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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...

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Related Experiment Video

Updated: Feb 17, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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PeptideNavigator: An interactive tool for exploring large and complex data sets generated during peptide-based drug

Kyle I Diller¹, Alexander S Bayden², Joseph Audie³

¹CMDBioscience Inc., 5 Science Park, New Haven, CT 06511, United States.

Computers in Biology and Medicine

|December 6, 2017

Summary

This summary is machine-generated.

PeptideNavigator is a new computational tool designed to help scientists explore complex data in peptide drug discovery. It aids in visualizing and analyzing peptide properties for better decision-making in drug design.

Keywords:

Data mining Informatics Molecular modeling Peptide drug discovery Visualization

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

Computational chemistry
Drug discovery
Bioinformatics

Background:

Peptide-based drug design faces significant 'big data' challenges due to the complexity and size of molecular information.
Effective management and visualization of large datasets are crucial for optimizing peptide drug discovery projects.

Purpose of the Study:

To introduce PeptideNavigator, an interactive computational environment for exploring and visualizing extensive data generated during peptide drug design.
To enable multidisciplinary scientists to make informed decisions by presenting complex experimental and computational data, including 3D structures.

Main Methods:

Development of an interactive computational environment, PeptideNavigator.
Integration of various viewing options: scatter plots, sequence views, sequence frequency diagrams, Ramachandran plots, and 3D visualization tools.
Implementation of linked views for seamless navigation from collective data exploration to individual peptide analysis.

Main Results:

PeptideNavigator facilitates the collective visualization and exploration of numerous peptides and their properties.
Users can efficiently narrow down focus from large datasets to specific peptides of interest.
Demonstrated utility through case studies in MHC-1A activating peptides and MDM2 scaffold design.

Conclusions:

PeptideNavigator effectively addresses the 'big data' challenge in peptide drug design by providing integrated visualization and analysis tools.
The environment supports multidisciplinary teams in making optimal decisions throughout the peptide drug discovery pipeline.
The tool enhances the ability to navigate complex peptide data, from broad overviews to detailed structural analysis.