Green biomanufacturing is an emerging paradigm for chemical synthesis that addresses the energy and environmental challenges of the modern era. A key strategy for green biomanufacturing is the use of microbial cell factories (MCFs), which exploit the capabilities of microbial cells as efficient production platforms for various applications, such as biofuels (Xu et al., 2013), chemicals (Choi et al., 2024; Soong et al., 2023), pharmaceuticals (Pandey et al., 2016; Yan et al., 2023), food (Lv et al., 2021; Sun et al., 2021) and bioplastics (Zhang et al., 2020). The development of MCFs aims to rewiring the cellular metabolism to enhance the production of endogenous metabolites or enable the synthesis of novel products (Nielsen and Keasling, 2016). Recent developments in de novo synthesis pathways for vinblastine (Zhang et al., 2022) and tropane alkaloids (Srinivasan and Smolke, 2020) exemplify the capacity of MCFs in facilitating the biosynthesis of complex natural products. Additionally, other high-value natural products and their derivatives, including polyamines (Qin et al., 2021; Reed and Alper, 2021) and fatty acids (Zhu et al., 2020), have been investigated.

Protein components constitute one of the foundational elements in the construction of cellular factories. Among these, enzymes, serving as catalytic components, are pivotal in governing metabolic pathways within cellular factories. Additionally, transport proteins are responsible for regulating the transport of substances both into and out of cells, ensuring the efficient entry of substrates and effective expulsion of products. In addition to direct manipulation of biosynthetic proteins, genetically encoded biosensors play a pivotal role in enhancing the efficacy of MCFs. These biosensors expedite strain and enzyme refinement through the implementation of high-throughput screening methods and adaptive laboratory evolution. Moreover, they bolster the fine-tuning of heterologous pathways by incorporating dynamic control circuits (Mahr and Frunzke, 2016). However, when integrating enzymes into nature or non-nature pathways, their applications are sometimes restricted by limited enzymatic activity, undesired selectivity, narrow substrate spectrum, and even loss of function in heterologous hosts (Li et al., 2020a). Moreover, hurdles such as the inability of biosensors to satisfy screening criteria (Snoek et al., 2019) and the suboptimal efficiency of endogenous transport proteins (Ahmed et al., 2021) present constraints within the domain of MCFs.

In this context, protein engineering typically develop novel proteins with specific, desirable properties by altering amino acid sequences found in nature, emerges as a promising approach to address these challenges. Protein engineering techniques include directed evolution, semi-rational design, and computational design (Fig. 1). Depending on the availability of protein information, researchers employ both irrational and rational design methods to obtain protein elements with expected functions. Through these methods, the activity (Lovelock et al., 2022), selectivity (Wu et al., 2022a), promiscuity (Cui et al., 2021) and stability (Adi Goldenzweig, 2018) of enzymes, as well as the performance of biosensors (Snoek et al., 2019; Yu et al., 2023a) and transporters (van der Hoek and Borodina, 2020) can be effectively improved. Furthermore, with the advancement of computational power, novel technologies such as machine learning-aided protein engineering (Lu et al., 2022; Mazurenko et al., 2019) and de novo protein design (Chen and Arnold, 2020; Kiss et al., 2013; Lovelock et al., 2022) have provided new solutions for the efficient engineering or design of protein components.

This review will summarize the state-of-the-art protein engineering technologies, ranging from directed evolution to rational design. The effective application of protein engineering to enhance the properties of enzymes for efficient pathways will be systematically reviewed. Furthermore, the potential of applying these techniques to improve the efficiency of transcription factor-based biosensors will be explored. Finally, we will provide a perspective on the future prospects of protein engineering and its potential impact on the advancement of high-performance MCFs.