CyclicBoltz1, fast and accurately predicting structures of cyclic peptides and complexes containing non-canonical amino acids using AlphaFold 3 Framework

Cyclic peptides exhibit favourable properties making them promising candidates as therapeutics. While the design and modeling of peptides in general has seen rapid advances since the advent of modern machine learning methods, existing deep learning models cannot effectively predict cyclic peptide structures containing non-canonical amino acids (ncAAs), which are often crucial for peptide therapeutics. To address this limitation, we here extend the recent AF3-style model Boltz to cyclic peptides with ncAAs. In addition to the positional encoding offset, we used a simple yet effective extension of the cyclic offset encoding based on AlphaFold3 tokenization scheme that allows the modeling of cyclic peptides with modified residues. On a test set of peptides with ncAAs, our approach outperforms HighFold2 on 13/17 cases, with an average Cα RMSD of 1.877Å and an average all-atom RMSD of 3.361Å. Our results show that the cyclic offset encoding shown for AlphaFold2 generalizes to AlphaFold3-based models and can be extended to incorporate ncAAs, showing potential in the design of novel cyclic peptides with ncAAs for therapeutic applications.