Ren Yuyao, Celińska Ewelina, Cai Peng, Zhou Yongjin J
Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Chem Bio Eng. 2025 May 1;2(8):449-459. doi: 10.1021/cbe.5c00002. eCollection 2025 Aug 28.
The booming field of synthetic biology and metabolic engineering provides promising approaches for sustainable manufacturing of chemicals from renewable feedstocks with microbial cell factories. Classical metabolic engineering strategies mainly focus on altering gene expression levels and enzyme concentrations to improve the metabolic fluxes of specific pathways. However, the impact and limitations of enzyme properties, which are usually ignored in classical metabolic engineering efforts, can hinder further optimization of microbial cell factories. Protein engineering and directed evolution are powerful tools for modifying proteins to achieve desirable properties, and they have been integrated into metabolic engineering efforts to build highly efficient metabolic pathways and optimal industrial chassis. In this review, we present traditional and data-driven strategies and techniques of directed evolution, including random library design, semirational design, smart library design, and continuous evolution. We also discuss how these directed evolution strategies have been applied in metabolic engineering toward superphenotypes that cannot be achieved through simple gene overexpression or knockout. Finally, we discuss the challenges of applying protein engineering in metabolic engineering and the prospects for accelerating the directed evolution workflow using the state-of-art technologies.
合成生物学和代谢工程领域的蓬勃发展为利用微生物细胞工厂从可再生原料可持续制造化学品提供了有前景的方法。经典的代谢工程策略主要集中在改变基因表达水平和酶浓度以改善特定途径的代谢通量。然而,酶的性质所产生的影响和局限性在经典代谢工程工作中通常被忽视,这可能会阻碍微生物细胞工厂的进一步优化。蛋白质工程和定向进化是修饰蛋白质以获得理想性质的强大工具,它们已被整合到代谢工程工作中,以构建高效的代谢途径和优化的工业底盘。在本综述中,我们介绍了定向进化的传统策略和数据驱动策略及技术,包括随机文库设计、半理性设计、智能文库设计和连续进化。我们还讨论了这些定向进化策略如何应用于代谢工程以实现通过简单基因过表达或敲除无法实现的超表型。最后,我们讨论了在代谢工程中应用蛋白质工程面临的挑战以及使用最新技术加速定向进化工作流程的前景。