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.
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Yang et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69ca134b883daed6ee095282 — DOI: https://doi.org/10.1021/acssynbio.5c00951
Qing Yang
Fang-Ying Zhu
Xiaojian Zhang
ACS Synthetic Biology
Zhejiang University of Technology
Chemical Synthesis Lab
Biocon (India)
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