ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties
Abstract The UniProt database is a valuable resource for biocatalyst discovery, yet predicting enzymatic functions remains challenging, especially for low-similarity sequences. Identifying superior enzymes with enhanced catalytic properties is even harder. To overcome these challenges, we develop ES...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58521-y |
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| _version_ | 1849734734923431936 |
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| author | Hui Qian Yuxuan Wang Xibin Zhou Tao Gu Hui Wang Hao Lyu Zhikai Li Xiuxu Li Huan Zhou Chengchen Guo Fajie Yuan Yajie Wang |
| author_facet | Hui Qian Yuxuan Wang Xibin Zhou Tao Gu Hui Wang Hao Lyu Zhikai Li Xiuxu Li Huan Zhou Chengchen Guo Fajie Yuan Yajie Wang |
| author_sort | Hui Qian |
| collection | DOAJ |
| description | Abstract The UniProt database is a valuable resource for biocatalyst discovery, yet predicting enzymatic functions remains challenging, especially for low-similarity sequences. Identifying superior enzymes with enhanced catalytic properties is even harder. To overcome these challenges, we develop ESM-Ezy, an enzyme mining strategy leveraging the ESM-1b protein language model and similarity calculations in semantic space. Using ESM-Ezy, we identify novel multicopper oxidases (MCOs) with superior catalytic properties, achieving a 44% success rate in outperforming query enzymes (QEs) in at least one property, including catalytic efficiency, heat and organic solvent tolerance, and pH stability. Notably, 51% of the MCOs excel in environmental remediation applications, and some exhibited unique structural motifs and unique active centers enhancing their functions. Beyond MCOs, 40% of L-asparaginases identified show higher specific activity and catalytic efficiency than QEs. ESM-Ezy thus provides a promising approach for discovering high-performance biocatalysts with low sequence similarity, accelerating enzyme discovery for industrial applications. |
| format | Article |
| id | doaj-art-5a08a31d772a4dec8a99b43ebe5a15df |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-5a08a31d772a4dec8a99b43ebe5a15df2025-08-20T03:07:43ZengNature PortfolioNature Communications2041-17232025-04-0116111110.1038/s41467-025-58521-yESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior propertiesHui Qian0Yuxuan Wang1Xibin Zhou2Tao Gu3Hui Wang4Hao Lyu5Zhikai Li6Xiuxu Li7Huan Zhou8Chengchen Guo9Fajie Yuan10Yajie Wang11School of Engineering, Westlake UniversitySchool of Engineering, Westlake UniversitySchool of Engineering, Westlake UniversitySchool of Engineering, Westlake UniversityBeijing Academy of Artificial IntelligenceSchool of Engineering, Westlake UniversitySchool of Engineering, Westlake UniversitySchool of Engineering, Westlake UniversityWestlake Laboratory of Life Sciences and Biomedicine, Xihu DistrictSchool of Engineering, Westlake UniversitySchool of Engineering, Westlake UniversitySchool of Engineering, Westlake UniversityAbstract The UniProt database is a valuable resource for biocatalyst discovery, yet predicting enzymatic functions remains challenging, especially for low-similarity sequences. Identifying superior enzymes with enhanced catalytic properties is even harder. To overcome these challenges, we develop ESM-Ezy, an enzyme mining strategy leveraging the ESM-1b protein language model and similarity calculations in semantic space. Using ESM-Ezy, we identify novel multicopper oxidases (MCOs) with superior catalytic properties, achieving a 44% success rate in outperforming query enzymes (QEs) in at least one property, including catalytic efficiency, heat and organic solvent tolerance, and pH stability. Notably, 51% of the MCOs excel in environmental remediation applications, and some exhibited unique structural motifs and unique active centers enhancing their functions. Beyond MCOs, 40% of L-asparaginases identified show higher specific activity and catalytic efficiency than QEs. ESM-Ezy thus provides a promising approach for discovering high-performance biocatalysts with low sequence similarity, accelerating enzyme discovery for industrial applications.https://doi.org/10.1038/s41467-025-58521-y |
| spellingShingle | Hui Qian Yuxuan Wang Xibin Zhou Tao Gu Hui Wang Hao Lyu Zhikai Li Xiuxu Li Huan Zhou Chengchen Guo Fajie Yuan Yajie Wang ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties Nature Communications |
| title | ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties |
| title_full | ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties |
| title_fullStr | ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties |
| title_full_unstemmed | ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties |
| title_short | ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties |
| title_sort | esm ezy a deep learning strategy for the mining of novel multicopper oxidases with superior properties |
| url | https://doi.org/10.1038/s41467-025-58521-y |
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