ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties

Hui Qian, Yuxuan Wang, Xibin Zhou, Tao Gu, Hui Wang, Hao Lyu, Zhikai Li, Xiuxu Li, Huan Zhou, Chengchen Guo, Fajie Yuan () and Yajie Wang ()
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Hui Qian: Westlake University
Yuxuan Wang: Westlake University
Xibin Zhou: Westlake University
Tao Gu: Westlake University
Hui Wang: Beijing Academy of Artificial Intelligence
Hao Lyu: Westlake University
Zhikai Li: Westlake University
Xiuxu Li: Westlake University
Huan Zhou: Xihu District
Chengchen Guo: Westlake University
Fajie Yuan: Westlake University
Yajie Wang: Westlake University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58521-y

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