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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...
Conserved Binding Sites01:49

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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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

Biological network motif detection: principles and practice.

Elisabeth Wong1, Brittany Baur, Saad Quader

  • 1Bowdoin College, Brunswick, Maine, USA.

Briefings in Bioinformatics
|March 8, 2012
PubMed
Summary
This summary is machine-generated.

Network motifs are fundamental building blocks in complex biological networks. This study explores their significance, challenges in detection, and strategies for identifying these crucial sub-structures.

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Peptide-based Identification of Functional Motifs and their Binding Partners
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Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Area of Science:

  • Systems Biology
  • Bioinformatics
  • Network Science

Background:

  • Network motifs are statistically overrepresented sub-structures in complex networks.
  • They are recognized as fundamental building blocks of biological networks.
  • Studying network motifs can provide insights into critical biological questions.

Purpose of the Study:

  • To discuss the biological significance of network motifs.
  • To outline the motivation for solving the network motif-finding problem.
  • To present strategies for addressing various aspects of motif detection.

Main Methods:

  • A classification scheme is proposed to analyze existing motif-finding algorithms.
  • Strengths and weaknesses of different algorithms are evaluated.
  • Comparative studies from literature are used to discuss experimental results.

Main Results:

  • The exponential increase in sub-graph possibilities poses a significant challenge.
  • The lack of polynomial-time algorithms for graph topological equivalence complicates detection.
  • Analysis reveals varying performance characteristics of existing algorithms.

Conclusions:

  • Further research is needed to overcome computational challenges in motif detection.
  • Developing efficient algorithms is crucial for advancing biological network analysis.
  • Understanding network motifs is key to deciphering complex biological systems.