Department of Chemistry, Bowling Green State University, Bowling Green, OH, 43403, USA.
Department of Applied Chemistry, Graduate School of Urban Environmental Sciences, Tokyo Metropolitan University, Tokyo, 192-0397, Japan.
Chemistry. 2021 Aug 5;27(44):11344-11351. doi: 10.1002/chem.202100896. Epub 2021 Jun 29.
The newly prepared fluorescent carboxyamidoquinolines (1-3) and their Zn(II) complexes (Zn@1-Zn@3) were used to bind and sense various phosphate anions utilizing a relay mechanism, in which the Zn(II) ion migrates from the Zn@1-Zn@3 complexes to the phosphate, namely adenosine 5'-triphosphate (ATP) and pyrophosphate (PPi), a process accompanied by a dramatic change in fluorescence. Zn@1-Zn@3 assemblies interact with adenine nucleotide phosphates while displaying an analyte-specific response. This process was investigated using UV-vis, fluorescence, and NMR spectroscopy. It is shown that the different binding selectivity and the corresponding fluorescence response enable differentiation of adenosine 5'-triphosphate (ATP), adenosine 5'-diphosphate (ADP), pyrophosphate (PPi), and phosphate (Pi). The cross-reactive nature of the carboxyamidoquinolines-Zn(II) sensors in conjunction with linear discriminant analysis (LDA) was utilized in a simple fluorescence chemosensor array that allows for the identification of ATP, ADP, PPi, and Pi from 8 other anions including adenosine 5'-monophosphate (AMP) with 100 % correct classification. Furthermore, the support vector machine algorithm, a machine learning method, allowed for highly accurate quantitation of ATP in the range of 5-100 μM concentration in unknown samples with error <2.5 %.
新制备的荧光羧甲酰胺喹啉(1-3)及其 Zn(II)配合物(Zn@1-Zn@3)被用于利用接力机制结合和感应各种磷酸阴离子,其中 Zn(II)离子从 Zn@1-Zn@3 配合物迁移到磷酸,即腺嘌呤核苷 5'-三磷酸(ATP)和焦磷酸(PPi),这一过程伴随着荧光的剧烈变化。Zn@1-Zn@3 组装物与腺嘌呤核苷酸磷酸相互作用,同时表现出分析物特异性响应。该过程使用 UV-vis、荧光和 NMR 光谱进行了研究。结果表明,不同的结合选择性和相应的荧光响应能够区分腺嘌呤核苷 5'-三磷酸(ATP)、腺嘌呤核苷 5'-二磷酸(ADP)、焦磷酸(PPi)和磷酸(Pi)。羧甲酰胺喹啉-Zn(II)传感器的交叉反应性质结合线性判别分析(LDA)被用于简单的荧光化学传感器阵列,该阵列允许从 8 种其他阴离子中识别 ATP、ADP、PPi 和 Pi,包括腺苷 5'-单磷酸(AMP),具有 100%的正确分类。此外,支持向量机算法,一种机器学习方法,允许在未知样品中以 5-100 μM 的浓度范围内对 ATP 进行高度准确的定量,误差<2.5%。