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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,...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
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: May 13, 2026

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

Published on: January 26, 2024

A matrix based algorithm for Protein-Protein Interaction prediction using Domain-Domain Associations.

S Binny Priya1, Subhojit Saha, Ramesh Anishetty

  • 1Department of Biotechnology, Anna University, Chennai 600025, India.

Journal of Theoretical Biology
|March 12, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel matrix-based method to predict protein-protein interactions (PPI) using "all against all" high-throughput data and Pfam domain composition. The approach successfully infers domain-domain associations (DDA) and predicts new PPI with approximately 70% accuracy.

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Last Updated: May 13, 2026

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

Background:

  • Protein-protein interactions (PPI) are fundamental to cellular functions.
  • High-throughput experimental data offers insights into both interacting and non-interacting proteins.
  • Assessing domain associations in interacting proteins is crucial for understanding biological mechanisms.

Purpose of the Study:

  • To develop a computational method for predicting PPI using 'all against all' datasets.
  • To infer positive and negative domain-domain associations (DDA).
  • To leverage Pfam domain composition for PPI prediction.

Main Methods:

  • Utilized 'all against all' high-throughput PPI datasets.
  • Employed Pfam domain composition of proteins.
  • Developed a matrix-based method to infer domain-domain associations (DDA).

Main Results:

  • Generated over a million domain association values.
  • Achieved a prediction accuracy of approximately 70% across multiple databases and a curated set.
  • Demonstrated sensitivity of 68.1% and specificity of 65.3% on a test set.

Conclusions:

  • The developed matrix-based method effectively predicts protein-protein interactions.
  • The inferred domain-domain associations can guide experimental studies.
  • This approach has potential applications in understanding host-pathogen interactions.