MuCO2: Unlocking Multi-Stage Conformation Generation for Noncanonical Cyclic Peptides

Cyclic peptides containing noncanonical amino acids (ncAAs) represent an indispensable modality in modern drug discovery, offering improved proteolytic stability and therapeutic profiles. However, efficiently modeling their conformational ensembles across complex energy landscapes remains challenging: existing methods like the HighFold series and MuCO mainly focus on canonical peptides and struggle with complicated structural perturbations. Building upon MuCO, we propose MuCO2, a multi-stage conformation generation framework for noncanonical cyclic peptides with α-backbone modifications, stereochemical transformations, and open-ended side-chain modifications. In particular, MuCO2 represents open-set ncAAs by aligning the atomic representations of Uni-Mol2 with the residue-level embeddings from ESM-2. Leveraging a curriculum learning paradigm driven by conformational energy and symmetries, MuCO2 learns a quaternion flow-based cyclic backbone generator coupled with a structure-guided Feynman-Kac steering mechanism, enabling efficient, generalizable cyclic peptide sampling conditioned on chirality parity and N-methylation. Extensive evaluations on canonical and noncanonical benchmarks show that MuCO2 achieves high-throughput cyclic peptide sampling with near-perfect stereochemical preservation, consistently outperforming state-of-the-art methods in physical stability, structural diversity, and potential-well coverage.