Issue 13, 2024

A SARS-CoV-2 Mpro fluorescent sensor for exploring pharmacodynamic substances from traditional Chinese medicine

Abstract

The main protease of SARS-CoV-2 (SARS-CoV-2 Mpro) plays a critical role in the replication and life cycle of the virus. Currently, how to screen SARS-CoV-2 Mpro inhibitors from complex traditional Chinese medicine (TCM) is the bottleneck for exploring the pharmacodynamic substances of TCM against SARS-CoV-2. In this study, a simple, cost-effective, rapid, and selective fluorescent sensor (TPE-S-TLG sensor) was designed with an AIE (aggregation-induced emission) probe (TPE-Ph-In) and the SARS-CoV-2 Mpro substrate (S-TLG). The TPE-S-TLG sensor was characterized using UV-Vis absorption spectroscopy, fluorescence spectroscopy, dynamic light scattering (DLS), transmission electron microscopy (TEM), zeta potential, and Fourier transform infrared (FTIR) spectroscopy techniques. The limit of detection of this method to detect SARS-CoV-2 Mpro was measured to be 5 ng mL−1. Furthermore, the TPE-S-TLG sensor was also successfully applied to screen Mpro inhibitors from Xuebijing injection using the separation and collection of the HPLC-fully automatic partial fraction collector (HPLC-FC). Six active compounds, including protocatechualdehyde, chlorogenic acid, hydroxysafflower yellow A, caffeic acid, isoquercetin, and pentagalloylglucose, were identified using UHPLC-Q-TOF/MS that could achieve 90% of the Mpro inhibition rate for the Xuebijing injection. Accordingly, the strategy can be broadly applied in the detection of disease-related proteases as well as screening active substances from TCM.

Graphical abstract: A SARS-CoV-2 Mpro fluorescent sensor for exploring pharmacodynamic substances from traditional Chinese medicine

Supplementary files

Article information

Article type
Paper
Submitted
08 Mar 2024
Accepted
06 May 2024
First published
20 May 2024

Analyst, 2024,149, 3585-3595

A SARS-CoV-2 Mpro fluorescent sensor for exploring pharmacodynamic substances from traditional Chinese medicine

L. Han, B. Wang, K. Sun, M. Sitara, M. Li, P. Wang, N. Chen, X. Yu and J. Tian, Analyst, 2024, 149, 3585 DOI: 10.1039/D4AN00372A

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