Fang Huan, Dong Huina, Cai Tao, Zheng Ping, Li Haixing, Zhang Dawei, Sun Jibin
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
PLoS One. 2016 Mar 14;11(3):e0151149. doi: 10.1371/journal.pone.0151149. eCollection 2016.
In order to maximize the production of biologically-derived chemicals, kinetic analyses are first necessary for predicting the role of enzyme components and coordinating enzymes in the same reaction system. Precorrin-2 is a key precursor of cobalamin and siroheme synthesis. In this study, we sought to optimize the concentrations of several molecules involved in precorrin-2 synthesis in vitro: porphobilinogen synthase (PBGS), porphobilinogen deaminase (PBGD), uroporphyrinogen III synthase (UROS), and S-adenosyl-l-methionine-dependent urogen III methyltransferase (SUMT). Response surface methodology was applied to develop a kinetic model designed to maximize precorrin-2 productivity. The optimal molar ratios of PBGS, PBGD, UROS, and SUMT were found to be approximately 1:7:7:34, respectively. Maximum precorrin-2 production was achieved at 0.1966 ± 0.0028 μM/min, agreeing with the kinetic model's predicted value of 0.1950 μM/min. The optimal concentrations of the cofactor S-adenosyl-L-methionine (SAM) and substrate 5-aminolevulinic acid (ALA) were also determined to be 200 μM and 5 mM, respectively, in a tandem-enzyme assay. By optimizing the relative concentrations of these enzymes, we were able to minimize the effects of substrate inhibition and feedback inhibition by S-adenosylhomocysteine on SUMT and thereby increase the production of precorrin-2 by approximately five-fold. These results demonstrate the effectiveness of kinetic modeling via response surface methodology for maximizing the production of biologically-derived chemicals.
为了最大限度地提高生物衍生化学品的产量，首先需要进行动力学分析，以预测酶组分和协同酶在同一反应体系中的作用。维生素B12和钴胺素合成的关键前体是预钴胺素-2。在本研究中，我们试图在体外优化参与预钴胺素-2合成的几种分子的浓度：胆色素原合酶（PBGS）、胆色素原脱氨酶（PBGD）、尿卟啉原III合酶（UROS）和S-腺苷-L-甲硫氨酸依赖性尿卟啉原III甲基转移酶（SUMT）。应用响应面法建立动力学模型，以最大限度地提高预钴胺素-2的产量。发现PBGS、PBGD、UROS和SUMT的最佳摩尔比分别约为1:7:7:34。预钴胺素-2的最大产量为0.1966±0.0028μM/分钟，与动力学模型预测值0.1950μM/分钟一致。在串联酶测定中，辅因子S-腺苷-L-甲硫氨酸（SAM）和底物5-氨基乙酰丙酸（ALA）的最佳浓度也分别确定为200μM和5 mM。通过优化这些酶的相对浓度，我们能够将底物抑制和S-腺苷同型半胱氨酸对SUMT的反馈抑制作用降至最低，从而使预钴胺素-2的产量提高约五倍。这些结果证明了通过响应面法进行动力学建模对于最大化生物衍生化学品产量的有效性。
