Machine learning-guided multi-site combinatorial mutagenesis enhances the thermostability of pectin lyase

• 全文链接

机器学习引导的多位点组合突变增强了果胶裂合酶的热稳定性

关键词：

来源：

International Journal of Biological Macromolecules

全文链接：

//agri.nais.net.cn/topic/downloadFile/384e3e12-325f-4a8d-9152-15082109ce6b

来源地址：

https://www.sciencedirect.com/science/article/pii/S0141813024053352?via%3Dihub

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：

2024-08-10

摘要：

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.