ArticleJournal of chemical theory and computation2022
Cyclization and Docking Protocol for Cyclic Peptide-Protein Modeling Using HADDOCK2.4.
Article in Journal of chemical theory and computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A suite of macrocyclic peptide inhibitors and substrate probes for arginine methyltransferases.Chemical science · 2026Article
- Ensemble Sensitivity to Chemical Modifications in Free and Bound Macrocyclic Peptides.The journal of physical chemistry. B · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- Immunoregulatory protein-hybrid extracellular vesicles via self-loadable backbone cyclization for oral inflammatory bowel disease therapy.Bioactive materials · 2026Article
- Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation.Frontiers in molecular biosciences · 2026Review
- Flexible Docking of Cyclic Peptides to Proteins Using CABS-dock.Journal of chemical theory and computation · 2025Article
- Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking.Scientific reports · 2025Article
- Post-Docking Refinement of Peptide or Protein-RNA Complexes Using Thermal Titration Molecular Dynamics (TTMD): A Stability Insight.Journal of chemical information and modeling · 2025Article
- Exploring key features of selectivity in somatostatin receptors through molecular dynamics simulations.Computational and structural biotechnology journal · 2024Article
- Discovery of Cyclic Peptide Inhibitors Targeted on TNFα-TNFR1 from Computational Design and Bioactivity Verification.Molecules (Basel, Switzerland) · 2024Article
- Modeling Protein-Glycan Interactions with HADDOCK.Journal of chemical information and modeling · 2024Article
- Leveraging machine learning models for peptide-protein interaction prediction.RSC chemical biology · 2024Review
- HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints.Briefings in bioinformatics · 2024Article
- Article
- Abatement of the binding of human hexokinase II enzyme monomers by in-silico method with the design of inhibitory peptides.In silico pharmacology · 2024Article
- Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold.International journal of molecular sciences · 2023Article
- PEP-FOLD4: a pH-dependent force field for peptide structure prediction in aqueous solution.Nucleic acids research · 2023Article
- Comparative Binding Ability of Human Monoclonal Antibodies against Omicron Variants of SARS-CoV-2: AnAntibodies (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An emerging class of therapeutic molecules are cyclic peptides with over 40 cyclic peptide drugs currently in clinical use. Their mode of action is, however, not fully understood, impeding rational drug design. Computational techniques could positively impact their design, but modeling them and their interactions remains challenging due to their cyclic nature and their flexibility. This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide-protein complexes was used to optimize both cyclization and docking protocols. It supports peptides cyclized via an N- and C-terminus peptide bond and/or a disulfide bond. An ensemble of cyclic peptide conformations is then used in HADDOCK to dock them onto their target protein using knowledge of the binding site on the protein side to drive the modeling. The presented protocol predicts at least one acceptable model according to the critical assessment of prediction of interaction criteria for each complex of the dataset when the top 10 HADDOCK-ranked single structures are considered (100% success rate top 10) both in the bound and unbound docking scenarios. Moreover, its performance in both bound and fully unbound docking is similar to the state-of-the-art software in the field, Autodock CrankPep. The presented cyclization and docking protocol should make HADDOCK a valuable tool for rational cyclic peptide-based drug design and high-throughput screening.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.