Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2.

ABSTRACT: 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 the

SUBMITTER: Zhu C 

PROVIDER: S-EPMC12066415 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature