ArticleJournal of cheminformatics2022
Efficient 3D conformer generation of cyclic peptides formed by a disulfide bond.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- An in silico approach to peptide-based dual-receptor targeting for IL13RA2 and VEGFR-2 extracellular domain.Journal of molecular modeling · 2026Article
- Semaglutide Mitigates Ischemic Brain Injury by Inhibiting Ferroptosis via Modulation of FoXO1 and DRP1 Pathways.Molecular neurobiology · 2025Article
- Identification and inhibition of PIN1-NRF2 protein-protein interactions through computational and biophysical approaches.Scientific reports · 2025Article
- Insights into the Adsorption Mechanisms of the Antimicrobial Peptide CIDEM-501 on Membrane Models.Antibiotics (Basel, Switzerland) · 2024Article
- Structure prediction of linear and cyclic peptides using CABS-flex.Briefings in bioinformatics · 2024Article
- Computational Insights into the Formation and Structure of S-N Containing Cyclic Peptides.ACS omega · 2023Article
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5 authors.
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Abstract
Cyclic peptides formed by disulfide bonds have been one large group of common drug candidates in drug development. Structural information of a peptide is essential to understand its interaction with its target. However, due to the high flexibility of peptides, it is difficult to sample the near-native conformations of a peptide. Here, we have developed an extended version of our MODPEP approach, named MODPEP2.0, to fast generate the conformations of cyclic peptides formed by a disulfide bond. MODPEP2.0 builds the three-dimensional (3D) structures of a cyclic peptide from scratch by assembling amino acids one by one onto the cyclic fragment based on the constructed rotamer and cyclic backbone libraries. Being tested on a data set of 193 diverse cyclic peptides, MODPEP2.0 obtained a considerable advantage in both accuracy and computational efficiency, compared with other sampling algorithms including PEP-FOLD, ETKDG, and modified ETKDG (mETKDG). MODPEP2.0 achieved a high sampling accuracy with an average C[Formula: see text] RMSD of 2.20 Å and 1.66 Å when 10 and 100 conformations were considered, respectively, compared with 3.41 Å and 2.62 Å for PEP-FOLD, 3.44 Å and 3.16 Å for ETKDG, 3.09 Å and 2.72 Å for mETKDG. MODPEP2.0 also reproduced experimental peptide structures for 81.35% of the test cases when an ensemble of 100 conformations were considered, compared with 54.95%, 37.50% and 50.00% for PEP-FOLD, ETKDG, and mETKDG. MODPEP2.0 is computationally efficient and can generate 100 peptide conformations in one second. MODPEP2.0 will be useful in sampling cyclic peptide structures and modeling related protein-peptide interactions, facilitating the development of cyclic peptide drugs.
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