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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.
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The Ras Gene02:38

The Ras Gene

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The Ras-gene-encoded proteins are regulators of signaling pathways controlling cell proliferation, differentiation, or cell survival. The Ras-gene family in humans constitutes three primary members—the HRas, NRas, and KRas. These genes code for four functionally distinct yet closely related proteins—the HRas, NRas, KRas4A, and KRas4B. The involvement of mutant Ras genes in human cancer was first discovered in 1982 and is among the most common causes of human tumorigenesis.
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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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Ras and Rho are small monomeric GTPases that act downstream of receptor tyrosine kinase (RTK) and regulate various cellular processes. These GTPases switch between active and inactive states by binding to guanine nucleotides.
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Related Experiment Video

Updated: Sep 16, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Advancing edge-based clustering and graph embedding for biological network analysis: a case study in RASopathies.

Federico García-Criado1, Pedro Seoane1,2,3, Elena Rojano1,2,3

  • 1Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain.

Briefings in Bioinformatics
|July 7, 2025
PubMed
Summary

We developed a novel network embedding method that integrates Hierarchical Link Clustering (HLC) to better represent complex biological networks. This approach enhances the discovery of functional relationships and potential disease gene candidates in protein-protein interaction (PPI) networks.

Keywords:
HLCRASopathiesnetwork embeddingoverlap communityprotein-protein interaction

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Area of Science:

  • Computational Biology
  • Network Science
  • Bioinformatics

Background:

  • Protein-protein interaction (PPI) networks are crucial for understanding biological processes.
  • Existing network representation methods often fail to capture the overlapping modular structure inherent in biological systems.
  • Accurate network representations are essential for biological interpretability and predictive power.

Purpose of the Study:

  • To propose a novel network embedding strategy that improves biological interpretability and predictive power by addressing the limitations of traditional clustering methods.
  • To integrate Hierarchical Link Clustering (HLC) into a network embedding workflow for large, weighted, undirected networks.
  • To enhance the representation of biological pathways and identify novel gene candidates for diseases like RASopathies.

Main Methods:

  • Developed optimized Hierarchical Link Clustering (HLC) implementations in Python and R for improved accuracy and scalability.
  • Integrated HLC into a network embedding workflow by restricting random walks to HLC-defined communities.
  • Applied the cluster embedding workflow to analyze the human PPI network using Reactome pathways and investigate RASopathies.

Main Results:

  • The optimized HLC implementations demonstrated superior clustering accuracy and scalability compared to existing methods.
  • Restricting random walks to HLC-defined communities improved the representation of biological pathways within the human PPI network.
  • The cluster embedding workflow successfully identified potential novel gene candidates associated with RASopathies, including Noonan and Costello syndrome.

Conclusions:

  • Integrating Hierarchical Link Clustering (HLC) with network embedding provides a more biologically interpretable and predictive representation of complex PPI networks.
  • This approach enhances the analysis of biological pathways and facilitates the discovery of novel disease-associated genes.
  • The developed HLC implementations and embedding workflow offer valuable tools for network biology research.