Interaction (PPI) network topology and protein domain information.
  • To model functional synergy between protein pairs using bidirectional functional participation.

Main Methods:

  • Developed the FOPE framework for multi-source information fusion.
  • Integrated PPI network topology and protein domain data.
  • Employed bidirectional functional participation modeling to quantify functional synergy.

Main Results:

  • FOPE achieved significant improvements in predicting Gene Ontology (GO) terms across Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories.
  • Average macro-Fmax improvements of 25.3% (BP), 19.3% (CC), and 20.9% (MF) were observed compared to existing methods.
  • Ablation studies highlighted the dominant role of domain information for MF and PPI network features for BP prediction, with combined integration yielding comprehensive performance.
  • Robustness tests demonstrated FOPE's stability and error-tolerance in predictions even with substantial noise in PPI networks.

Conclusions:

  • The FOPE framework offers a feasible and effective approach for protein function prediction through multi-source information fusion.
  • The method demonstrates broad applicability across different functional categories and multiple model organisms.
  • FOPE provides a computational pathway for exploring protein functional synergy relationships from a systems-level perspective.