LassoHTP/LassoPred for Lasso Peptide Structure Prediction and Modeling
Lasso peptides (LaPs) are RiPPs with a characteristic slipknot (lariat-knot) topology that confers exceptional thermal stability, protease resistance, and often potent bioactivity. Despite thousands of LaP sequences identified by bioinformatics, only ~50 have experimentally determined structures, largely because their irregular scaffold and isopeptide bond confound general-purpose predictors.
LassoPred/LassoHTP enables end-to-end prediction and modeling of LaPs from sequence. It pairs a classifier that annotates ring, loop, and tail segments from sequence with a constructor that assembles constraint-consistent 3D models. The constructor comprises a scaffold constructor for knot-correct 3D topologies, a mutant generator for targeted or random mutagenesis, and an MD simulator for ensemble generation. The tool provides a practical engine for LaP structure generation, exploration, and variant design.
Reecan Juarez and Ouyang Xingyu led the development of LassoHTP and LassoPred, respectively. Both tools are modified from the code base of EnzyHTP originally developed by Qianzhen Shao.
The software code:
https://github.com/ChemBioHTP/LassoPred
Web Interface:
https://lassopred.mutexa.org/
Publications:
https://www.nature.com/articles/s41467-025-60544-4
https://pubs.acs.org/doi/10.1021/acs.jcim.2c00945