Robust enzyme discovery and engineering with deep learning using CataPro

• 全文链接

使用 CataPro 进行深度学习，实现稳健的酶发现和工程设计

关键词：

来源：

Nature Communications

全文链接：

//agri.nais.net.cn/topic/downloadFile/178cc881-644d-463f-811f-2074b1fb234c

来源地址：

https://www.nature.com/articles/s41467-025-58038-4

资源所属：

饲料用酶工程

类型：

学术文献

语种：

英语

原文发布日期：

2025-03-20

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.