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A Fluorescence Sensor Array Based on Zinc(II)‐Carboxyamidoquinolines: Toward Quantitative Detection of ATP **
Author(s) -
Pushina Mariia,
Farshbaf Sepideh,
Mochida Wakana,
Kanakubo Masashi,
Nishiyabu Ryuhei,
Kubo Yuji,
Anzenbacher Pavel
Publication year - 2021
Publication title -
chemistry – a european journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.687
H-Index - 242
eISSN - 1521-3765
pISSN - 0947-6539
DOI - 10.1002/chem.202100896
Subject(s) - pyrophosphate , fluorescence , chemistry , adenosine triphosphate , phosphate , adenosine diphosphate , fluorescence spectroscopy , analyte , nucleotide , adenosine monophosphate , adenosine , zinc , nucleoside , biochemistry , chromatography , enzyme , biology , organic chemistry , physics , platelet , platelet aggregation , quantum mechanics , gene , immunology
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 %.