Cutting-edge computational approaches in enzyme design and activity enhancement

被引:1
|
作者
Sun, Ruobin [1 ,2 ]
Wu, Dan [1 ,2 ]
Chen, Pengcheng [1 ,2 ]
Zheng, Pu [1 ,2 ]
机构
[1] Jiangnan Univ, Sch Biotechnol, 1800 Lihu Rd, Wuxi 214122, Peoples R China
[2] Jiangnan Univ, Sch Biotechnol, Key Lab Ind Biotechnol Educ, Wuxi 214122, Peoples R China
关键词
Enzyme activity; Enzyme evolution; Molecular dynamics; Rosetta; Machine learning; Computational enzyme design; DIRECTED EVOLUTION; COUPLING ANALYSIS; CONFORMATIONAL DYNAMICS; PROTEIN; PREDICTION; METALLOENZYME; PERFORMANCE; REDUCTION; SITE;
D O I
10.1016/j.bej.2024.109510
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Enzyme activity is crucial in biocatalysis, making methods to enhance enzyme performance a major focus of research. Computational design provides an efficient approach to boosting enzyme activity, thereby expanding its applications across various fields. This review highlights three main computational methods: molecular dynamics simulations, Rosetta, and machine learning, and explores recent advances in their use for rapidly enhancing enzyme activity in enzyme engineering. These techniques provide a novel perspective on enzyme activity optimization, significantly reducing the complexity of traditional screening processes. By integrating these advanced computational approaches, high-activity enzymes can be designed more rapidly, accelerating progress in protein engineering and synthetic biology.
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页数:13
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