Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Networks

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 Interfaces

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...

You might also read

Related Articles

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

Sort by

Same author

Simulations of allosteric motions in the zinc sensor CzrA.

Journal of the American Chemical Society·2011

Same author

[Platelet parameters and platelet Toll-like receptor 4 (TLR4) expression in patients with sepsis, and the effect of a joint treatment-plan integrating traditional Chinese and western medicine: a clinical study].

Zhongguo wei zhong bing ji jiu yi xue = Chinese critical care medicine = Zhongguo weizhongbing jijiuyixue·2011

Same author

A novel kernel Fisher discriminant analysis: constructing informative kernel by decision tree ensemble for metabolomics data analysis.

Analytica chimica acta·2011

Same author

Anterior debridement and reconstruction via thoracoscopy-assisted mini-open approach for the treatment of thoracic spinal tuberculosis: minimum 5-year follow-up.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2011

Same author

[A family-based association study of FXYD6 gene polymorphisms and schizophrenia].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics·2011

Same author

Prenatal diagnosis of penoscrotal transposition with 2- and 3-dimensional ultrasonography.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2011

Search research articles

Related Experiment Video

Updated: Jun 12, 2026

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

Inferring protein-protein interactions using a hybrid genetic algorithm/support vector machine method.

Bing Wang¹, Peng Chen, Jun Zhang

¹School of Electrical & Information, Anhui University of Technology, Ma'anshan, Anhui, 243002, China. wangbing@ustc.edu

Protein and Peptide Letters

|June 1, 2010

Summary

This summary is machine-generated.

This study introduces a novel hybrid method combining Genetic Algorithms (GA) and Support Vector Machines (SVM) to predict protein-protein interactions based on protein domains. The approach demonstrates high accuracy in identifying these crucial biological interactions.

More Related Videos

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Published on: March 3, 2015

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

Related Experiment Videos

Last Updated: Jun 12, 2026

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Published on: March 3, 2015

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

Area of Science:

Computational Biology
Bioinformatics
Molecular Biology

Background:

Understanding protein-protein interactions (PPIs) is fundamental for deciphering biological systems, processes, and enabling targeted mutant design.
Protein domains, as distinct structural and functional units, form the basis for characterizing protein composition and interactions.

Purpose of the Study:

To develop and evaluate a novel hybrid method for predicting protein-protein interactions.
To leverage protein domain composition and relationships for enhanced interaction prediction.

Main Methods:

A hybrid approach integrating Genetic Algorithm (GA) for domain composition transformation and Support Vector Machines (SVM) for prediction was employed.
Proteins were represented by their domain compositions, accounting for domain duplication effects.
GA was used to simulate and optimize domain combinations, with SVM identifying the optimal predictor.

Main Results:

The proposed GA/SVM method achieved significant prediction performance.
Achieved metrics include 0.85 sensitivity, 0.90 specificity, and 0.88 accuracy.
Demonstrated superior effectiveness and efficiency compared to random prediction methods.

Conclusions:

The hybrid GA/SVM method offers an effective and efficient strategy for predicting protein-protein interactions.
This domain-centric approach provides valuable insights into the mechanisms underlying protein interactions.