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Identification of Oligosaccharide Isomers Using Electrostatically Asymmetric OmpF Nanopore.

  • Published In: Angewandte Chemie, 2025, v. 137, n. 9. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gao, Fan; Wang, Jia‐Hong; Ma, Hui; Xia, Bingqing; Wen, Liuqing; Long, Yi‐Tao; Ying, Yi‐Lun 3 of 3

Abstract

Glycans, unlike uniformly charged DNA and compositionally diverse peptides, are typically uncharged and possess rich stereoisomeric diversity in the glycosidic bonds between two monosaccharide units. These unique features, including charge heterogeneity and structural complexity, pose significant challenges for accurate analysis. Herein, we developed a novel single‐molecule oligosaccharide sensor, OmpF nanopore. The natural electroosmotic flow within OmpF generates a robust driving force for unlabeled neutral oligosaccharides, enabling detection at a concentration as low as 6.4 μM. Furthermore, the asymmetric constriction zone of OmpF was employed to construct a stereoselective recognition site, enabling sensitive identification of glycosidic bond differences in cell lysate samples. With the assistance of machine learning algorithms, the OmpF nanopore achieved a recognition accuracy of 99.9 % for tetrasaccharides differing in only one glycosidic bond was achieved. This nanopore sensor provides a highly sensitive analytical tool with a broad dynamic range. It enables chiral recognition of oligosaccharides at low concentrations and is suitable for analysing both low‐abundance and practical samples. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Angewandte Chemie. 2025/02, Vol. 137, Issue 9, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2025
  • ISSN:0044-8249
  • DOI:10.1002/ange.202422118
  • Accession Number:184017894
  • Copyright Statement:Copyright of Angewandte Chemie is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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