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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...
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 Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.

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Related Experiment Video

Updated: May 22, 2026

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

Graph spectral analysis of protein interaction network evolution.

Thomas Thorne1, Michael P H Stumpf

  • 1Centre of Integrative Systems Biology and Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. thomas.thorne@imperial.ac.uk

Journal of the Royal Society, Interface
|May 4, 2012
PubMed
Summary
This summary is machine-generated.

We analyzed protein interaction networks using a novel Bayesian approach. Our findings favor a duplication-divergence model for network evolution, improving our understanding of biological systems.

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

Published on: March 3, 2015

Related Experiment Videos

Last Updated: May 22, 2026

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

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
  • Network Science
  • Bioinformatics

Background:

  • Protein interaction networks are crucial for understanding cellular processes.
  • Existing models of network evolution often overlook sampling biases and use limited statistical measures.
  • Accurate modeling of network growth is essential for inferring biological properties.

Purpose of the Study:

  • To develop and apply a Bayesian framework for analyzing protein interaction network evolution.
  • To compare various network growth models, including duplication and scale-free models.
  • To account for dataset incompleteness and estimate network properties from sampled data.

Main Methods:

  • Bayesian inference with Approximate Bayesian Computation and Sequential Monte Carlo (ABC-SMC).
  • Utilizing graph spectra for a more natural representation of network data compared to degree distributions.
  • Incorporating sampling effects to correct for incomplete biological datasets.

Main Results:

  • A preference for the duplication-divergence with linear preferential attachment model was identified across most datasets.
  • The developed method effectively handles varying degrees of sampling and dataset incompleteness.
  • Demonstrated the ability to perform multi-model inference for estimating full network parameters.

Conclusions:

  • The duplication-divergence model provides a robust explanation for the evolution of many protein interaction networks.
  • The proposed Bayesian framework with graph spectra and sampling correction offers a powerful tool for network analysis.
  • This approach enhances the estimation of biological network properties from incomplete empirical data.