生物催化
体内
化学
计算生物学
合成生物学
生物
生物化学
细胞生物学
生化工程
酶
生物技术
代谢工程
生物转化
基因
模式生物
纳米技术
定向分子进化
作者
Qing Yang,Fang-Ying Zhu,Xiaojian Zhang,Xue Cai,Jie Zhou,Zhi-qiang Liu,Yupeng Zheng
标识
DOI:10.1021/acssynbio.5c00951
摘要
In vivo multiple-enzyme cascades have attracted considerable interest for their ability to provide a native microenvironment that supports enzymatic activity and membrane protein function. This review outlined four pivotal strategies for their optimization, increasingly empowered by Artificial Intelligence (AI): (1) enhancing enzyme performance by enzyme discovery and engineering; (2) precisely modulating enzyme expression via rationally designed genetic regulatory elements; (3) implementing spatial and stoichiometric control using protein, nucleic acid, or synthetic scaffolds and compartments; and (4) employing multimodule systems including multiple cell modules and hybrid in vivo/in vitro cascades. Advances in AI accelerate these strategies, enabling novel approaches such as de novo protein design, directed evolution, and the computational design of genetic parts and supramolecular scaffolds. The integrated implementation of these methods substantially increased target compound titers. This lays a strong foundation for industrial implementation. However, several key challenges remain to be addressed.
科研通智能强力驱动
Strongly Powered by AbleSci AI