Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Key Laboratory of Industrial Biocatalysis (Tsinghua University), the Ministry of Education, Beijing 100084, China.
Tanwei College, Tsinghua University, Beijing 100084, China.
Biotechnol Adv. 2024 Oct;75:108417. doi: 10.1016/j.biotechadv.2024.108417. Epub 2024 Jul 20.
Protein expression is a critical process in diverse biological systems. For Escherichia coli, a widely employed microbial host in industrial catalysis and healthcare, researchers often face significant challenges in constructing recombinant expression systems. To maximize the potential of E. coli expression systems, it is essential to address problems regarding the low or absent production of certain target proteins. This article presents viable solutions to the main factors posing challenges to heterologous protein expression in E. coli, which includes protein toxicity, the intrinsic influence of gene sequences, and mRNA structure. These strategies include specialized approaches for managing toxic protein expression, addressing issues related to mRNA structure and codon bias, advanced codon optimization methodologies that consider multiple factors, and emerging optimization techniques facilitated by big data and machine learning.
蛋白质表达是各种生物系统中的一个关键过程。对于大肠杆菌,作为工业催化和医疗保健中广泛应用的微生物宿主,研究人员在构建重组表达系统时经常面临着巨大的挑战。为了最大限度地发挥大肠杆菌表达系统的潜力,解决某些目标蛋白产量低或缺失的问题至关重要。本文提出了大肠杆菌中异源蛋白表达面临的主要因素的可行解决方案,这些因素包括蛋白质毒性、基因序列的内在影响和 mRNA 结构。这些策略包括专门用于管理毒性蛋白表达的方法、解决与 mRNA 结构和密码子偏性相关的问题、考虑多种因素的高级密码子优化方法,以及利用大数据和机器学习的新兴优化技术。