Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy

Nan Zheng, Yongchao Cai, Zehua Zhang, Huimin Zhou, Yu Deng, Shuang Du, Mai Tu, Wei Fang and Xiaole Xia ()
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Nan Zheng: Jiangnan University
Yongchao Cai: Jiangnan University
Zehua Zhang: Jiangnan University
Huimin Zhou: Jiangnan University
Yu Deng: Jiangnan University
Shuang Du: Jiangnan University
Mai Tu: Jiangnan University
Wei Fang: Jiangnan University
Xiaole Xia: Jiangnan University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract The pursuit of obtaining enzymes with high activity and stability remains a grail in enzyme evolution due to the stability-activity trade-off. Here, we develop an isothermal compressibility-assisted dynamic squeezing index perturbation engineering (iCASE) strategy to construct hierarchical modular networks for enzymes of varying complexity. Molecular mechanism analysis elucidates that the peak of adaptive evolution is reached through a structural response mechanism among variants. Furthermore, this dynamic response predictive model using structure-based supervised machine learning is established to predict enzyme function and fitness, demonstrating robust performance across different datasets and reliable prediction for epistasis. The universality of the iCASE strategy is validated by four sorts of enzymes with different structures and catalytic types. This machine learning-based iCASE strategy provides guidance for future research on the fitness evolution of enzymes.

Date: 2025
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DOI: 10.1038/s41467-025-55944-5

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