Download MendeleyESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties.
Qian, H., Wang, Y., Zhou, X., Gu, T., Wang, H., Lyu, H., Li, Z., Li, X., Zhou, H., Guo, C., Yuan, F., Wang, Y.(2025) Nat Commun 16: 3274-3274
- PubMed: 40188191 Search on PubMedSearch on PubMed Central
- DOI: https://doi.org/10.1038/s41467-025-58521-y
- Primary Citation Related Structures:
8Z59, 8Z5B - PubMed 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.
- School of Engineering, Westlake University, Hangzhou, 310014, Zhejiang, China.
Organizational Affiliation:
