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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,...
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...
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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Updated: Jun 23, 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

Predicting protein-protein interactions from sequence using correlation coefficient and high-quality interaction

Ming-Guang Shi1, Jun-Feng Xia, Xue-Ling Li

  • 1Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, 230031 Hefei, China.

Amino Acids
|April 24, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method for predicting protein-protein interactions (PPIs) using correlation coefficient (CC) transformation and support vector machine (SVM). The developed sequence-based approach achieved high accuracy in identifying protein interactions.

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Protein-protein interactions (PPIs) are fundamental to cellular functions and proteome machinery.
  • High-throughput experimental data for PPIs are often incomplete and contain noise, necessitating computational prediction methods.
  • Developing accurate computational tools and high-quality datasets is crucial for reliable PPI prediction.

Purpose of the Study:

  • To develop and evaluate a novel sequence-based computational method for predicting protein-protein interactions.
  • To combine correlation coefficient (CC) transformation with support vector machine (SVM) for enhanced PPI prediction accuracy.
  • To objectively assess the method's performance using curated gold standard positive and negative datasets.

Main Methods:

  • A sequence-based prediction method was developed by integrating correlation coefficient (CC) transformation with a support vector machine (SVM) model.
  • CC transformation was employed to capture neighboring effects within protein sequences and quantify the correlation between sequences.
  • Performance was evaluated using the MIPS Core (positive) and GO-NEG (negative) datasets for yeast Saccharomyces cerevisiae.

Main Results:

  • The proposed method, combining CC transformation and SVM, demonstrated superior performance in predicting protein-protein interactions.
  • The model achieved a high accuracy of 87.94% when evaluated on the gold standard positive and negative datasets.
  • The CC transformation effectively considered protein sequence neighborhood effects and described the correlation level between protein sequences.

Conclusions:

  • The integrated CC transformation and SVM approach is an effective computational strategy for predicting protein-protein interactions.
  • This method offers a valuable tool for overcoming the limitations of experimental PPI data, such as incompleteness and noise.
  • The developed method provides a reliable and accurate means to advance the understanding of cellular functions and proteome organization.