Robust enzyme discovery and engineering with deep learning using CataPro

Zechen Wang, Dongqi Xie, Dong Wu, Xiaozhou Luo, Sheng Wang, Yangyang Li, Yanmei Yang (), Weifeng Li () and Liangzhen Zheng ()
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Zechen Wang: Shandong University
Dongqi Xie: Shanghai Zelixir Biotech Co. Ltd
Dong Wu: Shanghai Zelixir Biotech Co. Ltd
Xiaozhou Luo: Chinese Academy of Sciences
Sheng Wang: Shanghai Zelixir Biotech Co. Ltd
Yangyang Li: Shandong University
Yanmei Yang: Shandong Normal University
Weifeng Li: Shandong University
Liangzhen Zheng: Shanghai Zelixir Biotech Co. Ltd

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

Abstract: Abstract Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased datasets to evaluate the actual performance of these methods and proposed a deep learning model, CataPro, based on pre-trained models and molecular fingerprints to predict turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat/Km). Compared with previous baseline models, CataPro demonstrates clearly enhanced accuracy and generalization ability on the unbiased datasets. In a representational enzyme mining project, by combining CataPro with traditional methods, we identified an enzyme (SsCSO) with 19.53 times increased activity compared to the initial enzyme (CSO2) and then successfully engineered it to improve its activity by 3.34 times. This reveals the high potential of CataPro as an effective tool for future enzyme discovery and modification.

Date: 2025
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DOI: 10.1038/s41467-025-58038-4

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