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Discrimination of Ribonucleoside Mono-, Di-, and Triphosphates Using an Engineered Nanopore.

Yuqin WangPingping FanShanyu ZhangLiying WangXinyue LiWendong JiaYao LiuKefan WangXiaoyu DuPan-Ke ZhangShuo Huang
Published in: ACS nano (2022)
Ribonucleotides, which widely exist in all living organisms and are essential to both physiological and pathological processes, can naturally appear as ribonucleoside mono-, di-, and triphosphates. Natural ribonucleotides can also dynamically switch between different phosphorylated forms, posing a great challenge for sensing. A specially engineered nanopore sensor is promising for full discrimination of all canonical ribonucleoside mono-, di-, and triphosphates. However, such a demonstration has never been reported, due to the lack of a suitable nanopore sensor that has a sufficient resolution. In this work, we utilized a phenylboronic acid (PBA) modified Mycobacterium smegmatis porin A (MspA) hetero-octamer for ribonucleotide sensing. Twelve types of ribonucleotides, including mono-, di-, and triphosphates of cytidine (CMP, CDP, CTP), uridine (UMP, UDP, UTP), adenosine (AMP, ADP, ATP), and guanosine (GMP, GDP, GTP) were simultaneously discriminated. A machine-learning algorithm was also developed, which achieved a general accuracy of 99.9% for ribonucleotide sensing. This strategy was also further applied to identify ribonucleotide components in ATP tablets and injections. This sensing strategy provides a direct, accurate, easy, and rapid solution to characterize ribonucleotide components in different phosphorylated forms.
Keyphrases
  • single molecule
  • machine learning
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  • pseudomonas aeruginosa
  • high resolution
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  • deep learning
  • cystic fibrosis
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  • gram negative