Abstract
COVID-19 is a sneaking deadly disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid increase in the number of infected patients worldwide enhances the exigency for medicines. However, precise therapeutic drugs are not available for COVID-19; thus, exhaustive research is critically required to unscramble the pathogenic tools and probable therapeutic targets for the development of effective therapy. This study utilizes a chemogenomics strategy, including computational tools for the identification of viral-associated differentially expressed genes (DEGs), and molecular docking of potential chemical compounds available in antiviral, anticancer, and natural product-based libraries against these DEGs. We scrutinized the messenger RNA expression profile of SARS-CoV-2 patients, publicly available on the National Center for Biotechnology Information-Gene Expression Omnibus database, stratified them into different groups based on the severity of infection, superseded by identification of overlapping mild and severe infectious (MSI)-DEGs. The profoundly expressed MSI-DEGs were then subjected to trait-linked weighted co-expression network construction and hub module detection. The hub module MSI-DEGs were then exposed to enrichment (gene ontology + pathway) and protein-protein interaction network analyses where Rho guanine nucleotide exchange factor 1 (ARHGEF1) gene conjectured in all groups and could be a probable target of therapy. Finally, we used the molecular docking and molecular dynamics method to identify inherent hits against the ARHGEF1 gene from antiviral, anticancer, and natural product-based libraries. Although the study has an identified significant association of the ARHGEF1 gene in COVID19; and probable compounds targeting it, using in silico methods, these targets need to be validated by both in vitro and in vivo methods to effectively determine their therapeutic efficacy against the devastating virus.