Zhu Lei, Shabbir Shagufta H, Anslyn Eric V
Department of Chemistry and Biochemistry, The University of Texas at Austin, Austin, TX 78712, USA.
Chemistry. 2007;13(1):99-104. doi: 10.1002/chem.200600402.
The previously established enantioselective indicator-displacement assays (eIDAs) for the determination of concentration and enantiomeric excess (ee) require two spectroscopic measurements for each chiral sample. To further simplify the operation of eIDAs, we now introduce two innovative analytical methods, both of which utilize a dual-chamber quartz cuvette, which reduces the number of spectroscopic measurements from two to one. An attractive feature of this cuvette is that the concentration- and ee-dependent absorption data can be collected at the isosbestic points or transparent regions of the spectra recorded in each individual chamber, thereby reflecting optical changes that occur in the other chamber. Therefore, two independent equations, which are needed to solve the values of the two independent variables-concentration and ee-can be established with only a single spectroscopic measurement. The first method takes advantage of this feature in conjunction with a judicious choice of indicator/host combinations to generate concentration- and ee-dependent calibration curves. Our second method removes the requirement to measure equilibrium constants and molar absorptivities altogether through the use of artificial neural networks (ANNs). The most frequently used three-layer feed-forward network is generated, which relates the absorption data to concentration and ee of the samples by training with a back propagation procedure. Here, the data collection is not limited to the isosbestic points or transparent regions. Both approaches enabled accurate and rapid determination of concentration and ee of chiral samples. The technology removes the relative difficulty, which is the need for two separate measurements for concentration and ee respectively, of analyzing chiral samples compared to achiral samples. When implemented in a high-throughput format, this technology should greatly facilitate the discovery of asymmetric catalysts in the same way as conventional high-throughput screening assays.
先前建立的用于测定浓度和对映体过量（ee）的对映选择性指示剂置换分析（eIDAs），每个手性样品需要进行两次光谱测量。为了进一步简化eIDAs的操作，我们现在引入两种创新的分析方法，这两种方法都使用双腔石英比色皿，将光谱测量次数从两次减少到一次。这种比色皿的一个吸引人的特点是，可以在每个单独腔室记录的光谱的等吸收点或透明区域收集浓度和ee依赖性吸收数据，从而反映在另一个腔室中发生的光学变化。因此，仅通过一次光谱测量就可以建立两个独立的方程，用于求解两个独立变量——浓度和ee的值。第一种方法利用这一特点，结合明智地选择指示剂/主体组合，生成浓度和ee依赖性校准曲线。我们的第二种方法通过使用人工神经网络（ANNs）完全消除了测量平衡常数和摩尔吸光率的要求。生成了最常用的三层前馈网络，通过反向传播过程进行训练，将吸收数据与样品的浓度和ee相关联。在这里，数据收集不限于等吸收点或透明区域。这两种方法都能够准确、快速地测定手性样品的浓度和ee。与非手性样品相比，该技术消除了分析手性样品相对困难的地方，即分别需要对浓度和ee进行两次单独测量。当以高通量形式实施时，该技术应该会像传统高通量筛选分析一样，极大地促进不对称催化剂的发现。
