biostudies-literatureZhu CNatural Science Foundation of Zhejiang ProvinceScience and Technology Development Fund of Macaubbaf202https://www.ebi.ac.uk/biostudies/studies/S-EPMC12066415biostudies-literatureUnknown26(3)Cyclic peptides containing unnatural amino acids possess many excellent properties and have become promising candidates in drug discovery. Therefore, accurately predicting the 3D structures of cyclic peptides containing unnatural residues will significantly advance the development of cyclic peptide-based therapeutics. Although deep learning-based structural prediction models have made tremendous progress, these models still cannot predict the structures of cyclic peptides containing unnatural amino acids. To address this gap, we introduce a novel model, HighFold2, built upon the AlphaFold-Multimer framework. HighFold2 first extends the pre-defined rigid groups and their initial atomic coordinates from natural amino acids to unnatural amino acids, thus enabling structural prediction for theBriefings in bioinformaticsPredicting the structures of cyclic peptides containing unnatural amino acids by HighFold2.PMC12066415LD22H3000040177/2023/RIA3Guo JSu ALi CCao SShang TDuan HZhu CfalsePredicting the structures of cyclic peptides containing unnatural amino acids by HighFold2.Cyclic peptides containing unnatural amino acids possess many excellent properties and have become promising candidates in drug discovery. Therefore, accurately predicting the 3D structures of cyclic peptides containing unnatural residues will significantly advance the development of cyclic peptide-based therapeutics. Although deep learning-based structural prediction models have made tremendous progress, these models still cannot predict the structures of cyclic peptides containing unnatural amino acids. To address this gap, we introduce a novel model, HighFold2, built upon the AlphaFold-Multimer framework. HighFold2 first extends the pre-defined rigid groups and their initial atomic coordinates from natural amino acids to unnatural amino acids, thus enabling structural prediction for the2025-01-01T00:00:00Z2025 May2026-06-01T05:55:48.871Z2026-04-08T09:44:47.684ZS-EPMC120664154035069810.1093/bib/bbaf202