In Silico Comparative Analysis of the Functional, Structural, and Evolutionary Properties of SARS-CoV-2 Variant Spike Proteins

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Abstract

Background: A recent global outbreak of COVID-19 caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) created a pandemic and emerged as a potential threat to humanity. The analysis of virus genetic composition has revealed that the spike protein, one of the major structural proteins, facilitates the entry of the virus to host cells. Objective: The spike protein has become the main target for prophylactics and therapeutics studies. Here, we compared the spike proteins of SARS-CoV-2 variants using bioinformatics tools. Methods: The spike protein sequences of wild-type SARS-CoV-2 and its 6 variants—D614G, alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2), gamma (P.1), and omicron (B.1.1.529)—were retrieved from the NCBI database. The ClustalX program was used to sequence multiple alignment and perform mutational analysis. Several online bioinformatics tools were used to predict the physiological, immunological, and structural features of the spike proteins of SARS-CoV-2 variants. A phylogenetic tree was constructed using CLC software. Statistical analysis of the data was done using jamovi 2 software. Results: Multiple sequence analysis revealed that the P681R mutation in the delta variant, which changed an amino acid from histidine (H) to arginine (R), made the protein more alkaline due to arginine’s high pKa value (12.5) compared to histidine’s (6.0). Physicochemical properties revealed the relatively higher isoelectric point (7.34) and aliphatic index (84.65) of the delta variant compared to other variants. Statistical analysis of the isoelectric point, antigenicity, and immunogenicity of all the variants revealed significant correlation, with P values ranging from

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Math, R. K., Mudennavar, N., Javaregowda, P. K., & Savanur, A. (2022). In Silico Comparative Analysis of the Functional, Structural, and Evolutionary Properties of SARS-CoV-2 Variant Spike Proteins. JMIR Bioinformatics and Biotechnology, 3(1). https://doi.org/10.2196/37391

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