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-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

[The application of domestic ear molding system in bilateral neonatal congenital ear malformation].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery·2026

Same author

Computational intelligence using nailfold videocapillaroscopy for the prediction of carotid intima-media thickness in rheumatoid arthritis: a cohort-based study.

Rheumatology international·2026

Same author

Condensate-based sequestration enables high-efficiency synthesis of mussel foot protein Mcofp-3.

Applied and environmental microbiology·2026

Same author

miR-494-3p/G6PC/FOXO1 signaling axis drives autophagy-mediated breast cancer progression and predicts poor prognosis.

Cellular signalling·2026

Same author

Complete genome sequence of <i>Methylomonas defluvii</i> YC3 isolated from the cyclic activated sludge system of a banknote printing facility.

Microbiology resource announcements·2026

Same author

How the digital literacy level of teachers affects student well-being: a configuration analysis approach based on fsQCA.

Frontiers in public health·2026

Search research articles

Related Experiment Video

Updated: Jul 9, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

A dual-population multi-objective evolutionary algorithm driven by generative adversarial networks for benchmarking

Honglei Cheng¹, Gai-Ge Wang¹, Liyan Chen²

¹School of Computer Science and Technology, Ocean University of China, Qingdao, China.

Computers in Biology and Medicine

|November 29, 2023

Summary

This summary is machine-generated.

This study introduces a dual-population evolutionary algorithm (DGMOEA) to overcome model collapse in multi-objective optimization problems (MOPs). DGMOEA enhances solution quality and diversity, significantly outperforming existing methods on benchmark functions and protein-peptide docking.

Keywords:

Deep learning Dual-population Evolutionary algorithm Generative adversarial network Multi-objective optimization Protein-peptide docking

More Related Videos

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Jul 9, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Area of Science:

Computational Science and Engineering
Bioinformatics
Artificial Intelligence

Background:

Multi-objective optimization problems (MOPs) involve optimizing multiple conflicting objectives simultaneously.
Model-based evolutionary algorithms (MBEAs) utilize machine learning but suffer from model collapse, leading to local optima and reduced diversity.
Addressing model collapse is crucial for improving the performance of MBEAs in complex optimization tasks.

Purpose of the Study:

To propose a novel dual-population multi-objective evolutionary algorithm driven by Wasserstein generative adversarial network with gradient penalty (DGMOEA).
To enhance the generation of high-quality solutions and improve diversity in MOPs.
To evaluate DGMOEA's effectiveness on benchmark functions and a real-world protein-peptide docking problem.

Main Methods:

Developed DGMOEA, a dual-population algorithm leveraging Wasserstein generative adversarial networks with gradient penalty.
Coordinated dual populations to collaboratively generate superior candidate solutions.
Compared DGMOEA against seven state-of-the-art algorithms on 20 multi-objective benchmark functions and the LEADS-PEP dataset.

Main Results:

DGMOEA demonstrated significant improvements on MOPs, outperforming comparative algorithms.
Achieved superior performance in terms of Inverted Generational Distance (IGD) and Hypervolume (HV) metrics on 15 and 18 out of 20 benchmarks, respectively.
Effectively reduced Root Mean Square Deviation (RMSD) in protein-peptide docking, yielding competitive results on the LEADS-PEP dataset.

Conclusions:

DGMOEA effectively addresses model collapse issues in MBEAs, enhancing solution quality and diversity.
The proposed algorithm offers a robust and efficient approach for solving complex MOPs.
DGMOEA shows strong potential for applications in computational biology, such as protein-peptide docking.