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.

Background: SARS-CoV-2 virus is a highly transmissible pathogen that causes COVID-19. The outbreak originated in Wuhan, China in December 2019. A number of nonsynonymous mutations located at different SARS-CoV-2 proteins have been reported by multiple studies. However, there are limited computational studies on the biological impacts of these mutations on the structure and function of the proteins.   Methods: In our study nonsynonymous mutations of the SARS-CoV-2 genome and their frequencies were identified from 30,229 sequences. Subsequently, the effects of the top 10 highest frequency nonsynonymous mutations of different SARS-CoV-2 proteins were analyzed using bioinformatics tools including co-mutation analysis, prediction of the protein structure stability and flexibility analysis, and prediction of the protein functions.   Results: A total of 231 nonsynonymous mutations were identified from 30,229 SARS-CoV-2 genome sequences. The top 10 nonsynonymous mutations affecting nine amino acid residues were ORF1a nsp5 P108S, ORF1b nsp12 P323L and A423V, S protein N501Y and D614G, ORF3a Q57H, N protein P151L, R203K and G204R. Many nonsynonymous mutations showed a high concurrence ratio, suggesting these mutations may evolve together and interact functionally. Our result showed that ORF1a nsp5 P108S, ORF3a Q57H and N protein P151L mutations may be deleterious to the function of SARS-CoV-2 proteins. In addition, ORF1a nsp5 P108S and S protein D614G may destabilize the protein structures while S protein D614G may have a more open conformation compared to the wild type.   Conclusion: The biological consequences of these nonsynonymous mutations of SARS-CoV-2 proteins should be further validated by in vivo and in vitro experimental studies in the future.

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 works had been done to study nonsynonymous mutations, which change protein sequences.  However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome.   Methods: A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified.  Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. 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.. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Conclusion:   Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

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Background: SARS-CoV-2 virus is a highly transmissible pathogen that causes COVID-19. The outbreak originated in Wuhan, China in December 2019. A number of nonsynonymous mutations located at different SARS-CoV-2 proteins have been reported by multiple studies. However, there are limited computational studies on the biological impacts of these mutations on the structure and function of the proteins.   Methods: In our study nonsynonymous mutations of the SARS-CoV-2 genome and their frequencies were identified from 30,229 sequences. Subsequently, the effects of the top 10 nonsynonymous mutations of different SARS-CoV-2 proteins were analyzed using bioinformatics tools including co-mutation analysis, prediction of the protein structure stability and flexibility analysis, and prediction of the protein functions.   Results: A total of 231 nonsynonymous mutations were identified from 30,229 SARS-CoV-2 genome sequences. The top 10 nonsynonymous mutations affecting nine amino acid residues were ORF1a nsp5 P108S, ORF1b nsp12 P323L and A423V, S protein N501Y and D614G, ORF3a Q57H, N protein P151L, R203K and G204R. Many nonsynonymous mutations showed a high concurrence ratio, suggesting these mutations may evolve together and interact functionally. Our result showed that ORF1a nsp5 P108S, ORF3a Q57H and N protein P151L mutations may be deleterious to the function of SARS-CoV-2 proteins. In addition, ORF1a nsp5 P108S and S protein D614G may destabilize the protein structures while S protein D614G may have a more open conformation compared to the wild type.   Conclusion: The biological consequences of these nonsynonymous mutations of SARS-CoV-2 proteins should be further validated by in vivo and in vitro experimental studies in the future.