DOAJ Open Access 2025

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 +4 lainnya

Abstrak

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.

Topik & Kata Kunci

Science

Penulis (9)

Nan Zheng

Yongchao Cai

Zehua Zhang

Huimin Zhou

Yu Deng

Shuang Du

Mai Tu

Wei Fang

Xiaole Xia

Format Sitasi

APA MLA BibTeX

Zheng, N., Cai, Y., Zhang, Z., Zhou, H., Deng, Y., Du, S. et al. (2025). Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy. https://doi.org/10.1038/s41467-025-55944-5

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Informasi Jurnal

Tahun Terbit: 2025
Sumber Database: DOAJ
DOI: 10.1038/s41467-025-55944-5
Akses: Open Access ✓