Characterization and discrimination of spike protein in SARS‐CoV‐2 virus‐like particles via surface‐enhanced Raman spectroscopy

Author:

Akdeniz Munevver12,Al‐Shaebi Zakarya12,Altunbek Mine3,Bayraktar Canan4,Kayabolen Alisan45,Bagci‐Onder Tugba4,Aydin Omer1267ORCID

Affiliation:

1. Department of Biomedical Engineering Erciyes University Kayseri Turkey

2. Nanothera Lab Drug Application and Research Center (ERFARMA) Erciyes University Kayseri Turkey

3. Department of Chemical Engineering University of Massachusetts Lowell Massachusetts USA

4. Koç University Research Center for Translational Medicine (KUTTAM) Koç University Istanbul Turkey

5. McGovern Institute for Brain Research at MIT Massachusetts Institute of Technology Cambridge Massachusetts USA

6. Clinical Engineering Research and Implementation Center (ERKAM) Erciyes University Kayseri Turkey

7. Nanotechnology Research and Application Center (ERNAM) Erciyes University Kayseri Turkey

Abstract

AbstractNon‐infectious virus‐like particles (VLPs) are excellent structures for development of many biomedical applications such as drug delivery systems, vaccine production platforms, and detection techniques for infectious diseases including SARS‐CoV‐2 VLPs. The characterization of biochemical and biophysical properties of purified VLPs is crucial for development of detection methods and therapeutics. The presence of spike (S) protein in their structure is especially important since S protein induces immunological response. In this study, development of a rapid, low‐cost, and easy‐to‐use technique for both characterization and detection of S protein in the two VLPs, which are SARS‐CoV‐2 VLPs and HIV‐based VLPs was achieved using surface‐enhanced Raman spectroscopy (SERS). To analyze and classify datasets of SERS spectra obtained from the VLP groups, machine learning classification techniques including support vector machine (SVM), k‐nearest neighbors (kNN), and random forest (RF) were utilized. Among them, the SVM classification algorithm demonstrated the best classification performance for SARS‐CoV‐2 VLPs and HIV‐based VLPs groups with 87.5% and 92.5% accuracy, respectively. This study could be valuable for the rapid characterization of VLPs for the development of novel therapeutics or detection of structural proteins of viruses leading to a variety of infectious diseases.

Funder

Bilimsel Araştırma Projeleri, Erciyes Üniversitesi

Publisher

Wiley

Subject

Molecular Medicine,Applied Microbiology and Biotechnology,General Medicine

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