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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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Updated: May 29, 2026

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

Using coevolution to predict protein-protein interactions.

Gregory W Clark1, Vaqaar-Un-Nisa Dar, Alexandr Bezginov

  • 1Department of Medical Biophysics, University of Toronto, Campbell Family Institute for Cancer Research, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.

Methods in Molecular Biology (Clifton, N.J.)
|August 31, 2011
PubMed
Summary
This summary is machine-generated.

Bioinformatic methods like MMM accurately predict protein-protein interactions (PPI) by analyzing coevolutionary rates. This computational approach rivals experimental techniques and enhances the discovery of protein complexes.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Bioinformatic methods for predicting protein-protein interactions (PPI) are emerging but lack mechanistic understanding.
  • Coevolutionary analysis is a promising computational approach for PPI prediction.

Purpose of the Study:

  • To evaluate a new rate-based coevolutionary method (MMM) for predicting PPI.
  • To compare the accuracy of coevolutionary methods with existing techniques.
  • To investigate the applicability of coevolutionary analysis at the protein domain level.

Main Methods:

  • Development and application of a novel rate-based coevolutionary method, MMM.
  • Benchmarking against gold-standard PPI databases.
  • Comparison with methods based on abundance ratios and coexpression.
  • Analysis of coevolutionary signals at the protein domain level.

Main Results:

  • The MMM method effectively identifies obligate interacting proteins forming complexes, aligning with experimental findings.
  • MMM demonstrates superior accuracy compared to abundance ratio methods.
  • Coevolutionary rates show potential as a better predictor of interacting proteins than coexpression.
  • Detecting coevolution at the protein domain level remains challenging, even with MMM.

Conclusions:

  • Coevolutionary methods, including MMM, are reliable for predicting PPI, either independently or to guide experimental approaches like co-immunoprecipitation.
  • Correlated evolutionary rates offer a robust alternative to coexpression for PPI prediction.
  • Further research is needed to refine coevolutionary analysis for multi-domain proteins.