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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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Related Experiment Video

Updated: Jun 2, 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

Protein interaction sentence detection using multiple semantic kernels.

Tamara Polajnar1, Theodoros Damoulas, Mark Girolami

  • 1School of Computing Science, University of Glasgow, Glasgow, UK. tamara@dcs.gla.ac.uk.

Journal of Biomedical Semantics
|May 17, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach using semantic kernels to improve the detection of protein-protein interactions (PPIs) in biomedical texts. Combining these kernels significantly enhances recognition rates for PPI sentences.

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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

Related Experiment Videos

Last Updated: Jun 2, 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

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

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:

  • Protein-protein interaction (PPI) sentence detection is a complex pattern recognition challenge.
  • Current methods often rely on Support Vector Machines (SVMs) and kernel classification.
  • A novel data integration approach using semantic kernels is proposed.

Purpose of the Study:

  • To investigate the effectiveness of combining semantic kernels for improved PPI sentence detection.
  • To explore a probabilistic analogue to SVMs for classification tasks.
  • To assess if semantic information from unlabeled text enhances short text classification.

Main Methods:

  • Utilized semantic kernels derived from statistical information in large unlabeled biomedical texts.
  • Employed lexical semantic models to construct word-based semantic kernels.
  • Fused multiple semantic kernels into a composite classification space.
  • Applied a probabilistic multiple kernel learning method.

Main Results:

  • Combinations of semantic kernels demonstrated statistically significant improvements in recognition rates.
  • Receiver Operating Characteristic (ROC) scores were enhanced compared to standard Gaussian kernels.
  • The kernel composition method enabled automatic inference of the most discriminative kernels.

Conclusions:

  • Semantic information from unlabeled text and its combinations are valuable for classifying PPI sentences.
  • This approach represents a foundational step in evaluating semantic kernels for PPI detection.
  • The modular method supports various feature types, kernels, and semantic models for protein interaction extraction.