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

4.1K
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,...
4.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.8K
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...
13.8K
Nuclear Protein Sorting01:34

Nuclear Protein Sorting

4.8K
Nuclear protein sorting is the selective trafficking of histones, polymerases, gene regulatory proteins into the nucleus and exporting RNAs and ribosomes to the cytosol. It is a tightly controlled process that regulates gene expression within a cell.
Proteins targeted to the nucleus carry nuclear localization signals or NLS recognized by import receptors in the cytosol. Similarly, proteins with nuclear export signals are recognized by export receptors. Import and export receptors are...
4.8K
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

94
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...
94
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

3.9K
3.9K

You might also read

Related Articles

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

Sort by
Same author

Genetic analysis of childhood-onset dystonia-28 caused by a variant (c.5076G>A) in the <i>KMT2B</i> gene.

Global medical genetics·2026
Same author

Regime shifts and climate responses of alpine grasslands in Gannan Prefecture, China.

Frontiers in plant science·2026
Same author

Identification of the MYH6 c.804G>C Synonymous Variant Causing Exon Skipping in a Hypertrophic Cardiomyopathy Family.

Molecular genetics & genomic medicine·2026
Same author

A network analysis of adolescent nonsuicidal self-injury from the perspective of an invalidating family environment.

Psychiatry research·2026
Same author

A TIGIT nanotrapping-guided STING-activatable immunometabolic strategy overcomes innate immune silence and T cell exhaustion in breast cancer.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

HRS-7535 for Type 2 Diabetes Inadequately Controlled With Metformin: A Randomized Clinical Trial.

JAMA network open·2026

Related Experiment Video

Updated: Sep 19, 2025

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

8.6K

NPI-HetGNN: A Prediction Model of ncRNA-Protein Interactions Based on Heterogeneous Graph Neural Networks.

Fan Zhang1,2, Chaoyang Liu2, Binjie Wang1

  • 1Radiology Department, Huaihe Hospital of Henan University, Kaifeng, 475004, China.

Interdisciplinary Sciences, Computational Life Sciences
|June 2, 2025
PubMed
Summary

This study introduces NPI-HetGNN, a novel computational model for predicting non-coding RNA-protein interactions (NPI). The model leverages heterogeneous graph neural networks to accurately identify these crucial biological interactions, advancing epigenetic research.

Keywords:
Heterogeneous graph neural networkNon-coding RNAProteinncRNA-protein interaction

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

919
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K

Related Experiment Videos

Last Updated: Sep 19, 2025

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

8.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

919
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Non-coding RNAs (ncRNAs) are key epigenetic regulators of gene expression.
  • Understanding ncRNA-protein interactions (NPI) is vital for exploring biological functions and diseases.
  • Traditional NPI experimental methods are resource-intensive; computational approaches offer an efficient alternative.

Purpose of the Study:

  • To develop a novel computational model, NPI-HetGNN, for predicting ncRNA-protein interactions (NPI).
  • To utilize heterogeneous graph neural networks (GNNs) for integrating diverse biological data and network topology.

Main Methods:

  • Constructing initial features by integrating ncRNA sequence properties, protein data, and heterogeneous network topology.
  • Employing metapath walking to aggregate semantic information from multilevel homogeneous subgraphs.
  • Fusing homogeneous node information within subgraph metapaths and incorporating an energy-constrained self-attention module for enhanced feature extraction.

Main Results:

  • The NPI-HetGNN model demonstrated high performance on four benchmark datasets.
  • Ablation experiments validated the model's design, comprehensiveness, and effectiveness.
  • NPI-HetGNN outperformed six state-of-the-art methods in NPI prediction.

Conclusions:

  • NPI-HetGNN provides a robust and efficient computational method for predicting ncRNA-protein interactions.
  • The model's success highlights the potential of heterogeneous graph neural networks in bioinformatics.
  • This approach offers a valuable tool for advancing research in epigenetics and related diseases.