Novel coronavirus has wrecked medical and health care facilities claiming ∼5% death tollsglobally. All efforts to contain the pathogenesis either using inhibitory drugs or vaccines largelyremained futile due to a lack of better understanding of the genomic feature of this virus. In thepresent study, we compared the 2019-nCoV with other coronaviruses, which indicated that batSARS like coronavirus could be a probable ancestor of the novel coronavirus. The proteinsequence similarity of pangolin-hCoV and bat-hCoV with human coronavirus was higher ascompared to their nucleotide similarity denoting the occurrence of more synonymous mutationsin the genome. Phylogenetic and alignment analysis of 591 novel coronaviruses of differentclades from Group I to Group V revealed several mutations and concomitant amino acidchanges. Detailed investigation on nucleotide substitution unfolded 100 substitutions in thecoding region of which 43 were synonymous and 57 were of non-synonymous type. The nonsynonymous substitutions resulting into 57 amino acid changes were found to be distributed overdifferent hCoV proteins with maximum on spike protein. An important di-amino acid change RGto KR was observed in ORF9 protein. Additionally, several interesting features of the novelcoronavirus genome have been highlighted in respect to various other human infecting viruseswhich may explain extreme pathogenicity, infectivity and simultaneously the reason behindfailure of the antiviral therapies. SUMMARY: This study presents a comprehensive phylogenetic analysis of SARS-CoV2 isolates to understand discrete mutations that are occurring between patient samples. The analysis unravel various amino acid mutations in the viral proteins which may provide an explanation for varying treatment efficacies of different inhibitory drugs and a future direction towards a combinatorial treatment therapies based on the kind of mutation in the viral genome.
Investigating the genomic landscape of novel coronavirus (2019-nCoV) to identify non-synonymous mutations for use in diagnosis and drug design.