Coussens Nathan P, Schuck Peter, Zhao Huaying
Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, U.S.A.
J Chem Thermodyn. 2012 Sep 1;52:95-107. doi: 10.1016/j.jct.2012.02.008.
Isothermal titration calorimetry (ITC) is a traditional and powerful method for studying the linkage of ligand binding to proton uptake or release. The theoretical framework has been developed for more than two decades and numerous applications have appeared. In the current work, we explored strategic aspects of experimental design. To this end, we simulated families of ITC data sets that embed different strategies with regard to the number of experiments, range of experimental pH, buffer ionization enthalpy, and temperature. We then re-analyzed the families of data sets in the context of global analysis, employing a proton linkage binding model implemented in the global data analysis platform SEDPHAT, and examined the information content of all data sets by a detailed statistical error analysis of the parameter estimates. In particular, we studied the impact of different assumptions about the knowledge of the exact concentrations of the components, which in practice presents an experimental limitation for many systems. For example, the uncertainty in concentration may reflect imperfectly known extinction coefficients and stock concentrations or may account for different extents of partial inactivation when working with proteins at different pH values. Our results show that the global analysis can yield reliable estimates of the thermodynamic parameters for intrinsic binding and protonation, and that in the context of the global analysis the exact molecular component concentrations may not be required. Additionally, a comparison of data from different experimental strategies illustrates the benefit of conducting experiments at a range of temperatures.
等温滴定量热法（ITC）是一种用于研究配体结合与质子吸收或释放之间联系的传统且强大的方法。其理论框架已经发展了二十多年，并且出现了大量应用。在当前工作中，我们探讨了实验设计的策略方面。为此，我们模拟了一系列ITC数据集，这些数据集在实验次数、实验pH范围、缓冲液电离焓和温度方面嵌入了不同的策略。然后，我们在全局分析的背景下重新分析这些数据集，使用全局数据分析平台SEDPHAT中实现的质子连接结合模型，并通过对参数估计进行详细的统计误差分析来检查所有数据集的信息含量。特别是，我们研究了关于组分精确浓度知识的不同假设的影响，这在实际中对许多系统来说是一个实验限制。例如，浓度的不确定性可能反映了不完全已知的消光系数和储备浓度，或者可能解释了在不同pH值下处理蛋白质时不同程度的部分失活。我们的结果表明，全局分析可以对内在结合和质子化的热力学参数给出可靠的估计，并且在全局分析的背景下可能不需要精确的分子组分浓度。此外，对来自不同实验策略的数据进行比较说明了在一系列温度下进行实验的好处。
