MD Average of Vibrational Spectra of Nucleotides in a SERS Sensor Simulation with Varying Number of Au Nanoparticles

Engineering Proceedings

article

Tatiana Zolotoukhina¹  Kota Maruyama¹ 

[1] Department of Mechanical Engineering, University of Toyama

关键词: vibrational spectra;    molecular dynamics;    nucleotides;    Au nanoparticle;    SERS;   

DOI  :  10.3390/IECB2023-14568

来源: mdpi

【 摘 要 】

Applying the surface-enhanced Raman scattering (SERS) method to detect bioactive molecules such as DNA, proteins, and drugs has significant potential for structure-sensitive nondestructive chemical analysis. The SERS discrimination of single-molecule oligomers in DNA, microRNA, and proteins has attracted wide attention due to the possibility of developing new sensing technology. The collected signal’s sensitivity has the level of detection of single oligomers, which can be compared with the simulation results corresponding to the sensor structure. We investigate the averaging method of the individual bond spectra for DNA nucleotides in the ring part of the pyrimidine (6-ring) and purine (6–5-ring) bases to form vibrational spectra obtained by molecular dynamics (MD) simulation. The system consists of the Au nanoparticles (from 1 to 4 NP assay) attached to a graphene sheet at the edge of the nanopore that localizes in the nanopore nucleotide interaction and spectral enhancement. The nucleotide translocation velocity set at 0.025 m/s compares with the experimental range. The vibrational spectra ring average has been tested for adenine and guanine with close correspondence (in the 500–1700 cm−1 range) to the experimental Raman and SERS spectra and extended to cytosine and thymine nucleotides. We also modified the number of the Au nanoparticles from one NP to four identical NPs to evaluate the influence of the interaction on the MD transient spectra. The variations of mode frequencies and amplitudes due to the number of Au NPs in bond spectra, as well as ring averages, mark the corresponding Au–nucleotide interactions and are considered for use as training sets for machine learning methods.

【 授权许可】

Unknown