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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...
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...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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Related Experiment Video

Updated: Jun 3, 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

Predicting protein phenotypes based on protein-protein interaction network.

Lele Hu1, Tao Huang, Xiao-Jun Liu

  • 1Institute of Systems Biology, Shanghai University, Shanghai, China.

Plos One
|March 23, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a computational method to predict protein phenotypes in yeast using protein-protein interaction networks. The new approach accurately identifies potential protein functions, aiding genetic research.

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Related Experiment Videos

Last Updated: Jun 3, 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

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

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Published on: March 3, 2015

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Area of Science:

  • Genetics
  • Computational Biology
  • Systems Biology

Background:

  • Identifying protein-associated phenotypes is crucial in modern genetics.
  • Multifactorial traits often arise from the combined effects of numerous proteins.
  • Computational methods offer an alternative to high-throughput assays for phenotype prediction.

Purpose of the Study:

  • To develop a novel computational method for predicting protein phenotypes in yeast.
  • To leverage protein-protein interaction networks for phenotype prediction.
  • To generate and rank a series of potential phenotypes for proteins.

Main Methods:

  • Utilized a protein-protein interaction network in yeast.
  • Developed a ranking system based on a 'tethering potential score'.
  • Employed a Jackknife test for validation on 1,267 proteins.

Main Results:

  • Achieved a first-order prediction accuracy of 65.4% for protein phenotypes.
  • This accuracy significantly surpasses random guessing (15.4%).
  • The top 3 predicted phenotypes encompassed all true phenotypes for 70.6% of proteins.

Conclusions:

  • The predicted candidate phenotypes offer valuable insights for experimental validation.
  • The developed method is adaptable for predicting protein phenotypes in other organisms.
  • This computational approach enhances the understanding of protein function in biological systems.