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基于金等离子体纳米孔的高通量单分子表面增强拉曼光谱技术对肽中单氨基酸取代的分析。

High-Throughput Single-Molecule Surface-Enhanced Raman Spectroscopic Profiling of Single-Amino Acid Substitutions in Peptides by a Gold Plasmonic Nanopore.

机构信息

State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.

State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China.

出版信息

ACS Nano. 2024 Jul 23;18(29):19200-19207. doi: 10.1021/acsnano.4c04775. Epub 2024 Jul 12.

DOI:10.1021/acsnano.4c04775
PMID:38996344
Abstract

Simultaneous detection and structural characterization of protein variants on a single platform are highly desirable but technically challenging. Herein, we present a single-molecule spectral system based on a gold plasmonic nanopore for analyzing two peptides and their single-point mutated variants. The gold plasmonic nanopore enabled the high-throughput acquisition of surface-enhanced Raman scattering (SERS) spectra at the single-molecule level by electrically driving analytes into hot spots. Furthermore, a statistical method based on Boolean operations was developed to extract prominent features from fluctuated single-molecule SERS spectra. The effects of the single-amino acid substitutions on both the intramolecular interactions and the peptide conformations were directly characterized by the nanopore system, and the results agreed with the predictions by AlphaFold2. This study highlights the mutual benefits of spectroscopy and nanopore technology, whereby the gold plasmonic nanopore offers a powerful tool for the structural analysis of single-molecule proteins.

摘要

同时在单个平台上检测和结构表征蛋白质变体是非常理想的,但在技术上具有挑战性。在此,我们提出了一种基于金等离子体纳米孔的单分子光谱系统,用于分析两种肽及其单点突变变体。金等离子体纳米孔通过电驱动分析物进入热点,实现了在单分子水平上高通量获取表面增强拉曼散射(SERS)光谱。此外,开发了一种基于布尔运算的统计方法,从波动的单分子 SERS 光谱中提取突出特征。纳米孔系统直接表征了单个氨基酸取代对分子内相互作用和肽构象的影响,结果与 AlphaFold2 的预测一致。本研究强调了光谱学和纳米孔技术的相互益处,其中金等离子体纳米孔为单分子蛋白质的结构分析提供了强大的工具。

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Single-Molecule SERS Discrimination of Proline from Hydroxyproline Assisted by a Deep Learning Model.基于深度学习模型辅助的脯氨酸与羟脯氨酸的单分子表面增强拉曼光谱鉴别
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