Sensitive and Label-Free Detection of Protein Secondary Structure by Amide Ⅲ Spectral Signals using Surface-Enhanced Raman Spectroscopy

Proteins and peptides perform a vital role in living systems, however it remains a challenge for accurate description of proteins at the molecular level. Despite that surface-enhanced Raman spectroscopy (SERS) can provide the intrinsic fingerprint information of samples with ultrahigh sensitivity, it suffers from the poor reproducibility and reliability. Herein, we demonstrate that the silver nanorod array fabricated by an oblique angle deposition method is a powerful substrate for SERS to probe the protein secondary structures without exogenous labels. With this method, the SERS signals of two typical proteins (lysozyme and cytochrome c) are successfully obtained. Additionally, by analyzing the spectral signals of the amide Ⅲ of protein backbone, the influence of concentration on the folding status of proteins has been elucidated. With the concentration increasing, the components of α-helix and β-sheet structures of lysozyme increase while the secondary structures of cytochrome c almost keep constant. The SERS method in this work offers an effective optical marker to characterize the structures of proteins.

表面增强拉曼散射光谱结合化学计量学方法对蜂蜜中双甲脒的高效检测（英文）

本文建立了一种灵敏、快速、环保的表面增强拉曼光谱(SERS)方法,以银纳米棒阵列为基底,测定了蜂蜜中微量的双甲脒.银纳米棒采用斜角沉积法制备,具有良好的SERS活性,增强因子为-10^7.利用密度泛函理论首先对双甲脒的特征峰进行了归属.进一步探测了该方法用于双甲脒检测的可行性,其对蜂蜜中双甲脒的最低检测浓度为0.08mg/kg.另一方面,采用偏最小二乘回归分析方法,对SERS全波谱进行分析并与双甲脒的浓度进行关联,建立了一种多元的双甲脒预测模型.结果表明,蜂蜜样本中双甲脒的预测浓度与实际浓度吻合较好.与传统的基于SERS单峰强度的单变量定量模型相比,本文提出的多元预测模型综合了双甲脒的所有特征峰,提高了检测精度和抗干扰能力.