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Protein Networks

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 Networks

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 Interfaces

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...

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Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

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

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

Published on: October 19, 2021

A structural approach for finding functional modules from large biological networks.

Mutlu Mete¹, Fusheng Tang, Xiaowei Xu

¹Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas, USA. mxmete@ualr.edu

BMC Bioinformatics

|September 20, 2008

Summary

This summary is machine-generated.

SCAN efficiently identifies functional modules in biological networks, offering a faster and more accurate approach than existing methods for analyzing complex protein-protein interaction data.

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Area of Science:

Systems Biology
Bioinformatics
Network Science

Background:

Biological systems function through complex networks of interacting components.
Protein-protein interaction (PPI) networks are crucial for cellular functions.
Identifying functional modules in large biological networks is a key goal of systems biology.

Purpose of the Study:

To develop an efficient and accurate computational method for identifying functional modules, hubs, and outliers in complex biological networks.
To classify nodes within networks based on their structural roles.
To address limitations of existing clustering methods in terms of accuracy and speed.

Main Methods:

Developed the Structural Clustering Algorithm for Networks (SCAN).
Applied SCAN to the protein-protein interaction network of Saccharomyces cerevisiae.
Validated clustering results by comparing with known protein functions and Gene Ontology (GO) terms.
Compared SCAN's performance against the CNM (Clauset-Newman-Moore) algorithm.

Main Results:

SCAN efficiently identifies functional modules, hubs, and outliers in complex networks.
Demonstrated high purity of predicted functional modules in the yeast PPI network.
Achieved linear running time, representing a significant speed improvement over existing methods.
SCAN outperformed CNM in accurately partitioning networks and identifying functionally relevant groups.

Conclusions:

SCAN is a highly accurate and efficient algorithm for network module detection.
The algorithm successfully identifies functionally coherent groups of proteins.
SCAN provides a superior alternative to existing methods like CNM for analyzing large-scale biological networks.