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多元组件蛋白质系统的数字解码通过纳米腔封闭单分子拉曼指纹

Wenjing Tang¹, Zhuodong Tang¹, Jinxiang Li¹

¹State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, School of Environment, Nanjing University, Nanjing, 210023, P.R. China.

Angewandte Chemie (International ed. in English)

|December 19, 2025

概括

这项研究引入了一种新的单分子拉曼指纹方法,用于精确识别复杂混合物中的蛋白质. 该技术利用先进的等离子体纳米腔和机器学习来实现蛋白质组成的自动数字解码,从而推进分子诊断.

科学领域:

生物物理学的生物物理.
分析化学分析化学
频谱学是一种光谱学.

背景情况:

在复杂的生物系统中识别单个蛋白质对于分子诊断和蛋白质组学至关重要.
目前的方法在单分子分析的特异性和灵敏性方面面临挑战.

研究的目的:

开发一个单分子拉曼指纹策略,用于复杂系统中蛋白质组成的自动数字解码.
在环境条件下建立一个高通量平台来进行多重蛋白质分析.

主要方法:

采用了双放大,接口合的等离子纳米洞架构,整合了间隙模式合和表面等离子共振.
在高度增强的热点内实现单一蛋白质的封闭和隔离,以获得内在的拉曼光谱.
采用一个定制的机器学习算法,在单分子拉曼数据集上进行训练,用于蛋白质识别.

主要成果:

成功执行了七种代表性蛋白质的高通量超谱拉曼指纹.
证明了单个蛋白质物种的自动识别和空间映射.
生成了蛋白质组成的定量和可定位的解码地图.

结论:

开发的战略使得智能,数据驱动,多重蛋白质分析成为可能.
这种方法对下一代分子诊断,生物传感和蛋白质功能研究有重大影响.

关键词:

机器学习是机器学习.纳米空洞是指纳米的空洞.拉曼的指纹是指纹.单分子光谱学单分子光谱学通过表面增强的拉曼散射.

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