Institute of Organic Chemistry and Biochemistry, Academy of Sciences, Flemingovo náměstí 2, 16610, Prague 6, Czech Republic.
Department of Chemistry of Natural Compounds, Faculty of Food and Biochemical Technology, University of Chemistry and Technology, Prague, Technická 5, 166 28, Prague 6, Czech Republic.
Amino Acids. 2021 Apr;53(4):517-532. doi: 10.1007/s00726-020-02911-7. Epub 2020 Nov 18.
Oxidative stress can lead to various derivatives of the tyrosine residue in peptides and proteins. A typical product is 3-nitro-L-tyrosine residue (Nit), which can affect protein behavior during neurodegenerative processes, such as those associated with Alzheimer's and Parkinson's diseases. Surface enhanced Raman spectroscopy (SERS) is a technique with potential for detecting peptides and their metabolic products at very low concentrations. To explore the applicability to Nit, we use SERS to monitor tyrosine nitration in Met-Enkephalin, rev-Prion protein, and α-synuclein models. Useful nitration indicators were the intensity ratio of two tyrosine marker bands at 825 and 870 cm and a bending vibration of the nitro group. During the SERS measurement, a conversion of nitrotyrosine to azobenzene containing peptides was observed. The interpretation of the spectra has been based on density functional theory (DFT) simulations. The CAM-B3LYP and ωB97XD functionals were found to be most suitable for modeling the measured data. The secondary structure of the α-synuclein models was monitored by electronic and vibrational circular dichroism (ECD and VCD) spectroscopies and modeled by molecular dynamics (MD) simulations. The results suggest that the nitration in these peptides has a limited effect on the secondary structure, but may trigger their aggregation.
氧化应激会导致肽和蛋白质中酪氨酸残基的各种衍生物。一个典型的产物是 3-硝基-L-酪氨酸残基（Nit），它可以影响神经退行性过程中蛋白质的行为，如与阿尔茨海默病和帕金森病相关的过程。表面增强拉曼光谱（SERS）是一种具有在非常低浓度下检测肽及其代谢产物的潜力的技术。为了探索其在 Nit 检测方面的适用性，我们使用 SERS 监测 Met-Enkephalin、rev-Prion 蛋白和 α-突触核蛋白模型中的酪氨酸硝化。有用的硝化指标是 825 和 870 cm 处两个酪氨酸标记带的强度比以及硝基基团的弯曲振动。在 SERS 测量过程中，观察到硝基酪氨酸向含有偶氮苯的肽的转化。对光谱的解释是基于密度泛函理论（DFT）模拟。发现 CAM-B3LYP 和 ωB97XD 函数最适合于模拟实测数据。通过电子和振动圆二色性（ECD 和 VCD）光谱监测 α-突触核蛋白模型的二级结构，并通过分子动力学（MD）模拟进行建模。结果表明，这些肽中的硝化对二级结构的影响有限，但可能引发它们的聚集。
