An Su

Cycledesigner Leveraging RFdiffusion and HighFold to Design Cyclic Peptide Binders for Specific Targets

This study introduces cycledesigner, a method combining RFdiffusion, ProteinMPNN, and HighFold to design cyclic peptide binders for specific targets, followed by scoring functions for screening.

NCPepFold: Accurate Prediction of Non-canonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation

NCPepFold predicts structures of non-canonical cyclic peptides with improved accuracy using multigranular representation and cyclization optimization.

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

HighFold2 predicts 3D structures of cyclic peptides with unnatural amino acids, achieving a median RMSD of 1.891 Å for monomers and complexes.