Eicher Johann J, Snoep Jacky L, Rohwer Johann M
Triple-J Group for Molecular Cell Physiology, Department of Biochemistry, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
Metabolites. 2012 Nov 6;2(4):818-43. doi: 10.3390/metabo2040818.
Enzyme kinetics for systems biology should ideally yield information about the enzyme's activity under in vivo conditions, including such reaction features as substrate cooperativity, reversibility and allostery, and be applicable to enzymatic reactions with multiple substrates. A large body of enzyme-kinetic data in the literature is based on the uni-substrate Michaelis-Menten equation, which makes unnatural assumptions about enzymatic reactions (e.g., irreversibility), and its application in systems biology models is therefore limited. To overcome this limitation, we have utilised NMR time-course data in a combined theoretical and experimental approach to parameterize the generic reversible Hill equation, which is capable of describing enzymatic reactions in terms of all the properties mentioned above and has fewer parameters than detailed mechanistic kinetic equations; these parameters are moreover defined operationally. Traditionally, enzyme kinetic data have been obtained from initial-rate studies, often using assays coupled to NAD(P)H-producing or NAD(P)H-consuming reactions. However, these assays are very labour-intensive, especially for detailed characterisation of multi-substrate reactions. We here present a cost-effective and relatively rapid method for obtaining enzyme-kinetic parameters from metabolite time-course data generated using NMR spectroscopy. The method requires fewer runs than traditional initial-rate studies and yields more information per experiment, as whole time-courses are analyzed and used for parameter fitting. Additionally, this approach allows real-time simultaneous quantification of all metabolites present in the assay system (including products and allosteric modifiers), which demonstrates the superiority of NMR over traditional spectrophotometric coupled enzyme assays. The methodology presented is applied to the elucidation of kinetic parameters for two coupled glycolytic enzymes from Escherichia coli (phosphoglucose isomerase and phosphofructokinase). 31P-NMR time-course data were collected by incubating cell extracts with substrates, products and modifiers at different initial concentrations. NMR kinetic data were subsequently processed using a custom software module written in the Python programming language, and globally fitted to appropriately modified Hill equations.
系统生物学中的酶动力学理想情况下应能提供有关酶在体内条件下活性的信息，包括底物协同性、可逆性和别构等反应特征，并适用于多底物的酶促反应。文献中大量的酶动力学数据基于单底物米氏方程，该方程对酶促反应做出了不自然的假设（如不可逆性），因此其在系统生物学模型中的应用受到限制。为克服这一限制，我们采用理论与实验相结合的方法，利用核磁共振时间进程数据对通用可逆希尔方程进行参数化，该方程能够根据上述所有特性描述酶促反应，且参数比详细的机理动力学方程少；此外，这些参数是通过操作定义的。传统上，酶动力学数据是通过初始速率研究获得的，通常使用与产生或消耗NAD(P)H的反应偶联的测定法。然而，这些测定法非常耗费人力，特别是对于多底物反应的详细表征。我们在此提出一种经济高效且相对快速的方法，可从使用核磁共振光谱法生成的代谢物时间进程数据中获取酶动力学参数。该方法所需的运行次数比传统的初始速率研究少，并且每个实验能产生更多信息，因为整个时间进程都被分析并用于参数拟合。此外，这种方法允许实时同时定量测定系统中存在的所有代谢物（包括产物和别构调节剂），这证明了核磁共振相对于传统分光光度偶联酶测定法的优越性。所提出的方法被应用于阐明来自大肠杆菌的两种糖酵解偶联酶（磷酸葡萄糖异构酶和磷酸果糖激酶）的动力学参数。通过将细胞提取物与不同初始浓度的底物、产物和调节剂孵育，收集了³¹P - 核磁共振时间进程数据。随后使用用Python编程语言编写的定制软件模块处理核磁共振动力学数据，并将其整体拟合到适当修改的希尔方程。
