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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...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Protein function prediction with the shortest path in functional linkage graph and boosting.

Xing-Ming Zhao1, Luonan Chen, Kazuyuki Aihara

  • 1Institute of Systems Biology, Shanghai University, Shanghai 200444, China. xm_zhao@shu.edu.cn

International Journal of Bioinformatics Research and Applications
|November 15, 2008
PubMed
Summary

This study introduces a novel protein function prediction method using a weighted functional linkage graph. It effectively handles data errors and improves accuracy for identifying protein biological functions.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate protein function annotation is crucial in the post-genomic era.
  • High-throughput technologies provide data for protein function prediction, but data errors are a challenge.
  • Existing methods struggle to effectively handle errors in high-throughput data.

Purpose of the Study:

  • To develop a new technique for protein function prediction that addresses data errors.
  • To improve the accuracy of predicting biological functions for unknown proteins.
  • To leverage diverse biological data for enhanced functional linkage analysis.

Main Methods:

  • Constructing a weighted functional linkage graph using protein-protein interaction, complex, and gene expression data.
  • Identifying functional links between proteins by finding shortest paths within the graph.
  • Utilizing support vector machines (SVM) and a boosting algorithm for function prediction based on identified links.

Main Results:

  • The proposed method effectively captures functional links among proteins.
  • Support vector machines combined with boosting algorithm demonstrate improved prediction accuracy.
  • Experimental results on yeast genes show promising outcomes and validate the method's efficiency.

Conclusions:

  • The developed weighted functional linkage graph approach offers an effective solution for protein function prediction.
  • The method successfully integrates and utilizes multiple data sources while mitigating data errors.
  • This technique provides a robust and accurate means for annotating protein biological functions.