Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
School of Light Industry and Engineering, South China University of Technology, Guangzhou 510006, China.
Int J Biol Macromol. 2024 Oct;277(Pt 4):134530. doi: 10.1016/j.ijbiomac.2024.134530. Epub 2024 Aug 5.
Enhancing the thermostability of enzymes is crucial for industrial applications. Methods such as directed evolution are often limited by the huge sequence space and combinatorial explosion, making it difficult to obtain optimal mutants. In recent years, machine learning (ML)-guided protein engineering has become an attractive tool because of its ability to comprehensively explore the sequence space of enzymes and discover superior mutants. This study employed ML to perform combinatorial mutation design on the pectin lyase PMGL-Ba from Bacillus licheniformis, aiming to improve its thermostability. First, 18 single-point mutants with enhanced thermostability were identified through semi-rational design. Subsequently, the initial library containing a small number of low-order mutants was utilized to construct an ML model to explore the combinatorial sequence space (theoretically 196,608 mutants) of single-point mutants. The results showed that the ML-predicted second library was successfully enriched with highly thermostable combinatorial mutants. After one iteration of learning, the best-performing combinatorial mutant in the third library, P36, showed a 67-fold and 39-fold increase in half-life at 75 °C and 80 °C, respectively, as well as a 2.1-fold increase in activity. Structural analysis and molecular dynamics simulations provided insights into the improved performance of the engineered enzyme.
提高酶的热稳定性对于工业应用至关重要。定向进化等方法常常受到巨大的序列空间和组合爆炸的限制，难以获得最优的突变体。近年来，机器学习 (ML) 指导的蛋白质工程因其能够全面探索酶的序列空间并发现优越的突变体而成为一种有吸引力的工具。本研究采用 ML 对来自地衣芽孢杆菌的果胶裂解酶 PMGL-Ba 进行组合突变设计，旨在提高其热稳定性。首先，通过半理性设计鉴定了 18 个具有增强热稳定性的单点突变体。随后，利用包含少量低阶突变体的初始文库构建了一个 ML 模型来探索单点突变体的组合序列空间（理论上有 196,608 个突变体）。结果表明，ML 预测的第二文库成功地富集了具有高耐热性的组合突变体。在一轮学习后，第三文库中表现最好的组合突变体 P36 在 75°C 和 80°C 时半衰期分别提高了 67 倍和 39 倍，活性提高了 2.1 倍。结构分析和分子动力学模拟为工程酶性能的提高提供了见解。
