Qian Hui, Wang Yuxuan, Zhou Xibin, Gu Tao, Wang Hui, Lyu Hao, Li Zhikai, Li Xiuxu, Zhou Huan, Guo Chengchen, Yuan Fajie, Wang Yajie
School of Engineering, Westlake University, Hangzhou, 310014, Zhejiang, China.
The Center for Synthetic Biology and Integrated Bioengineering, Westlake University, Hangzhou, 310014, Zhejiang, China.
Nat Commun. 2025 Apr 6;16(1):3274. doi: 10.1038/s41467-025-58521-y.
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.
UniProt数据库是生物催化剂发现的宝贵资源，但预测酶的功能仍然具有挑战性，尤其是对于低相似性序列。鉴定具有增强催化特性的优质酶则更加困难。为了克服这些挑战，我们开发了ESM-Ezy，这是一种利用ESM-1b蛋白质语言模型和语义空间中的相似性计算的酶挖掘策略。使用ESM-Ezy，我们鉴定出具有优异催化特性的新型多铜氧化酶（MCO），在至少一种特性（包括催化效率、耐热性和有机溶剂耐受性以及pH稳定性）方面优于查询酶（QE）的成功率达到44%。值得注意的是，51%的MCO在环境修复应用中表现出色，并且一些展现出独特的结构基序和独特的活性中心，增强了它们的功能。除了MCO，鉴定出的40%的L-天冬酰胺酶显示出比QE更高的比活性和催化效率。因此，ESM-Ezy为发现具有低序列相似性的高性能生物催化剂提供了一种有前景的方法，加速了用于工业应用的酶的发现。
