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

Abstract

Non-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. S protein in the two VLPs, which are SARS-CoV-2 VLPs and HIV-based VLPs was characterized and detected with surface-enhanced Raman spectroscopy (SERS) which is a rapid, low-cost, and easy-to-use technique. Machine learning techniques combined with SERS can be used to detect emerging mutations of pandemics, which could facilitate not only detection but also discovery and characterization of novel vaccines and other VLP-based therapeutics.