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-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
Protein Networks02:26

Protein Networks

3.6K
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,...
3.6K
Protein Networks02:26

Protein Networks

1.8K
1.8K
Proteomics01:33

Proteomics

7.4K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.4K

You might also read

Related Articles

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

Sort by
Same author

Enhancer-associated long non-coding RNA LEENE regulates endothelial nitric oxide synthase and endothelial function.

Nature communications·2018
Same author

Temperature Dependence of Raman-Active In-Plane E<sub>2g</sub> Phonons in Layered Graphene and h-BN Flakes.

Nanoscale research letters·2018
Same author

KRAS Dimerization Impacts MEK Inhibitor Sensitivity and Oncogenic Activity of Mutant KRAS.

Cell·2018
Same author

Targeting <i>HER2</i> Aberrations in Non-Small Cell Lung Cancer with Osimertinib.

Clinical cancer research : an official journal of the American Association for Cancer Research·2018
Same author

Ozone-triggered surface uptake and stress volatile emissions in Nicotiana tabacum 'Wisconsin'.

Journal of experimental botany·2018
Same author

Elevation of soybean seed oil content through selection for seed coat shininess.

Nature plants·2018

Related Experiment Video

Updated: Apr 24, 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

17.8K

Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.

Zhu-Hong You1, Shuai Li2, Xin Gao3

  • 1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China.

Biomed Research International
|September 13, 2014
PubMed
Summary

This study introduces a new computational method to improve protein-protein interaction (PPI) detection. By combining biosensor data with an advanced algorithm, it enhances accuracy and reduces costs in identifying crucial biological interactions.

More Related Videos

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

4.8K
Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
11:19

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

18.0K

Related Experiment Videos

Last Updated: Apr 24, 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

17.8K
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

4.8K
Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
11:19

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

18.0K

Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Protein-protein interactions (PPIs) are fundamental to cellular functions.
  • Biosensors offer high-throughput PPI identification but face challenges with cost and data accuracy (false positives/negatives).
  • Existing experimental methods for PPI detection are time-consuming and expensive.

Purpose of the Study:

  • To develop a novel computational model for accurate PPI detection.
  • To integrate biosensor-based PPI data with computational approaches.
  • To address the limitations of current high-throughput PPI identification methods.

Main Methods:

  • Developed a computational model integrating biosensor PPI data.
  • Utilized the extreme learning machine algorithm.
  • Employed a novel protein sequence descriptor for data representation.

Main Results:

  • Achieved 84.8% prediction accuracy on a large-scale human protein interaction dataset.
  • Demonstrated 84.08% sensitivity at 85.53% specificity.
  • Outperformed support vector machine (SVM) in comparative experiments.

Conclusions:

  • The proposed method shows significant promise for detecting novel PPIs.
  • This computational approach can effectively supplement biosensor-based PPI data.
  • The findings contribute to more efficient and accurate PPI detection in biological research.