De novo Folding Mechanisms of Lasso Peptides

Abstract

Lasso peptides adopt a distinctive [1]rotaxane conformation, yet the principles governing the folding of this kinetically trapped structure have remained elusive. Here, we integrated extensive molecular dynamics simulations and deep learning to elucidate the de novo folding mechanism of 20 lasso peptides lacking secondary post-translational modifications. We constructed Multi-Ensemble Markov Models for each lasso peptide and uncovered a universal uphill folding landscape with spontaneous folding probabilities consistently below 0.8%. Loop stability strongly correlated with folding propensity, and targeted experiments further validated that enhancing loop {beta}-hairpin formation promotes folding of microcin J25, the well-studied lasso peptide extensively characterized as an in vitro model. Additionally, the substantial entropy cost opposed lasso peptide folding. Simulations mimicking enzymatic spatial confinement reduced this penalty and stabilize folding. Leveraging Variational AutoEncoder-based pathway clustering, we resolved distinct pathway channels and representative folding pathways. Together, these findings establish representative folding models and fundamental thermodynamic and kinetic principles for rational engineering of lasso peptides.