{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Zhu Q"],"funding":["NIGMS NIH HHS"],"pubmed_abstract":["Rational computational design is crucial to the pursuit of novel drugs and therapeutic agents. Meso-scale cyclic peptides, which consist of 7-40 amino acid residues, are of particular interest due to their conformational rigidity, binding specificity, degradation resistance, and potential cell permeability. Because there are few natural cyclic peptides, <i>de novo</i> design involving non-canonical amino acids is a potentially useful goal. Here, we develop an efficient pipeline (CyclicChamp) for cyclic peptide design. After converting the cyclic constraint into an error function, we employ a variant of simulated annealing to search for low-energy peptide backbones while maintaining peptide closure. Compared to the previous random sampling approach, which was capable of sampling conformatio"],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2024.07.03.601955"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11244984"],"repository":["biostudies-literature"],"pubmed_title":["Heuristic energy-based cyclic peptide design."],"pmcid":["PMC11244984"],"funding_grant_id":["R01 GM121753"],"pubmed_authors":["Shasha D","Mulligan VK","Zhu Q"],"additional_accession":[]},"is_claimable":false,"name":"Heuristic energy-based cyclic peptide design.","description":"Rational computational design is crucial to the pursuit of novel drugs and therapeutic agents. Meso-scale cyclic peptides, which consist of 7-40 amino acid residues, are of particular interest due to their conformational rigidity, binding specificity, degradation resistance, and potential cell permeability. Because there are few natural cyclic peptides, <i>de novo</i> design involving non-canonical amino acids is a potentially useful goal. Here, we develop an efficient pipeline (CyclicChamp) for cyclic peptide design. After converting the cyclic constraint into an error function, we employ a variant of simulated annealing to search for low-energy peptide backbones while maintaining peptide closure. Compared to the previous random sampling approach, which was capable of sampling conformatio","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Feb","modification":"2026-04-08T18:39:53.243Z","creation":"2025-02-19T02:02:19.591Z"},"accession":"S-EPMC11244984","cross_references":{"pubmed":["39005429"],"doi":["10.1101/2024.07.03.601955"]}}