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Related Experiment Video

Updated: Sep 21, 2025

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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Cyclization and Docking Protocol for Cyclic Peptide-Protein Modeling Using HADDOCK2.4.

Vicky Charitou1, Siri C van Keulen1, Alexandre M J J Bonvin1

  • 1Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Science for Life, Faculty of Science─Chemistry, Utrecht University, Padualaan 8, Utrecht 3584 CH, The Netherlands.

Journal of Chemical Theory and Computation
|June 2, 2022
PubMed
Summary
This summary is machine-generated.

This study presents a protocol for modeling cyclic peptides and their protein targets. The new method improves drug design by accurately predicting interactions for cyclic peptide therapeutics.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Cyclic peptides are an emerging class of therapeutics with over 40 drugs in clinical use.
  • Understanding their mechanism of action is crucial for rational drug design but challenging due to their complex structure and flexibility.
  • Computational modeling of cyclic peptides and their interactions presents significant hurdles.

Purpose of the Study:

  • To develop and validate a step-by-step computational protocol for generating cyclic peptide conformations and docking them to protein targets.
  • To optimize cyclization and docking procedures using a dataset of 30 cyclic peptide-protein complexes.
  • To assess the protocol's performance in both bound and unbound docking scenarios.

Main Methods:

  • Utilized HADDOCK2.4 for generating cyclic peptide conformations and performing protein-ligand docking.
  • Developed protocols supporting peptides cyclized via N-/C-terminus peptide bonds and/or disulfide bonds.
  • Employed an ensemble of cyclic peptide conformations, driven by known protein binding sites, for docking.

Main Results:

  • Achieved a 100% success rate in predicting at least one acceptable model within the top 10 HADDOCK-ranked structures for all 30 complexes.
  • Demonstrated comparable performance to state-of-the-art software Autodock CrankPep in both bound and fully unbound docking scenarios.
  • Validated the protocol's effectiveness across diverse cyclic peptide structures and cyclization types.

Conclusions:

  • The presented HADDOCK2.4 protocol provides a robust and accurate method for modeling cyclic peptide-protein interactions.
  • This protocol can significantly aid in rational drug design and high-throughput screening of cyclic peptide-based therapeutics.
  • The findings facilitate the advancement of cyclic peptide drug discovery by addressing key computational challenges.