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

作者：Hui Qian, Yuxuan Wang, Xibin Zhou, Tao Gu, Hui Wang, Hao Lyu, Zhikai Li, Zhikai Li, Huan Zhou, Chengchen Guo, Fajie Yuan, Yajie Wang · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-58521-y · 被引用次数：30 · 研究领域：Enzyme-mediated dye degradation、Electrochemical sensors and biosensors、Advanced Nanomaterials in Catalysis

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.