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...

You might also read

Related Articles

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

Sort by

Same author

Agnostic material classification using differential de Bruijn graphs of DNA imprints.

bioRxiv : the preprint server for biology·2026

Same author

Identifying membrane-bound transcriptional regulatory proteins from rare but evolutionarily conserved domain combinations.

Nucleic acids research·2026

Same author

Comparative proteomic profiling of receptor kinase signaling reveals key trafficking components enforcing plant stomatal development.

Science advances·2026

Same author

Validation and analysis of 12,000 AI-driven CAR-T designs in the <i>Bits to Binders</i> competitions.

bioRxiv : the preprint server for biology·2026

Same author

Protein abundance inference via expectation-maximization in fluorosequencing.

Bioinformatics advances·2026

Same author

La1: an evolutionarily conserved player in the Arabidopsis telomerase complex.

bioRxiv : the preprint server for biology·2026

Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026

Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026

Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026

Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026

Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026

Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 8, 2025

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

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

Published on: November 17, 2019

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction

Meghana Venkata Palukuri¹, Edward M Marcotte²

¹Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America.

|December 31, 2021

Summary

This summary is machine-generated.

Super.Complex is a novel pipeline for identifying protein complexes using supervised machine learning and parallel computing. It accurately detects protein assemblies in large networks, aiding in understanding cellular functions and disease mechanisms.

More Related Videos

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

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

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

Published on: October 19, 2021

Related Experiment Videos

Last Updated: Oct 8, 2025

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

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

Published on: November 17, 2019

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

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

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

Published on: October 19, 2021

Area of Science:

Computational biology
Bioinformatics
Systems biology

Background:

Protein complexes are crucial for cellular functions like gene regulation.
Existing community detection methods for protein-protein interaction networks have limitations in accuracy and scalability.
Supervised machine learning approaches for complex detection are often serial and can be improved.

Purpose of the Study:

To develop a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks.
To introduce new evaluation metrics for comparing detected and known protein communities.
To enhance the accuracy and scalability of protein complex identification.

Main Methods:

Developed Super.Complex, a distributed, supervised AutoML pipeline for overlapping community detection.
Employed an AutoML method to learn a community fitness function from known communities.
Utilized a heuristic local search algorithm and a parallel implementation for scalability.
Proposed three novel evaluation measures for community detection.

Main Results:

Super.Complex outperformed 10 other methods on a yeast protein-interaction network.
Identified 1,028 protein complexes in a human protein-interaction network.
Discovered 234 complexes linked to SARS-CoV-2, including 111 uncharacterized proteins.
Demonstrated generalizability and transferability of learned community characteristics.

Conclusions:

Super.Complex offers an accurate and scalable solution for identifying protein complexes.
The pipeline facilitates the discovery of disease-related protein assemblies and uncharacterized proteins.
The method is adaptable and can be improved by incorporating domain-specific features.