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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 21, 2026

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

Estimating support for protein-protein interaction data with applications to function prediction.

Erliang Zeng1, Chris Ding, Giri Narasimhan

  • 1Bioinformatics Research Group, School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA. ezeng001@cs.fiu.edu

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference
|August 1, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using Gene Ontology information to assess protein-protein interaction (PPI) data reliability. Semantic similarity effectively distinguishes true interactions from false positives, improving function prediction.

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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

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Last Updated: Jun 21, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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Published on: November 3, 2011

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

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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:

  • Cellular processes rely on protein-protein interactions (PPIs).
  • High-throughput methods generate extensive PPI data but often include false positives.
  • Accurate PPI data is crucial for understanding protein functions and networks.

Purpose of the Study:

  • To develop a novel method for evaluating the reliability of high-throughput PPI data.
  • To leverage Gene Ontology (GO) information and semantic similarity for assessing PPI support.
  • To improve protein function prediction using a more reliable PPI dataset.

Main Methods:

  • Computed semantic similarity between genes using GO information and Resnik's formula.
  • Modeled PPI data as a mixture model based on semantic similarity scores.
  • Utilized semantic similarity as a metric to quantify support for PPIs.

Main Results:

  • True PPIs exhibit higher semantic similarity support than false positives.
  • The proposed method effectively models PPI data, distinguishing true interactions.
  • New function prediction approaches based on this metric outperform existing methods.

Conclusions:

  • Semantic similarity derived from Gene Ontology is a robust metric for evaluating PPI data quality.
  • This approach enhances the reliability of PPI networks for biological analysis.
  • The developed method offers improved accuracy in predicting protein functions.