Identification of nine mammal monosaccharides by solid-state nanopores
Abstract Glycans, nucleic acids and proteins are three major classes of natural biopolymers. The extremely high diversity of isomerization makes structural elucidation of glycans the most challenging job among three classes. In the past few years, the single molecule sensing technique based on nanop...
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| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83690-z |
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| author | Yunze Sun Zhuang Mi Xiaoyu Chen Jian-Jun Li Jun Lu Xinyan Shan Xinghua Lu Yuguang Du |
| author_facet | Yunze Sun Zhuang Mi Xiaoyu Chen Jian-Jun Li Jun Lu Xinyan Shan Xinghua Lu Yuguang Du |
| author_sort | Yunze Sun |
| collection | DOAJ |
| description | Abstract Glycans, nucleic acids and proteins are three major classes of natural biopolymers. The extremely high diversity of isomerization makes structural elucidation of glycans the most challenging job among three classes. In the past few years, the single molecule sensing technique based on nanopores has achieved great success in sequencing of DNA. Inspired by this, it is potential to sequence glycans in the similar manner. Herein, SiNx nanopores were used to identify nine common monosaccharides in mammals. Each monosaccharide showed characteristic blockage current, which roughly increased with the increase of its molecular weight. In order to distinguish nine monosaccharides, several machine learning models were trained and tested, of which the highest F1 value was 1. These results illustrated that nine common monosaccharides in mammals could be clearly identified and discriminate by our method combining solid-state nanopores and machine learning. As far as we know, this is the first report that monosaccharides can be sensed and distinguished by solid-state nanopores. Our work showed the great potential of solid-state nanopores in glycan sequencing, and would lay the foundation for solid-state nanopore-based glycan sequencing. |
| format | Article |
| id | doaj-art-44a67f824af04f0fa6aec490978b179b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-44a67f824af04f0fa6aec490978b179b2025-01-05T12:27:17ZengNature PortfolioScientific Reports2045-23222024-12-011411910.1038/s41598-024-83690-zIdentification of nine mammal monosaccharides by solid-state nanoporesYunze Sun0Zhuang Mi1Xiaoyu Chen2Jian-Jun Li3Jun Lu4Xinyan Shan5Xinghua Lu6Yuguang Du7State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of SciencesUniversity of Chinese Academy of SciencesUniversity of Chinese Academy of SciencesState Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of SciencesInstitute of Physics, Chinese Academy of SciencesUniversity of Chinese Academy of SciencesUniversity of Chinese Academy of SciencesState Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of SciencesAbstract Glycans, nucleic acids and proteins are three major classes of natural biopolymers. The extremely high diversity of isomerization makes structural elucidation of glycans the most challenging job among three classes. In the past few years, the single molecule sensing technique based on nanopores has achieved great success in sequencing of DNA. Inspired by this, it is potential to sequence glycans in the similar manner. Herein, SiNx nanopores were used to identify nine common monosaccharides in mammals. Each monosaccharide showed characteristic blockage current, which roughly increased with the increase of its molecular weight. In order to distinguish nine monosaccharides, several machine learning models were trained and tested, of which the highest F1 value was 1. These results illustrated that nine common monosaccharides in mammals could be clearly identified and discriminate by our method combining solid-state nanopores and machine learning. As far as we know, this is the first report that monosaccharides can be sensed and distinguished by solid-state nanopores. Our work showed the great potential of solid-state nanopores in glycan sequencing, and would lay the foundation for solid-state nanopore-based glycan sequencing.https://doi.org/10.1038/s41598-024-83690-zSolid-state nanoporeMonosaccharideHuman milk oligosaccharideMachine learningSilicon nitride |
| spellingShingle | Yunze Sun Zhuang Mi Xiaoyu Chen Jian-Jun Li Jun Lu Xinyan Shan Xinghua Lu Yuguang Du Identification of nine mammal monosaccharides by solid-state nanopores Scientific Reports Solid-state nanopore Monosaccharide Human milk oligosaccharide Machine learning Silicon nitride |
| title | Identification of nine mammal monosaccharides by solid-state nanopores |
| title_full | Identification of nine mammal monosaccharides by solid-state nanopores |
| title_fullStr | Identification of nine mammal monosaccharides by solid-state nanopores |
| title_full_unstemmed | Identification of nine mammal monosaccharides by solid-state nanopores |
| title_short | Identification of nine mammal monosaccharides by solid-state nanopores |
| title_sort | identification of nine mammal monosaccharides by solid state nanopores |
| topic | Solid-state nanopore Monosaccharide Human milk oligosaccharide Machine learning Silicon nitride |
| url | https://doi.org/10.1038/s41598-024-83690-z |
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