LassoPred: a tool to predict the 3D structure of lasso peptides

Area of Science:

• Biochemistry
• Structural Biology
• Bioinformatics

Background:

• Lasso peptides (LaPs) are a large class of ribosomally synthesized and post-translationally modified peptides (RiPPs) with diverse biological functions, including roles as antibiotics and enzyme inhibitors.
• Despite numerous predicted sequences, only about 50 distinct LaPs have been structurally characterized due to their complex, entangled slipknot-like structures and the presence of isopeptide bonds.
• Existing protein structure prediction tools like AlphaFold2, AlphaFold3, and ESMfold are inadequate for accurately modeling LaP structures.

Purpose of the Study:

• To develop a computational tool capable of accurately predicting the 3D structures of lasso peptides.
• To create the largest in silico-predicted lasso peptide structure database to date.
• To facilitate future structure-function relationship studies and the discovery of novel functional LaPs.

Main Methods:

• Development of LassoPred, a novel computational tool incorporating a classifier for annotating LaP sequence components (ring, loop, tail) and a constructor for building 3D structures.
• Application of LassoPred to predict the 3D structures of 4749 unique LaP core sequences.
• Public release of LassoPred via a web interface and command-line tool.

Main Results:

• Successful prediction of 3D structures for 4749 unique lasso peptide core sequences using LassoPred.
• Creation of the largest in silico-predicted lasso peptide structure database.
• Demonstration of LassoPred's capability to overcome limitations of existing prediction tools for LaPs.

Conclusions:

• LassoPred effectively predicts lasso peptide structures, addressing a significant gap in current computational biology tools.
• The generated database significantly expands the structural knowledge of LaPs, supporting further research.
• LassoPred is a valuable resource for exploring LaP structure-function relationships and accelerating the discovery of LaPs for chemical and biomedical applications.