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Related Concept Videos

Protein-protein Interfaces02:04

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 polypeptide...

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Related Experiment Video

Updated: May 29, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

PepCrawler: a fast RRT-based algorithm for high-resolution refinement and binding affinity estimation of peptide

Elad Donsky1, Haim J Wolfson

  • 1Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. eladdons@tau.ac.il

Bioinformatics (Oxford, England)
|September 2, 2011
PubMed
Summary

PepCrawler is a new computational tool that predicts peptide-protein complexes. It rapidly generates flexible peptide conformations and estimates binding affinity, aiding in the design of protein-protein interaction inhibitors.

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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

Published on: October 26, 2015

Area of Science:

  • Structural bioinformatics
  • Computational drug design

Background:

  • Designing protein-protein interaction (PPI) inhibitors is crucial for drug discovery.
  • Peptides are promising candidates for PPI inhibitors due to their ability to mimic protein interfaces.
  • Predicting peptide-protein complexes is challenging due to peptide conformational flexibility.

Purpose of the Study:

  • To introduce PepCrawler, a novel tool for predicting peptide-protein complexes.
  • To enable the derivation of binding peptides from known protein-protein complexes.
  • To facilitate the estimation of binding affinity for protein-peptide interactions.

Main Methods:

  • PepCrawler utilizes a fast path planning approach for rapid generation of flexible peptide conformations.
  • The tool performs high-resolution docking refinement to predict complex structures.
  • A novel binding energy funnel 'steepness score' is employed to evaluate binding affinity.

Main Results:

  • PepCrawler accurately predicted high binding affinity for native protein-peptide complexes and low affinity for decoys.
  • The tool's predictions were consistent with available experimental wet lab data in three test cases.
  • PepCrawler demonstrates high speed, completing predictions in minutes on a single PC, outperforming other flexible peptide-protein structure prediction algorithms.

Conclusions:

  • PepCrawler is an efficient and accurate tool for predicting peptide-protein complexes.
  • The tool's speed and accuracy make it valuable for computer-aided drug design and structural bioinformatics.
  • PepCrawler aids in the identification and design of novel peptide-based PPI inhibitors.