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

Clove Essential Oil as an Antifungal Agent and Putative Dual-Action Antifungal Mechanism: Experimental Validation and Computational Insights into Orthosteric and Allosteric Modulation of Chitin Synthase I in <i>Penicillium</i> Species.

Molecules (Basel, Switzerland)·2026

Same author

Computational Identification of Potential Novel Allosteric IHF Inhibitors Using QSAR Modeling to Inhibit Plasmid-Mediated Antibiotic Resistance.

International journal of molecular sciences·2026

Same author

A 2026 Update on Computational Approaches to the Discovery and Design of Antimicrobial Peptides.

Antibiotics (Basel, Switzerland)·2026

Same author

Hybrid Computational Framework Integrating Ensemble Learning, Molecular Docking, and Dynamics for Predicting Antimalarial Efficacy of Malaria Box Compounds.

International journal of molecular sciences·2026

Same author

Precision fragment addition: domain-specific DeepFrag2 models for smarter lead optimization.

Digital discovery·2026

Same author

Versatility of Click Chemistry in Hydrogel Synthesis: From Molecular Strategies to Applications in Regenerative Medicine.

Gels (Basel, Switzerland)·2026

Search research articles

Related Experiment Video

Updated: Jun 11, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks.

Kevin Castillo-Mendieta¹, Guillermin Agüero-Chapin^2,3, Edgar A Marquez⁴

¹School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí, Ecuador.

NPJ Systems Biology and Applications

|October 4, 2024

Summary

This summary is machine-generated.

Network science and data mining reveal patterns in hemolytic peptides, aiding drug development. Identifying toxic motifs helps create safer peptide-based drugs by predicting hemolytic activity.

More Related Videos

Analysis of Histone Antibody Specificity with Peptide Microarrays

Analysis of Histone Antibody Specificity with Peptide Microarrays

Published on: August 1, 2017

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: Jun 11, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Analysis of Histone Antibody Specificity with Peptide Microarrays

Analysis of Histone Antibody Specificity with Peptide Microarrays

Published on: August 1, 2017

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:

Medicinal Chemistry
Bioinformatics
Network Science

Background:

Peptides are valuable drug candidates but often exhibit undesirable properties like hemolytic activity.
Hemolytic activity in peptides can limit their therapeutic potential and necessitate careful design.
Understanding the chemical space of hemolytic peptides is crucial for developing safer peptide therapeutics.

Purpose of the Study:

To gain deeper insights into the chemical space of hemolytic peptides using network science and data mining.
To identify patterns and characteristics associated with hemolytic activity in peptides.
To develop methods for predicting and mitigating hemolytic activity in novel peptide drug candidates.

Main Methods:

Application of Metadata Networks (METNs) to characterize general patterns of hemolytic peptides.
Utilization of Half-Space Proximal Networks (HSPNs) to represent the hemolytic peptide space.
Extraction of hemolytic peptide scaffolds using network centrality and peptide similarity.
Employing an alignment-free approach to identify putative hemolytic motifs.

Main Results:

METNs successfully characterized general patterns linked to hemolytic peptides.
HSPNs effectively represented the hemolytic peptide chemical space.
Identified peptide scaffolds proved useful for developing robust similarity-based classifiers.
Discovered 47 putative hemolytic motifs, serving as potential toxic signatures.
Established a correlation between the number of hemolytic motifs and the likelihood of hemolytic activity.

Conclusions:

Network science and data mining offer novel approaches to understanding peptide properties.
The identified hemolytic motifs can guide the design of safer peptide-based drugs.
Predicting hemolytic activity based on sequence motifs is a viable strategy for drug development.
This study provides a foundation for developing peptide drugs with reduced hemolytic side effects.