The Department of Microbiology and Immunology, Albert Einstein College of Medicine, 1300 Morris Park Ave, Bronx, New York 10461-1926, USA.
J Phys Chem B. 2010 Dec 16;114(49):16131-6. doi: 10.1021/jp1055528. Epub 2010 Aug 24.
Progress curve analysis has been used sparingly in studies of enzyme-catalyzed reactions due largely to the complexity of the integrated rate expressions used in data analysis. Using an experimental design that simplifies the analysis, the advantages and limitations of progress curve experiments are explored in a study of four different enzyme-catalyzed reactions. The approach involves relatively simple protocols, requires 20-25% of the materials, and provides 10- to 20-fold signal enhancements compared to analogous initial rate studies. Product inhibition, which complicates integrated rate analysis, was circumvented using cloned, purified enzymes that remove the products and draw the reaction forward. The resulting progress curves can be transformed into the equivalent of thousands of initial rate and [S] measurements and, due to the absence of product inhibition, are plotted in the familiar, linear double-reciprocal format. Allowing product to accumulate during a reaction produces a continuously changing substrate/product ratio that can be used as the basis for obtaining product inhibition constants and to distinguish among the three classical inhibition mechanisms. Algebraic models describing the double-reciprocal patterns obtained from such inhibition studies are presented. The virtual continuum of substrate concentrations that occurs during a progress curve experiment provides a nearly errorless set of relative concentrations that results in remarkably precise data; kinetic constant standard deviations are on the order of 0.5%.
由于在数据分析中使用的积分速率表达式非常复杂,因此在研究酶催化反应中,很少使用进度曲线分析。本研究通过采用简化分析的实验设计,探索了进度曲线实验在四种不同酶催化反应中的优势和局限性。该方法涉及相对简单的方案,与类似的初始速率研究相比,仅需 20-25%的材料,可提供 10-20 倍的信号增强。使用克隆的、纯化的酶来消除产物并推动反应向前进行,可以避免因产物抑制而使积分速率分析复杂化。所得的进度曲线可以转化为等效的数千个初始速率和[S]测量值,并且由于不存在产物抑制,可以按照熟悉的线性双倒数格式进行绘制。在反应过程中允许产物积累会产生不断变化的底物/产物比,可以用作获得产物抑制常数的基础,并区分三种经典抑制机制。本文提出了描述此类抑制研究中获得的双倒数模式的代数模型。在进度曲线实验中,虚拟的连续底物浓度提供了几乎无误差的相对浓度集,从而产生非常精确的数据;动力学常数标准偏差约为 0.5%。