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...

Microtubule Associated Proteins (MAPs)

Microtubule Associated Proteins (MAPs)

Microtubule function and architecture are regulated by an array of specialized proteins called microtubule-associated proteins or MAPs. These proteins are widespread across different organisms and have conserved protein motifs, like the multi-TOG domain for tubulin binding found in the CLASP family of MAPs. Some MAPs are lineage-specific based on their conserved domains. Their functions depend upon the cytoskeletal architecture and cell type they are located within. In-plant cells, a specific...

You might also read

Related Articles

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

Sort by

Same author

Certain vs. uncertain actionable secondary findings in a cohort of 500 Lebanese participants: What to report to the patient?

PloS one·2025

Same author

Predicting Consanguinity Rates from Exome Sequencing Data in the Lebanese Population.

The Journal of molecular diagnostics : JMD·2024

Same author

Ant colony optimization for the identification of dysregulated gene subnetworks from expression data.

BMC bioinformatics·2024

Same author

Severe acute respiratory syndrome coronavirus 2 antibody seroprevalence in Lebanon: a population-based cross-sectional study.

IJID regions·2022

Same author

ReCodLiver0.9: Overcoming Challenges in Genome-Scale Metabolic Reconstruction of a Non-model Species.

Frontiers in molecular biosciences·2020

Same author

Application of quantitative transcriptomics in evaluating the ex vivo effects of per- and polyfluoroalkyl substances on Atlantic cod (Gadus morhua) ovarian physiology.

The Science of the total environment·2020

Search research articles

Related Experiment Video

Updated: Jun 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

Predicting protein complexes in protein interaction networks using Mapper and graph convolution networks.

Leonardo Daou¹, Eileen Marie Hanna¹

¹Department of Computer Science and Mathematics, Lebanese American University, Byblos, Lebanon.

Computational and Structural Biotechnology Journal

|November 4, 2024

Summary

MComplex predicts protein complexes using dynamic gene expression and protein interactions. This novel method outperforms existing approaches in identifying protein complexes, advancing disease research.

Keywords:

Generative adversarial network Graph convolutional network Mapper algorithm Protein complex Protein-protein interactions Topological data analysis

More Related Videos

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

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

Related Experiment Videos

Last Updated: Jun 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

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

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

Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Protein complexes are crucial for cellular functions and disease mechanisms.
High-throughput experiments generate large protein-protein interaction datasets.
Existing computational methods often rely on static protein interaction networks.

Purpose of the Study:

To develop an advanced computational method for predicting protein complexes.
To leverage dynamic biological data for more accurate complex identification.
To improve understanding of cellular processes and disease pathologies.

Main Methods:

MComplex utilizes time-series gene expression and protein interaction data.
A temporal network is generated and processed by a generative adversarial network (GAN) with a graph convolutional network (GCN) generator.
Embeddings are analyzed using a modified graph-based Mapper algorithm for complex prediction.

Main Results:

MComplex demonstrates superior performance compared to existing methods.
The method achieves high scores in recall and maximum matching ratio.
A composite score confirms MComplex's effectiveness in aggregated evaluation measures.

Conclusions:

MComplex offers a robust and accurate approach for protein complex prediction.
The integration of dynamic data enhances the identification of protein complexes.
This method has potential applications in disease mechanism studies and therapeutic development.