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

TitleLassoHTP: A High-Throughput Computational Tool for Lasso Peptide Structure Construction and Modeling.
Publication TypeJournal Article
Year of Publication2023
AuthorsJuarez, RJ, Jiang, Y, Tremblay, M, Shao, Q, A Link, J, Yang, ZJ
JournalJ Chem Inf Model
Volume63
Issue2
Pagination522-530
Date Published2023 Jan 23
ISSN1549-960X
KeywordsMagnetic Resonance Spectroscopy, Molecular Conformation, Molecular Dynamics Simulation, Peptides, Software
Abstract

<p>Lasso peptides are a subclass of ribosomally synthesized and post-translationally modified peptides with a slipknot conformation. With superior thermal stability, protease resistance, and antimicrobial activity, lasso peptides are promising candidates for bioengineering and pharmaceutical applications. To enable high-throughput computational prediction and design of lasso peptides, we developed a software, LassoHTP, for automatic lasso peptide structure construction and modeling. LassoHTP consists of three modules, including the scaffold constructor, mutant generator, and molecular dynamics (MD) simulator. With a user-provided sequence and conformational annotation, LassoHTP can either generate the structure and conformational ensemble as is or conduct random mutagenesis. We used LassoHTP to construct eight known lasso peptide structures and to simulate their conformational ensembles for 100 ns MD simulations. For benchmarking, we calculated the root mean square deviation (RMSD) of these ensembles with reference to their experimental crystal or NMR PDB structures; we also compared these RMSD values against those of the MD ensembles that are initiated from the PDB structures. Dihedral principal component analysis was also conducted. The results show that the LassoHTP-initiated ensembles are similar to those of the PDB-initiated ensembles. LassoHTP offers a computational platform to develop strategies for lasso peptide prediction and design.</p>

DOI10.1021/acs.jcim.2c00945
Alternate JournalJ Chem Inf Model
PubMed ID36594886