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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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