An overview of COVID-19 with an emphasis on computational approach for its preventive intervention

SARS-CoV-2, a novel Betacoronavirus, has attracted global attention because of its extremely high infection rate and large number of human deaths. It possesses a positive-sense, single-stranded RNA of ~ 30 kb nucleotides as its genetic material. It is responsible for COVID-19 which has been declared a pandemic by WHO. Having reported for the first time in Wuhan, China, the virus infected over 21.48 million people and caused over 0.77 million deaths till mid-august 2020. SARS-CoV-2 contains the spike protein site that gets activated by an enzyme furin which is found in the lung, liver, and small intestine of humans. It shows the potentiality of virus for attacking multiple organs and their failures. Due to the absence of vaccines, the cure is restricted to supportive care including repurposed drugs. In silico approaches may offer an alternative screening to optimize hits to lead stages. It can provide important related to drug resistance, their lineages and evolution. This approach may also help to find an effective vaccine against COVID-19. This review focuses on the in silico aspects of genomics, proteomics, pathogenesis, phylogenetic analysis and viral receptor binding analysis in Betacoronavirus.