Predicting 3D Structures of Lasso Peptides

TL;DR

This paper introduces LassoPred, a new tool that predicts 3D structures of lasso peptides, enabling large-scale structural analysis for biomedical and chemical research.

Contribution

LassoPred is the first tool specifically designed to predict 3D structures of lasso peptides, overcoming limitations of existing methods.

Findings

LassoPred accurately predicts the 3D structures of lasso peptides by classifying and constructing their unique entangled features.

The tool was used to generate a database of 4,749 predicted lasso peptide structures, the largest of its kind.

LassoPred is publicly accessible via a web interface and command-line tool for broader scientific use.

Abstract

Lasso peptides (LaPs), characterized by their entangled slipknot-like structures, are a large class of ribosomally synthesized and post-translationally modified peptides (RiPPs), with examples functioning as antibiotics, enzyme inhibitors, and molecular switches. Despite thousands of LaP sequences predicted by bioinformatics, only around 50 distinct LaPs have been structurally characterized in the past 30 years. Existing computational tools, such as AlphaFold2, AlphaFold3 and ESMfold, fail to accurately predict LaP structures due to their irregular scaffold featuring a lariat knot-like fold and the presence of an isopeptide bond. To address this challenge, we developed LassoPred, designed with a classifier to annotate the ring, loop, and tail of an LaP sequence and a constructor to build a 3D structure. Leveraging LassoPred, we predicted 3D structures for 4,749 unique LaP core sequences, creating the largest
in silico
-predicted lasso peptide structure database to date. LassoPred is publicly available through a web interface (https://lassopred.accre.vanderbilt.edu/) and a command-line tool, supporting future structure-function relationship studies and aiding in the discovery of functional lasso peptides for chemical and biomedical applications.