Related Experiment Video
Updated: May 25, 2026
06:50
Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
Published on: January 26, 2024
Predicting lasso peptide structure with LassoPred
Xingyu Ouyang1, Xinchun Ran1, Dantong Zhu1
1Department of Chemistry, Vanderbilt University, Nashville, TN, United States.
Methods in Enzymology
|May 23, 2026
Summary
LassoPred accurately predicts complex lasso peptide structures from sequence alone using machine learning. This tool expands structural coverage, aiding therapeutic design and synthetic biology applications.
Area of Science:
- Biochemistry and Structural Biology
- Computational Biology and Bioinformatics
- Synthetic Biology and Drug Discovery
Background:
- Lasso peptides (LaPs) are a unique class of ribosomally synthesized and post-translationally modified peptides (RiPPs) with a stable, threaded rotaxane topology and diverse bioactivities.
- Accurate 3D structure prediction of LaPs is challenging due to limited homologous templates and the inability of standard tools to model their knotted structures.
- Despite their therapeutic potential, the structural diversity of LaPs remains underexplored, hindering further research and development.
Purpose of the Study:
- To develop a computational pipeline, LassoPred, for high-throughput 3D structure prediction of lasso peptides solely from their amino acid sequences.
- To address the limitations of existing prediction tools in accurately modeling the unique topology of LaPs.
- To expand the structural coverage of known lasso peptides and facilitate their application in various biological and therapeutic fields.
Main Methods:
- LassoPred integrates machine learning, specifically support vector machine classifiers trained on ESM2 embeddings, to identify key topological features like the isopeptide ring and plug residues.
- A topology-aware structure constructor utilizes homology modeling, residue mutation, and energy minimization to assemble and refine atomic models.
- The pipeline is implemented in Python and accessible via a public web interface, supporting compatibility with AMBER and PyMOL.
Main Results:
- LassoPred achieves near-experimental accuracy in predicting lasso peptide structures.
- The prediction time is significantly reduced to minutes, enabling rapid analysis.
- Structural coverage of lasso peptides is expanded from fewer than 50 experimentally determined structures to over 4000 genome-mined models.
- The pipeline is extensible to engineered LaP variants and other RiPP families.
Conclusions:
- LassoPred effectively bridges the gap between lasso peptide sequence discovery and structural insight.
- The tool democratizes structural analysis of LaPs for both expert and non-specialist users.
- This advancement facilitates downstream applications in enzymology, structural biology, synthetic biology, and therapeutic design.
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