CDS&E: High-throughput Computational Workflow for Elucidating the Origin of Lasso Peptide Handedness

This project is co-funded by the Chemical Mechanism, Function, and Properties Program, the Chemistry of Life Processes Program in the Chemistry Division, and the NSF Office of Advanced Cyberinfrastructure. In this project, Professor Zhongyue John Yang of the Department of Chemistry at Vanderbilt University is investigating the origins of wrapping chirality in lasso peptides, which are a class of knotted, interlocked molecules with growing promise in biotechnology and therapeutics. Although lasso peptides can theoretically form in either left- or right-handed wrapping configurations, nature has exclusively selected the right-handed form, a phenomenon that remains mechanistically unclear. This project combines cutting-edge high-throughput protein modeling and machine learning to uncover why this chiral preference exists and whether it can be re-engineered. Lasso peptides are ribosomally synthesized and post-translationally modified peptides that adopt a mechanically interlocked “lariat” shape. Despite the theoretical possibility of both left- and right-handed wrapping topologies (l-LaPs and r-LaPs), only right-handed forms have been identified in nature. This research investigates two hypotheses: (1) that r-LaPs are thermodynamically and kinetically preferred over l-LaPs, and (2) that lasso peptide cyclases selectively catalyze right-handed pre-lasso conformations. To evaluate these hypotheses, the project will develop high-throughput quantum mechanics (QM) and classical molecular dynamics (cMD) workflows to quantify the energy landscapes and entropy profiles of both l- and r-LaPs (Aim 1), and apply multiscale QM/MM and machine learning techniques to model enzyme-substrate interactions and identify cyclase mutants that can potentially generate l-LaPs (Aim 2). These computational pipelines are built on the PI’s previously developed modeling tool, LassoHTP and LassoPred, enabling systematic benchmarking across diverse lasso peptide-enzyme systems. The results will advance understanding of molecular determinants of peptide chirality and refine multiscale modeling strategies for complex, topologically unique biomolecules. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.