Identification of Isomerically Diverse Ginsenosides Using Engineered Aerolysin Nanopore via Non‐Translocation Blockade Sensing

J Jing Wang (Hunan Cancer Hospital Changsha China) M Minmin Li (State Key Laboratory of Phytochemistry and Natural Medicines) C Chen Zhang (Shenzhen Institute for Quantum Science and Engineering, Department of Chemistry, and Department of Physics) X Xinjia Zhao (State Key Laboratory of Phytochemistry and Natural Medicines) Y Yuting Xiong Y Yuchen Cao (State Key Laboratory of Phytochemistry and Natural Medicines) D Dongdong Wang X Xiaonong Li (Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine) X Xinmiao Liang (State Key Laboratory of Phytochemistry and Natural Medicines) G Guangyan Qing (State Key Laboratory of Phytochemistry and Natural Medicines)

Abstract

Abstract Typical nanopore sensing depends on slowed translocation through the pore to acquire effective blockade signals. However, this paradigm often suffers from low signal precision and poor resolution, making it challenging to resolve pools of analytes with diverse, similar structures. Here, we present a non‐translocation blockade sensing based on an engineered aerolysin S278K that enables the identification of isomerically diverse ginsenosides—a class of glycoconjugates whose structural characterization has been a persistent challenge in glycoscience. By introducing the S278K mutation, the aerolysin acquires a highly positively charged interior and generates intense electro‐osmotic flow and enhanced steric/enthalpic barriers, effectively trapping ginsenoside molecules in the K278‐R220 region and preventing their further translocation. This distinct blockade sensing mode significantly improves the detection capability of aerolysin by prolonging residence time (e.g., 43‐fold longer), enabling detailed molecular characterization. As a result, we demonstrate the unambiguous identification of 30 ginsenosides differing in glycosyl composition, isomerism, modification, and aglycone, as well as the quantitative analysis of complex ginsenosides in real samples by integrating deep learning. This work underscores the promise of non‐translocation nanopore sensing for deciphering structurally complex and diverse small molecule analytes.

Article Details

Volume / Issue Vol. 64, Issue 32
Published August 04, 2025
ISSN 1433-7851
Publisher Wiley

Journal Info

Angewandte Chemie International Edition

Wiley

ISSN: 1433-7851 Physical Sciences

Authors (10)

J

Jing Wang

Hunan Cancer Hospital Changsha China

M

Minmin Li

State Key Laboratory of Phytochemistry and Natural Medicines

C

Chen Zhang

Shenzhen Institute for Quantum Science and Engineering, Department of Chemistry, and Department of Physics

X

Xinjia Zhao

State Key Laboratory of Phytochemistry and Natural Medicines

Y

Yuting Xiong

Y

Yuchen Cao

State Key Laboratory of Phytochemistry and Natural Medicines

D

Dongdong Wang

X

Xiaonong Li

Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine

X

Xinmiao Liang

State Key Laboratory of Phytochemistry and Natural Medicines

G

Guangyan Qing

State Key Laboratory of Phytochemistry and Natural Medicines