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

Xingyu Ouyang, Xinchun Ran, Han Xu, Runeem Al-Abssi, Yi-Lei Zhao (), A. James Link () and Zhongyue J. Yang ()
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Xingyu Ouyang: Shanghai Jiao Tong University
Xinchun Ran: Vanderbilt University
Han Xu: 200 8 Ave SW #400
Runeem Al-Abssi: Vanderbilt University
Yi-Lei Zhao: Shanghai Jiao Tong University
A. James Link: Princeton University
Zhongyue J. Yang: Vanderbilt University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: 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 predict the 3D structures for 4749 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.

Date: 2025
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DOI: 10.1038/s41467-025-60544-4

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