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Related Concept Videos

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-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...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...

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  3. Ppigan: Prediction Of Protein-protein Interactions Using Generative Adversarial Networks.
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  3. Ppigan: Prediction Of Protein-protein Interactions Using Generative Adversarial Networks.

Related Experiment Video

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

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Xu Zhang1, Songyan Xue1, Jing Geng1

  • 1College of Information Engineering, Northwest A&F University, Yangling, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|June 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed PPIGAN, a novel method using conditional generative adversarial networks (CGANs) to build negative datasets for protein-protein interaction (PPI) prediction. This approach enhances prediction accuracy and generalization, outperforming existing models.

Keywords:
conditional generative adversarial networknegative datasetprotein sequenceprotein–protein interaction

More Related Videos

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

Related Experiment Videos

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

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

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning in Biology

Background:

  • Protein-protein interactions (PPIs) are crucial for understanding cellular mechanisms.
  • Accurate negative datasets are essential for evaluating PPI prediction models.
  • Current random sampling methods for negative dataset construction suffer from unstable prediction accuracy.

Purpose of the Study:

  • To address the limitations of random sampling in constructing negative datasets for PPI prediction.
  • To propose a novel method, PPIGAN, for generating high-quality negative samples.
  • To improve the accuracy and generalization ability of PPI prediction models.

Main Methods:

  • Developed PPIGAN, a method based on conditional generative adversarial networks (CGANs).
  • Utilized a generative network to create negative samples for PPI prediction.
  • Employed a competitive learning process between the generator and the PPI prediction model.

    • Main Results:

    • Achieved high prediction accuracy: 94.68% on yeast datasets and 98.22% on human datasets via 5-fold cross-validation.
    • Demonstrated superior or comparable performance against advanced models like PIPR, CNN, DeepTrio, and DeepFE.
    • Showcased enhanced model generalization ability and prediction accuracy through adversarial training.

    Conclusions:

    • PPIGAN offers an effective solution for constructing negative datasets in PPI prediction.
    • The proposed method significantly improves the accuracy and reliability of PPI prediction.
    • This work provides a valuable tool for researchers investigating molecular mechanisms through PPI analysis.