Accurate structure prediction of cyclic peptides containing unnatural amino acids using HighFold3.

ABSTRACT: Cyclic peptides have emerged as a research hotspot in drug development in recent years due to their excellent stability, specificity, and cell penetration. However, existing computational models face challenges in accurately predicting the three-dimensional structures of cyclic peptides containing unnatural amino acids (unAAs), thereby limiting their drug design. The release of AlphaFold 3 has significantly enhanced the modeling capability of biomolecular complexes and enabled the inclusion of unAAs through definitions provided by the Chemical Component Dictionary (CCD). Nevertheless, its training data reliance limits its ability to accurately predict cyclic peptide structures, failing to meet the demand for precise cyclic peptide structure prediction. Based on the AlphaFold 3 framework, w

SUBMITTER: Cao S 

PROVIDER: S-EPMC12450345 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature