biostudies-literatureUnknownZhu QNIGMS NIH HHSRational 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 conformatiobioRxiv : the preprint server for biology2024.07.03.601955https://www.ebi.ac.uk/biostudies/studies/S-EPMC11244984biostudies-literatureHeuristic energy-based cyclic peptide design.PMC11244984R01 GM121753Shasha DMulligan VKZhu QfalseHeuristic energy-based cyclic peptide design.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 conformatio2025-01-01T00:00:00Z2025 Feb2026-04-08T18:39:53.243Z2025-02-19T02:02:19.591ZS-EPMC112449843900542910.1101/2024.07.03.601955