LassoPred: a tool to predict the 3D structure of lasso peptides
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 se...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60544-4 |
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| author | Xingyu Ouyang Xinchun Ran Han Xu Runeem Al-Abssi Yi-Lei Zhao A. James Link Zhongyue J. Yang |
| author_facet | Xingyu Ouyang Xinchun Ran Han Xu Runeem Al-Abssi Yi-Lei Zhao A. James Link Zhongyue J. Yang |
| author_sort | Xingyu Ouyang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-47c756ca2bb3432e8bcf849f16932dfa |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-47c756ca2bb3432e8bcf849f16932dfa2025-08-20T04:01:41ZengNature PortfolioNature Communications2041-17232025-07-0116111310.1038/s41467-025-60544-4LassoPred: a tool to predict the 3D structure of lasso peptidesXingyu Ouyang0Xinchun Ran1Han Xu2Runeem Al-Abssi3Yi-Lei Zhao4A. James Link5Zhongyue J. Yang6State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityDepartment of Chemistry, Vanderbilt UniversityNeo Financial, 200 8 Ave SW #400Department of Chemistry, Vanderbilt UniversityState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityDepartment of Chemical and Biological Engineering, Chemistry, and Molecular Biology, Princeton UniversityDepartment of Chemistry, Vanderbilt UniversityAbstract 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.https://doi.org/10.1038/s41467-025-60544-4 |
| spellingShingle | Xingyu Ouyang Xinchun Ran Han Xu Runeem Al-Abssi Yi-Lei Zhao A. James Link Zhongyue J. Yang LassoPred: a tool to predict the 3D structure of lasso peptides Nature Communications |
| title | LassoPred: a tool to predict the 3D structure of lasso peptides |
| title_full | LassoPred: a tool to predict the 3D structure of lasso peptides |
| title_fullStr | LassoPred: a tool to predict the 3D structure of lasso peptides |
| title_full_unstemmed | LassoPred: a tool to predict the 3D structure of lasso peptides |
| title_short | LassoPred: a tool to predict the 3D structure of lasso peptides |
| title_sort | lassopred a tool to predict the 3d structure of lasso peptides |
| url | https://doi.org/10.1038/s41467-025-60544-4 |
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