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Related Concept Videos

Protein Networks02:26

Protein Networks

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,...
Protein Networks02:26

Protein Networks

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,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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 polypeptide...
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...

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Updated: Jun 16, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Functional characterization and topological modularity of molecular interaction networks.

Jayesh Pandey1, Mehmet Koyutürk, Ananth Grama

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, USA. jpandey@cs.purdue.edu

BMC Bioinformatics
|February 4, 2010
PubMed
Summary

Functional similarity strongly correlates with network proximity in biomolecular interaction networks. Novel measures improve robustness to noisy data, outperforming aggregated pair-wise analyses for functional characterization.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Network Science

Background:

  • Analyzing molecular interaction networks for functional insights is challenging due to noisy and incomplete data.
  • Network-based methods infer functional associations by examining proximity of interacting molecules.

Purpose of the Study:

  • To formally investigate the relationship between functional coherence and topological proximity in biological networks.
  • To develop robust measures for assessing biomolecular set coherence and topological proximity.
  • To evaluate methods using diverse interaction network data.

Main Methods:

  • Comparative investigation of functional coherence and topological proximity.
  • Development of novel, robust measures for topological proximity.
  • Assessment of biomolecular set coherence considering functional specificity.

Main Results:

  • Strong correlation found between functional similarity and topological proximity across network types.
  • Domain interaction networks (DDIs) show higher correlation than protein-interaction networks (PPIs).
  • Set-based coherence measures are superior to aggregated pair-wise measures.

Conclusions:

  • Random-walk based topological proximity measures are well-suited for interaction data.
  • Validated methods on diverse PPIs, DDIs, and known biologically related molecule sets.
  • Findings enhance functional characterization of biomolecular networks.