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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...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...

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Related Experiment Video

Updated: Jul 2, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Large-scale Protein-Protein Interaction prediction using novel kernel methods.

Xue-Wen Chen1, Bing Han, Jianwen Fang

  • 1Electrical Engineering and Computer Science Department, The University of Kansas, Lawrence, KS 66045, USA. xwchen@ku.edu

International Journal of Data Mining and Bioinformatics
|September 5, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new domain-based kernel method for predicting protein-protein interactions (PPIs). The novel approach effectively identifies molecular mechanisms and cellular properties by analyzing protein similarities.

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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

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Related Experiment Videos

Last Updated: Jul 2, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

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

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
08:38

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Published on: March 3, 2015

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Understanding protein-protein interactions (PPIs) is crucial for deciphering cellular mechanisms.
  • Existing in silico methods for PPI prediction face challenges in accuracy and scope.

Purpose of the Study:

  • To develop a novel domain-based kernel method for predicting protein-protein interactions (PPIs).
  • To enhance the accuracy and applicability of computational methods in PPI research.

Main Methods:

  • Development of a new kernel function measuring protein pair similarity using a novel feature representation.
  • Application of the developed method to a large-scale PPI database.
  • Evaluation of cross-species PPI prediction and the impact of negative sample size.

Main Results:

  • The proposed domain-based kernel method demonstrates significant effectiveness in predicting PPIs.
  • Experimental results validate the method's performance on a large dataset.
  • Insights into cross-species prediction and negative sample influence were obtained.

Conclusions:

  • The novel domain-based kernel method offers a powerful tool for PPI prediction.
  • This approach advances the field of computational biology by improving the understanding of molecular interactions.
  • The study addresses fundamental challenges in in silico PPI prediction.