Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Protein Networks02:26

Protein Networks

4.2K
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,...
4.2K
Protein Networks02:26

Protein Networks

2.5K
2.5K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.1K
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...
14.1K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

4.1K
4.1K
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

6.1K
Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
6.1K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

6.8K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
6.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Resistin, an adipocytokine, offers protection against acute myocardial infarction.

Journal of molecular and cellular cardiology·2007
Same author

Liquid chromatographic analysis of phosphoamino acids at femtomole level using chemical derivatization with N-hydroxysuccinimidyl fluorescein-O-acetate.

Analytica chimica acta·2007
Same author

6-oxy-(acetyl piperazine) fluorescein as a new fluorescent labeling reagent for free fatty acids in serum using high-performance liquid chromatography.

Journal of chromatography. A·2007
Same author

Synthesis and fluorescence properties of 5,7-diphenylquinoline and 2,5,7-triphenylquinoline derived from m-terphenylamine.

Molecules (Basel, Switzerland)·2007
Same author

[Metabolic engineering of terpenoids in plants].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology·2007
Same author

Hedgehog signaling in the murine melanoma microenvironment.

Angiogenesis·2007

Related Experiment Video

Updated: Nov 3, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.2K

Predicting Protein-Protein Interactions via Gated Graph Attention Signed Network.

Zhijie Xiang1, Weijia Gong1, Zehui Li1

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China.

Biomolecules
|June 2, 2021
PubMed
Summary

Accurate protein-protein interaction (PPI) prediction is vital. A new method, SN-GGAT, uses graph attention networks and gating mechanisms to effectively predict PPIs in signed networks, showing strong performance on biological datasets.

Keywords:
PPI signed networkattention mechanismgating mechanismlink sign predictionprotein–protein interactions (PPIs)

More Related Videos

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.7K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.9K

Related Experiment Videos

Last Updated: Nov 3, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.2K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.7K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.9K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Protein-protein interactions (PPIs) are fundamental to cellular processes, including signal transduction and pharmacogenomics.
  • Accurate prediction of PPIs is essential for understanding biological mechanisms and drug discovery.
  • Graph structures, particularly signed networks representing positive and negative interactions, offer a powerful framework for modeling PPIs.

Purpose of the Study:

  • To propose a novel method, Signed Network-Gated Graph Attention Network (SN-GGAT), for accurate protein-protein interaction prediction.
  • To leverage graph attention mechanisms and gating to effectively model complex relationships in signed protein-protein interaction networks.
  • To enhance the prediction of protein-protein interactions by incorporating higher-order relationships and interaction sign information.

Main Methods:

  • Applied the Graph Attention Network (GAT) concept to signed networks, where 'attention' denotes neighbor node weights for feature aggregation.
  • Introduced a gating mechanism combined with balance theory to capture high-order protein node relations and refine attention.
  • Developed an attention mechanism adhering to the principle: 'low-order high attention, high-order low attention, different signs opposite'.

Main Results:

  • Successfully predicted protein-protein interactions using the SN-GGAT method on both the Saccharomyces cerevisiae core dataset and the Human dataset.
  • Demonstrated that the proposed SN-GGAT method achieves strong competitiveness compared to existing approaches.
  • Validated the effectiveness of the gating mechanism and balance theory in improving attention for signed network link prediction.

Conclusions:

  • The SN-GGAT method provides a robust and competitive approach for predicting protein-protein interactions in signed networks.
  • The integration of graph attention and gating mechanisms effectively captures complex interaction patterns and higher-order relationships.
  • This work contributes to advancing computational methods for biological network analysis and drug discovery through improved PPI prediction.