Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Malaysia.
Tropical Medicine & Biology Multidisciplinary Platform, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
Appl Microbiol Biotechnol. 2020 Apr;104(8):3253-3266. doi: 10.1007/s00253-020-10454-w. Epub 2020 Feb 19.
Over the past few decades, Escherichia coli (E. coli) remains the most favorable host among the microbial cell factories for the production of soluble recombinant proteins. Recombinant protein production (RPP) via E. coli is optimized at the level of gene expression (expression level) and the process condition of fermentation (process level). Presently, the reported studies do not give a clear view on the selection of methods employed in the optimization of RPP. Here, we have reviewed various optimization methods and their preferences with respect to the factors at expression and process levels to achieve the optimal levels of soluble RPP. With a greater understanding of these optimization methods, we proposed a stepwise methodology linking the factors from both levels for optimizing the production of soluble recombinant protein in E. coli. The proposed methodology is further explained through five sets of examples demonstrating the optimization of RPP at both expression and process levels.Key Points• Stepwise methodology of optimizing recombinant protein production is proposed.• In silico tools can facilitate the optimization of gene- and protein-based factors.• Optimization of gene- and protein-based factors aids host-vector selection.• Statistical optimization is preferred for achieving optimal levels of process factors.
在过去的几十年中,大肠杆菌(E. coli)仍然是微生物细胞工厂中生产可溶性重组蛋白的最理想宿主。通过大肠杆菌进行重组蛋白生产(RPP)在基因表达水平(表达水平)和发酵过程条件(过程水平)上进行优化。目前,报告的研究并没有清楚地说明用于优化 RPP 的方法选择。在这里,我们回顾了各种优化方法及其在表达和过程水平的因素方面的偏好,以达到可溶性 RPP 的最佳水平。通过对这些优化方法有了更深入的了解,我们提出了一种逐步的方法,将两个水平的因素联系起来,以优化大肠杆菌中可溶性重组蛋白的生产。通过五组示例进一步解释了该方法,这些示例演示了在表达和过程水平上优化 RPP。
关键点
• 提出了优化重组蛋白生产的分步方法。
• 计算工具可以促进基因和蛋白质因素的优化。
• 优化基因和蛋白质因素有助于宿主载体的选择。
• 统计优化是实现过程因素最佳水平的首选。