HighFold-Lasso: Topology-Guided Prediction of Lasso Peptide Structures Using AlphaFold3

Abstract

Lasso peptides are a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by a mechanically interlocked topology, in which the C-terminal tail is threaded through an N-terminal macrolactam ring formed by an isopeptide bond. This unique topology often confers high thermal stability and resistance to proteolytic degradation. However, the isopeptide linkage, threaded topology, and class-specific disulfide connectivity make lasso peptide structures difficult to reconstruct reliably using general-purpose structure prediction models. Here, we developed HighFold-Lasso, which combines custom covalent connectivity with lasso scaffold-guided adaptive low-noise initialization to enable conditional structure generation guided by lasso conformational priors. On a test set of 52 experimentally determined structures, HighFold-Lasso achieved a 100% lasso topology success rate, with a mean RMSD C-alpha of 1.942 Angstrom for the ring-loop region and 2.375 Angstrom for the full peptide. The framework supports modeling of the four classical lasso peptide classes (I-IV) and can represent noncanonical amino acids, providing computational support for structure-function studies and molecular engineering of lasso peptides.