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

Reecan J. Juarez, Yaoyukun Jiang, Matthew Tremblay, Qianzhen Shao, A. James Link, Zhongyue J. Yang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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 de novo 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.

Original languageEnglish (US)
Pages (from-to)522-530
Number of pages9
JournalJournal of Chemical Information and Modeling
Volume63
Issue number2
DOIs
StatePublished - Jan 23 2023

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering
  • General Chemistry
  • Library and Information Sciences
  • Computer Science Applications

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