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

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...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

You might also read

Related Articles

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

Sort by
Same author

Chemo-omic pipeline enables discovery of prion synaptotoxic pathways and inhibitory drugs.

PLoS pathogens·2026
Same author

Light-driven spatial proteomics: Photocatalytic strategies for mapping protein microenvironments.

Current opinion in chemical biology·2026
Same author

Decoding protein-phospholipid interaction networks in cancer: the role of acyl-chain remodeling.

RSC chemical biology·2026
Same author

circPDE4B downregulation triggers GEMIN5‑dependent translational stress response and autophagy to reduce MAPT pathology.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Correction: ToxoNet: A high confidence map of protein-protein interactions in Toxoplasma gondii.

PLoS computational biology·2026
Same author

Collagen-Bearing Exosomes from Breast Cancer-Associated Fibroblasts Promote T-cell Dysfunction.

Cancer research communications·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Predicting protein functions by relaxation labelling protein interaction network.

Pingzhao Hu1, Hui Jiang, Andrew Emili

  • 1Department of Computer Science and Engineering, York University, ON, Toronto, Canada. phu@cse.yorku.ca

BMC Bioinformatics
|February 4, 2010
PubMed
Summary
This summary is machine-generated.

Predicting protein functions is crucial. Our new network-based method improves accuracy by considering relationships between protein functions, outperforming previous approaches.

More Related Videos

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation
14:44

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Published on: September 24, 2012

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

Related Experiment Videos

Last Updated: Jun 16, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation
14:44

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Published on: September 24, 2012

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

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Assigning functions to uncharacterized proteins is a key challenge in the post-genomic era.
  • Protein functions are executed through interactions with other biomolecular units.
  • Studying protein interactions can reveal functions of unknown proteins, but existing methods often ignore functional term dependencies.

Purpose of the Study:

  • To develop a novel network-based protein function prediction method.
  • To incorporate the inter-relationship among functional labels into prediction.
  • To efficiently discover relevant non-local dependencies in protein function.

Main Methods:

  • Developed a new network-based protein function prediction approach.
  • Combined likelihood scores from local classifiers with a relaxation labeling technique.
  • Incorporated inter-relationships among functional labels and non-local dependencies.

Main Results:

  • The new method demonstrated superior prediction performance compared to a representative existing network-based method.
  • Evaluated performance using E. coli protein functional association networks.
  • The method effectively incorporated functional label dependencies.

Conclusions:

  • The developed method offers improved protein function prediction accuracy.
  • Highlights the significance of considering dependencies between functional terms for accurate prediction.
  • Provides new insights into network-based functional genomics.