Prieto M A, Vazquez J A, Murado M A
Superior de Investigaciones Científicas, Instituto de Investigaciones Marinas (IIM-CSIC), Spain.
Analyst. 2015 May 21;140(10):3587-602. doi: 10.1039/c4an02136c. Epub 2015 Apr 9.
We suggest a new and general model to describe the effects of temperature (T) and pH on the catalytic activity of enzymes. Despite the abundance of models to describe those effects, the current proposals are unsatisfactory, except for specific experimental cases in which the interactive mechanism between the two variables does not exist. For both variables, our solution analyses the activated and deactivated phases of an enzyme as phenomena of different nature. The system is described with independent probability functions. The interactive effects between T and pH are introduced with simple auxiliary functions. These functions describe the variations induced by each variable in the parameters that define the effects of the other. The structure of the resulting equation is, in theory and practice, very regular, which facilitates its use, and it is highly descriptive in different scenarios with or without interactive effects. The model was tested on three different enzymatic systems which are specifically designed to produce data for the evaluation of the effect of T and pH on the enzyme activity (A). Afterwards, our model was validated using results from other authors. Briefly, the authors found that: (1) other available models that were compared with our proposal were inefficient and in all cases our model provided the only statistically consistent solution; (2) in four cases, the enzymatic activity could only be explained if interactive effects are accepted; (3) synergy and antagonism concepts for the interaction between T and pH were described and classified; and (4) our solution is universal and independent of the structure of an enzyme and the reaction concerned.
我们提出了一种全新的通用模型，用于描述温度（T）和pH值对酶催化活性的影响。尽管已有大量模型来描述这些影响，但目前的模型并不令人满意，除了某些特定实验情况，即两个变量之间不存在相互作用机制的情况。对于这两个变量，我们的解决方案将酶的活化和失活阶段分析为不同性质的现象。该系统用独立的概率函数来描述。T和pH之间的相互作用通过简单的辅助函数引入。这些函数描述了每个变量在定义另一个变量影响的参数中所引起的变化。所得方程的结构在理论和实践上都非常规则，便于使用，并且在有或没有相互作用影响的不同情况下都具有高度的描述性。该模型在三个不同的酶系统上进行了测试，这些酶系统是专门设计用于生成数据，以评估T和pH对酶活性（A）的影响。之后，我们使用其他作者的结果对模型进行了验证。简而言之，作者发现：（1）与我们的模型相比，其他现有模型效率低下，在所有情况下我们的模型都提供了唯一统计上一致的解决方案；（2）在四种情况下，只有接受相互作用影响才能解释酶活性；（3）描述并分类了T和pH之间相互作用的协同和拮抗概念；（4）我们的解决方案具有通用性，与酶的结构和相关反应无关。
