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Related Concept Videos

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 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 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

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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 computational model for predicting protein interactions based on multidomain collaboration.

Woo-Hyuk Jang1, Suk-Hoon Jung, Dong-Soo Han

  • 1Department of Information and Communications Engineering, Korea Advanced Institute of Science and Technology, Kaist, 335 Gwahak-ro, Yuseong-gu, Daejeon 305-701, Korea. torajim@kaist.ac.kr

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|April 18, 2012
PubMed
Summary
This summary is machine-generated.

A new computational model improves protein-protein interaction (PPI) prediction by analyzing domain cohesion within proteins and domain combination coupling between proteins. This approach accurately identifies interacting domain pairs, enhancing prediction accuracy for complex protein interactions.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Protein interaction networks

Background:

  • Existing domain-based computational models for protein-protein interaction (PPI) prediction have limitations in specifying interacting domain pairs.
  • Conventional methods infer interactions from known protein sets but lack detail on specific domain or domain combination (DC) interactions.

Purpose of the Study:

  • To develop a more comprehensive computational model for predicting PPIs with enhanced accuracy.
  • To incorporate intraprotein domain cohesion and interprotein DC coupling for improved prediction.
  • To identify the primary interacting DC pair in predicted protein interactions.

Main Methods:

  • Developed a computational model integrating intraprotein domain cohesion and interprotein DC coupling.
  • Incorporated a method for identifying the primary interacting DC pair.
  • Evaluated the model's performance in PPI prediction accuracy.

Main Results:

  • The developed model demonstrated apparent improvement in PPI prediction accuracy.
  • The primary interacting DC pair identification was validated, particularly for multidomain protein interactions.
  • Intraprotein domain cohesion was found to be meaningful for improving domain-based PPI prediction accuracy.

Conclusions:

  • Intraprotein domain cohesion enhances the accuracy of domain-based PPI prediction.
  • The prediction model effectively identifies primary interacting DC pairs, especially in multidomain interactions.
  • A hybrid approach combining intra- and interdomain interaction information yields more accurate PPI predictions.