Work

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

Year: 2026

Type: preprint

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 mean RMSD Cα of 1.942 Å for the ring–loop region and 2.375 Å for the full peptide. The framework supports the 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.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.64898/2026.10.14.759041