Narrowing Signal Distribution by Adamantane Derivatization for Amino Acid Identification Using an α-Hemolysin Nanopore
Narrowing Signal Distribution by Adamantane Derivatization for Amino Acid Identification Using an α-Hemolysin Nanopore
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DOI：
10.1021/acs.nanolett.3c03593
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发表时间：
2024-01-24
期刊：
影响因子：
10.8
通讯作者：
Liu，Chang
中科院分区：
文献类型：
--
作者：
Wei，Xiaojun;Ma，Dumei;Liu，Chang
纳米孔传感的快速进展引发了人们对蛋白质测序的兴趣。尽管最近在使用纳米孔进行氨基酸识别方面取得了显着进展，但该过程中通常采用的化学修饰仍需要进一步改进。挑战之一是增强化学特异性以避免下游氨基酸的错误识别。通过使用金刚烷标记蛋白氨基酸，我们开发了一种使用野生型 α-溶血素纳米孔对单个氨基酸进行指纹识别的方法。金刚烷标记氨基酸 (ALAA) 的独特结构提高了空间分辨率，从而产生独特的电流信号。使用机器学习算法探索了各种纳米孔参数，并在区分九种选定氨基酸方面实现了 81.3% 的验证准确度。我们的结果不仅推进了利用纳米孔进行单分子蛋白质表征的工作，而且还为研究单个分子的内在和变异结构提供了一个潜在的平台。
The rapid progress in nanopore sensing has sparked interest in protein sequencing. Despite recent notable advancements in amino acid recognition using nanopores, chemical modifications usually employed in this process still need further refinements. One of the challenges is to enhance the chemical specificity to avoid downstream misidentification of amino acids. By employing adamantane to label proteinogenic amino acids, we developed an approach to fingerprint individual amino acids using the wild-type α-hemolysin nanopore. The unique structure of adamantane-labeled amino acids (ALAAs) improved the spatial resolution, resulting in distinctive current signals. Various nanopore parameters were explored using a machine-learning algorithm and achieved a validation accuracy of 81.3% for distinguishing nine selected amino acids. Our results not only advance the effort in single-molecule protein characterization using nanopores but also offer a potential platform for studying intrinsic and variant structures of individual molecules.