Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome

Abstract

Background: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple studies had been done to identify and study nonsynonymous mutations, which change amino acid residues of SARS-CoV-2 proteins. On the other hand, there is little study on the effects of SARS-CoV-2 synonymous mutations. Although these mutations do not alter amino acids, some studies suggest that they may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. Methods: A total of 30,229 SARS-CoV-2 genomic sequences were retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database and aligned using MAFFT. Then, the mutations and their respective frequency were identified. A prediction of RNA secondary structures and their base pair probabilities was performed to study the effect of synonymous mutations on RNA structure and stability. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. Results: A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a, followed by C313U and C9286U mutation in nsp1 and nsp4 of ORF1a, respectively. Conclusion: Among the synonymous mutations identified, C913U mutation in ORF1a and C26735U in membrane (M) protein may affect RNA secondary structure, reducing the stability of RNA folding and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.