Lakshmanan Meiyappan, Chung Bevan Kai-Sheng, Liu Chengcheng, Kim Seon-Won, Lee Dong-Yup
Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, Singapore.
J Bioinform Comput Biol. 2013 Dec;11(6):1343006. doi: 10.1142/S0219720013430063. Epub 2013 Nov 21.
Cofactors, such as NAD(H) and NADP(H), play important roles in energy transfer within the cells by providing the necessary redox carriers for a myriad of metabolic reactions, both anabolic and catabolic. Thus, it is crucial to establish the overall cellular redox balance for achieving the desired cellular physiology. Of several methods to manipulate the intracellular cofactor regeneration rates, altering the cofactor specificity of a particular enzyme is a promising one. However, the identification of relevant enzyme targets for such cofactor specificity engineering (CSE) is often very difficult and labor intensive. Therefore, it is necessary to develop more systematic approaches to find the cofactor engineering targets for strain improvement. Presented herein is a novel mathematical framework, cofactor modification analysis (CMA), developed based on the well-established constraints-based flux analysis, for the systematic identification of suitable CSE targets while exploring the global metabolic effects. The CMA algorithm was applied to E. coli using its genome-scale metabolic model, iJO1366, thereby identifying the growth-coupled cofactor engineering targets for overproducing four of its native products: acetate, formate, ethanol, and lactate, and three non-native products: 1-butanol, 1,4-butanediol, and 1,3-propanediol. Notably, among several target candidates for cofactor engineering, glyceraldehyde-3-phosphate dehydrogenase (GAPD) is the most promising enzyme; its cofactor modification enhanced both the desired product and biomass yields significantly. Finally, given the identified target, we further discussed potential mutational strategies for modifying cofactor specificity of GAPD in E. coli as suggested by in silico protein docking experiments.
辅因子，如NAD(H)和NADP(H)，通过为众多合成代谢和分解代谢的代谢反应提供必要的氧化还原载体，在细胞内的能量转移中发挥重要作用。因此，建立整体细胞氧化还原平衡对于实现理想的细胞生理功能至关重要。在几种操纵细胞内辅因子再生速率的方法中，改变特定酶的辅因子特异性是一种很有前景的方法。然而，识别用于这种辅因子特异性工程（CSE）的相关酶靶点通常非常困难且耗费人力。因此，有必要开发更系统的方法来寻找用于菌株改良的辅因子工程靶点。本文介绍了一种基于成熟的基于约束的通量分析开发的新型数学框架——辅因子修饰分析（CMA），用于在探索全局代谢效应的同时系统地识别合适的CSE靶点。使用大肠杆菌的基因组规模代谢模型iJO1366将CMA算法应用于大肠杆菌，从而识别出用于过量生产其四种天然产物（乙酸盐、甲酸盐、乙醇和乳酸盐）以及三种非天然产物（1-丁醇、1,4-丁二醇和1,3-丙二醇）的生长耦合辅因子工程靶点。值得注意的是，在几种辅因子工程的候选靶点中，3-磷酸甘油醛脱氢酶（GAPD）是最有前景的酶；其辅因子修饰显著提高了所需产物和生物量产量。最后，鉴于已识别的靶点，我们进一步讨论了如计算机模拟蛋白质对接实验所建议的用于修饰大肠杆菌中GAPD辅因子特异性的潜在突变策略。
