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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 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.
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 26, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Classifying protein-protein interaction articles using word and syntactic features.

Sun Kim1, W John Wilbur

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.

BMC Bioinformatics
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method using word and syntactic features to identify protein-protein interactions (PPIs) in biomedical texts. The approach improves PPI article filtering and ranking, proving valuable for search engines even with limited training data.

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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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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Identifying protein-protein interactions (PPIs) is crucial for understanding protein function and biological networks.
  • Machine learning methods are used to mine PPI patterns from text, but descriptions are complex, hindering extraction.
  • Existing methods face challenges in accurately extracting PPI information from scientific literature.

Purpose of the Study:

  • To develop an effective machine learning approach for identifying protein-protein interactions (PPIs) from biomedical literature.
  • To improve the accuracy and efficiency of PPI extraction by utilizing both word and syntactic features.
  • To enhance PPI article filtering and ranking performance.

Main Methods:

  • A Priority Model was used for automatic gene name identification.
  • A dependency parser extracted grammatical relations from text.
  • A large margin classifier with Huber loss function was trained on extracted features for prediction.

Main Results:

  • The system achieved top rankings in the BioCreative III ACT evaluation.
  • Performance metrics included 89.15% accuracy, 61.42% F1 score, 0.55306 MCC, and 67.98% AUC iP/R.
  • Syntactic information significantly improved PPI ranking performance.

Conclusions:

  • Utilizing syntactic information enhances PPI article filtering and ranking.
  • The developed system demonstrates the value of grammatical relations for PPI article filtering, especially with limited training data.
  • While not a perfect annotation tool, the system is effective for PPI article search engines focusing on highly-ranked results.