Issue 43, 2023

Discrimination of cis-diol-containing molecules using fluorescent boronate affinity probes by principal component analysis

Abstract

Fluorescent boronate affinity molecules have gained increasing attention in the field of fluorescence sensing and detection due to their selective recognition capability towards cis-diol-containing molecules (cis-diols). However, the conventional fluorescent boronate affinity molecules face a challenge in differentiating the type of cis-diol only by their fluorescence responses. In this study, a simple method was used to discriminate different types of cis-diols, including nucleosides, nucleotides, sugars, and glycoproteins based on the phenylboronic acid-functionalized fluorescent molecules combined with principal component analysis (PCA). Both fluorescent molecules were simply synthesized by the covalent interaction between the amino group in 3-aminophenyl boronic acid and the isothiocyanate group in fluorescein or rhodamine B. In view of their fluorescence-responsive behaviors to these cis-diols directly, it is impossible to differentiate their types even under the optimized experimental conditions. When PCA was employed to treat the fluorescence response data and the quenching constants with their molecular weight, different types of cis-diols can be distinguished successfully. As a result, by integrating the fluorescence response of the boronate affinity probes with PCA, it can greatly improve the specific recognition capability of the boronic acids, providing a simple and direct way to distinguish and identify different types of cis-diols.

Graphical abstract: Discrimination of cis-diol-containing molecules using fluorescent boronate affinity probes by principal component analysis

Supplementary files

Article information

Article type
Paper
Submitted
13 ربيع الأول 1445
Accepted
28 ربيع الأول 1445
First published
01 ربيع الثاني 1445

Anal. Methods, 2023,15, 5803-5812

Discrimination of cis-diol-containing molecules using fluorescent boronate affinity probes by principal component analysis

F. Wang, S. Xiong, T. Wang, Y. Hou and Q. Li, Anal. Methods, 2023, 15, 5803 DOI: 10.1039/D3AY01719B

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