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

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

13.9K
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...
13.9K
Conservation of Protein Domains02:26

Conservation of Protein Domains

3.9K
3.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

4.4K
4.4K
Protein Networks02:26

Protein Networks

4.4K
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,...
4.4K
Protein Networks02:26

Protein Networks

2.7K
2.7K

You might also read

Related Articles

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

Sort by
Same author

Advances in predicting T cell epitope recognition for cancer immunotherapy.

Nature cancer·2026
Same author

The anatomy of a jackstone: a novel morphometric analysis of a rare urinary calculus.

Urolithiasis·2026
Same author

Integrating experimental feedback improves generative models for biological sequences.

Nucleic acids research·2025
Same author

COVID-19 mRNA vaccine immune response to the addition of osteopathic manipulative treatment with lymphatic pumps: a randomized controlled trial.

Virus research·2025
Same author

Phage display enables machine learning discovery of cancer antigen-specific TCRs.

Science advances·2025
Same author

Tumor antigens preferentially derive from unmutated genomic sequences in melanoma and non-small cell lung cancer.

Nature cancer·2025

Related Experiment Video

Updated: Jan 5, 2026

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

2.5K

A multi-scale coevolutionary approach to predict interactions between protein domains.

Giancarlo Croce1, Thomas Gueudré2, Maria Virginia Ruiz Cuevas1

  • 1Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biologie computationnelle et quantitative-LCQB, Paris, France.

Plos Computational Biology
|October 22, 2019
PubMed
Summary

New computational methods predict protein interactions by analyzing coevolution across species and within protein sequences. This approach significantly improves accuracy in identifying unknown protein family relationships.

More Related Videos

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.6K
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

69.6K

Related Experiment Videos

Last Updated: Jan 5, 2026

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

2.5K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.6K
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

69.6K

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Genomics

Background:

  • Protein interactions are fundamental to biological processes.
  • Genomic data offers vast resources for studying protein variability and interactions.
  • Existing methods for predicting protein interactions have limitations.

Purpose of the Study:

  • To develop and validate a novel computational approach for predicting protein interactions.
  • To leverage multi-scale coevolutionary signals for enhanced prediction accuracy.
  • To identify previously unknown direct interactions between protein families.

Main Methods:

  • Introduction of 'direct phyletic couplings' based on global statistical models of phylogenetic profiles.
  • Integration of direct coupling analysis with inter-protein residue-residue coevolution.
  • Comparison of the new method against traditional correlation-based approaches like phylogenetic profiling.

Main Results:

  • Direct phyletic couplings significantly increase the accuracy of predicting related protein domains (80% vs. 30-50% positives).
  • Multi-scale coevolutionary evidence supports direct, yet previously unknown, interactions between protein families.
  • Identified negative phyletic couplings reveal alternative functional solutions and convergent evolution.

Conclusions:

  • Global statistical modeling offers powerful tools for genome-wide coevolutionary analysis.
  • The developed methods provide biologically sensible and experimentally testable predictions of protein interactions.
  • This work expands the application of coevolutionary analysis beyond individual protein complexes.