Identification of Oligosaccharide Isomers Using Electrostatically Asymmetric OmpF Nanopore

Area of Science:

• Carbohydrate Chemistry
• Analytical Chemistry
• Biophysics

Background:

• Oligosaccharides (glycans) present analytical challenges due to their uncharged nature and complex stereoisomeric diversity.
• Accurate analysis of glycans is crucial for understanding biological processes but is hindered by their structural complexity and lack of charge.

Purpose of the Study:

• To develop a novel single-molecule sensor for sensitive and stereoselective detection of oligosaccharides.
• To overcome the limitations of analyzing uncharged and structurally diverse glycans.

Main Methods:

• Development of a single-molecule oligosaccharide sensor utilizing the OmpF nanopore.
• Leveraging the natural electroosmotic flow within the OmpF nanopore for driving unlabeled neutral oligosaccharides.
• Employing the asymmetric constriction zone of OmpF to create a stereoselective recognition site.
• Integration of machine learning algorithms for data analysis and recognition.

Main Results:

• Detection of unlabeled neutral oligosaccharides at concentrations as low as 6.4 μM.
• Sensitive identification of differences in glycosidic bonds within cell lysate samples.
• Achieved 99.9% recognition accuracy for tetrasaccharides differing by a single glycosidic bond using machine learning.

Conclusions:

• The OmpF nanopore sensor offers a highly sensitive analytical tool with a broad dynamic range for oligosaccharide analysis.
• Enables chiral recognition of oligosaccharides at low concentrations.
• Suitable for analyzing low-abundance and complex biological samples, including cell lysates.