LassoHTP: A High-Throughput Computational Tool for Lasso Peptide Structure Construction and Modeling.

Area of Science:

• Biochemistry
• Computational Biology

Background:

• Lasso peptides are ribosomally synthesized and post-translationally modified peptides characterized by a unique slipknot conformation.
• These peptides exhibit remarkable thermal stability, protease resistance, and potent antimicrobial activity, making them highly valuable for bioengineering and pharmaceutical development.

Purpose of the Study:

• To develop a high-throughput computational tool, LassoHTP, for the automated construction and modeling of lasso peptide structures.
• To facilitate the prediction and design of novel lasso peptides for various applications.

Main Methods:

• LassoHTP integrates three modules: scaffold constructor, mutant generator, and molecular dynamics (MD) simulator.
• The software models lasso peptide structures and conformational ensembles from user-provided sequences, with options for random mutagenesis.
• Conformational ensembles were simulated for 100 ns MD simulations for eight known lasso peptides.

Main Results:

• LassoHTP successfully constructed eight known lasso peptide structures *de novo*.
• Root mean square deviation (RMSD) analysis showed that LassoHTP-initiated ensembles closely resemble those initiated from experimental PDB structures.
• Dihedral principal component analysis confirmed the similarity between LassoHTP-generated and PDB-initiated ensembles.

Conclusions:

• LassoHTP provides a robust computational platform for lasso peptide structure prediction and design.
• The software's ability to generate accurate conformational ensembles supports the development of new lasso peptide-based therapeutics and biotechnologies.