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<p>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 (<i>k</i><sub><i>c</i><i>a</i><i>t</i></sub>), Michaelis constant (<i>K</i><sub><i>m</i></sub>), and catalytic efficiency (<i>k</i><sub><i>c</i><i>a</i><i>t</i></sub>/<i>K</i><sub><i>m</i></sub>). 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.</p>

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Robust enzyme discovery and engineering with deep learning using CataPro

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月20日

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  • Zechen Wang,
  • Dongqi Xie,
  • Dong Wu,
  • Xiaozhou Luo,
  • Sheng Wang,
  • Yangyang Li,
  • Yanmei Yang,
  • Weifeng Li,
  • Liangzhen Zheng

摘要

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.