Photochemical synthesis of pink silver and its use for monitoring peptide nitration via surface enhanced Raman spectroscopy (SERS)

0558956 - ÚOCHB 2023 RIV AT eng J - Článek v odborném periodiku
Sokolová, M. - Šestáková, Hana - Truksa, M. - Šafařík, Martin - Hadravová, Romana - Bouř, Petr - Šebestík, Jaroslav
Photochemical synthesis of pink silver and its use for monitoring peptide nitration via surface enhanced Raman spectroscopy (SERS).
Amino Acids. Roč. 54, č. 9 (2022), s. 1261-1274. ISSN 0939-4451. E-ISSN 1438-2199
Grant CEP: GA MŠMT(CZ) EF16_019/0000729; GA ČR(CZ) GA22-04669S
Výzkumná infrastruktura: e-INFRA CZ - 90140
Institucionální podpora: RVO:61388963
Klíčová slova: nitration * oxidative stress * surface enhanced Raman spectroscopy (SERS) * photochemical synthesis * silver colloids * posttranslational protein modification (PTM)
Obor OECD: Biophysics
Impakt faktor: 3.5, rok: 2022
Způsob publikování: Omezený přístup
https://doi.org/10.1007/s00726-022-03178-w

Oxidative stress may cause extended tyrosine posttranslational modifications of peptides and proteins. The 3-nitro-L-tyrosine (Nit), which is typically formed, affects protein behavior during neurodegenerative processes, such as Alzheimer’s and Parkinson’s diseases. Such metabolic products may be conveniently detected at very low concentrations by surface enhanced Raman spectroscopy (SERS). Previously, we have explored the SERS detection of the Nit NO2 bending vibrational bands in a presence of hydrogen chloride (Niederhafner et al., Amino Acids 53:517–532, 2021, ibid). In this article, we describe performance of a new SERS substrate, “pink silver”, synthesized photochemically. It provides SERS even without the HCl induction, and the acid further decreases the detection limit about 9 times. Strong SERS bands were observed in the asymmetric (1550–1475 cm−1) and symmetric (1360–1290 cm−1) NO stretching in the NO2 group. The bending vibration was relatively weak, but appeared stronger when HCl was added. The band assignments were supported by density functional theory modeling.
Trvalý link: https://hdl.handle.net/11104/0332434