In silico characterization of mutations circulating in SARS-CoV-2 structural proteins

Abstract

SARS-CoV-2 has recently emerged as a pandemic that has caused more than 2.4 million deaths worldwide. Since the onset of infections, several full-length sequences of viral genome have been made available which have been used to gain insights into viral dynamics. We utilised a meta-data driven comparative analysis tool for sequences (Meta-CATS) algorithm to identify mutations in 829 SARS-CoV-2 genomes from around the world. The algorithm predicted sixty-one mutations among SARS-CoV-2 genomes. We observed that most of the mutations were concentrated around three protein coding genes viz nsp3 (non-structural protein 3), RdRp (RNA-directed RNA polymerase) and Nucleocapsid (N) proteins of SARS-CoV-2. We used various computational tools including normal mode analysis (NMA), C-α discrete molecular dynamics (DMD) and all-atom molecular dynamic simulations (MD) to study the effect of mutations on functionality, stability and flexibility of SARS-CoV-2 structural proteins including envelope (E), N and spike (S) proteins. PredictSNP predictor suggested that four mutations (L37H in E, R203K and P344S in N and D614G in S) out of seven were predicted to be neutral whilst the remaining ones (P13L, S197L and G204R in N) were predicted to be deleterious in nature thereby impacting protein functionality. NMA, C-α DMD and all-atom MD suggested some mutations to have stabilizing roles (P13L, S197L and R203K in N protein) where remaining ones were predicted to destabilize mutant protein. In summary, we identified significant mutations in SARS-CoV-2 genomes as well as used computational approaches to further characterize the possible effect of highly significant mutations on SARS-CoV-2 structural proteins.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• COVID-19
• protein stability
• mutations;
• nucleocapsid protein
• spike protein
• envelope protein
• discrete molecular dynamics
• molecular simulations
• viral pathogenicity

Acknowledgements

NP is thankful to UGC for PhD fellowship and PS is thankful to CSIR for PhD fellowship. VS received research grant from UGC, Govt. of India. SBR is thankful to his Chemistry Department for providing computational and infrastructure facilities.

Author contributions

NP, SBR, RP, PS, LN performed the experiments and analysed the data. RPB, PA, KRS and VS designed and supervised the study. VS and SBR wrote the first draft of the manuscript. VS, RPB, SBR, PA and KRS reviewed the data and finalized the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by grants from Department of Science and Technology (DST), India and University Grants Commission (UGC), India.