Eble Johannes A
Institute of Physiological Chemistry and Pathobiochemistry, University of Münster;
J Vis Exp. 2018 Feb 15(132):57334. doi: 10.3791/57334.
The dissociation constant describes the interaction between two partners in the binding equilibrium and is a measure of their affinity. It is a crucial parameter to compare different ligands, e.g., competitive inhibitors, protein isoforms and mutants, for their binding strength to a binding partner. Dissociation constants are determined by plotting concentrations of bound versus free ligand as binding curves. In contrast, titration curves, in which a signal that is proportional to the concentration of bound ligand is plotted against the total concentration of added ligand, are much easier to record. The signal can be detected spectroscopically and by enzyme-linked immunosorbent assay (ELISA). This is exemplified in a protocol for a titration ELISA that measures the binding of the snake venom-derived rhodocetin to its immobilized target domain of α2β1 integrin. Titration ELISAs are versatile and widely used. Any pair of interacting proteins can be used as immobilized receptor and soluble ligand, provided that both proteins are pure, and their concentrations are known. The difficulty so far has been to determine the dissociation constant from a titration curve. In this study, a mathematical function underlying titration curves is introduced. Without any error-prone graphical estimation of a saturation yield, this algorithm allows processing of the raw data (signal intensities at different concentrations of added ligand) directly by mathematical evaluation via non-linear regression. Thus, several titration curves can be recorded simultaneously and transformed into a set of characteristic parameters, among them the dissociation constant and the concentration of binding-active receptor, and they can be evaluated statistically. When combined with this algorithm, titration ELISAs gain the advantage of directly presenting the dissociation constant. Therefore, they may be used more efficiently in the future.
解离常数描述了结合平衡中两个结合伙伴之间的相互作用，是衡量它们亲和力的指标。它是比较不同配体（如竞争性抑制剂、蛋白质同工型和突变体）与结合伙伴结合强度的关键参数。解离常数通过绘制结合配体与游离配体的浓度作为结合曲线来确定。相比之下，滴定曲线绘制起来要容易得多，其中与结合配体浓度成正比的信号与添加配体的总浓度相对应。该信号可以通过光谱法和酶联免疫吸附测定（ELISA）进行检测。这在一种滴定ELISA方案中得到了体现，该方案用于测量蛇毒来源的红藻氨酸与α2β1整合素固定化靶结构域的结合。滴定ELISA用途广泛且应用广泛。任何一对相互作用的蛋白质都可以用作固定化受体和可溶性配体，前提是这两种蛋白质都是纯的，并且它们的浓度是已知的。到目前为止，难点在于从滴定曲线中确定解离常数。在本研究中，引入了滴定曲线背后的数学函数。该算法无需对饱和产量进行任何容易出错的图形估计，允许通过非线性回归直接对原始数据（添加配体不同浓度下的信号强度）进行数学评估。因此，可以同时记录几条滴定曲线，并将其转化为一组特征参数，其中包括解离常数和结合活性受体的浓度，并且可以进行统计学评估。当与该算法结合使用时，滴定ELISA具有直接呈现解离常数的优势。因此，它们未来可能会得到更有效的应用。
