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, we developed HighFold3 by introducing the Cyclic Position Offset Encoding Matrix (CycPOEM). HighFold3 comprises two submodels: HighFold3-Linear and HighFold3-Cyclic, designed for predicting the structures of linear and cyclic peptides, respectively. Our results demonstrate that HighFold3 outperforms existing models (HighFold, HighFold2, CyclicBoltz1, NCPepFold, CABS-flex, ESMFold, and HelixFold) in cyclic peptide structure prediction. It achieves atomic-level precision in predicting cyclic peptide monomers while demonstrating enhanced accuracy and generalization capability for cyclic peptide complexes containing unAAs. This offers unprecedented technical support for the structural design and optimization of cyclic peptide-based therapeutics.