Mapping Drug-gene Interactions to Identify Potential Drug Candidates Targeting Envelope Protein in SARS-CoV-2 Infection

Abstract

Background: COVID-19 is still widespread due to the rapidly mutating disposition of the virus, rendering vaccines and previously elicited antibodies ineffective in many cases. The integral membrane Envelope (E) protein which is 75 amino acid residues long, has also acquired several mutations. Objective: In this work, we have adopted a high-throughput approach incorporating patient gene expression patterns to identify drug repurposing candidates for COVID-19. We have come up with a list of FDA-approved drugs that can not only prevent E protein oligomerization in both its wild type and a mutational state but can also regulate gene targets responsible for inducing COVID symptoms. Methods: We performed an exhaustive analysis of the available gene expression profiles corresponding to a spectrum of COVID patient samples, followed by drug-gene interaction mapping. This revealed a set of drugs that underwent further efficacy tests through in silico molecular docking with the wild-type E-protein. We also built the molecular models of mutant E-protein by considering the important non-synonymous mutations affecting E-protein structure to check the activities of the screened set of drugs against the mutated E-protein. Finally, blind molecular docking simulations were performed to obtain unbiased docking results. Results: Interestingly, this work revealed a set of 8 drugs that have the potential to be effective for a wider spectrum of asymptomatic to severely symptomatic COVID patients. Conclusion: The varied stages of infection and rapid rate of mutation motivated us to search for a set of drugs that can be effective for a wider spectrum of asymptomatic to severely symptomatic COVID patients. Further, the efficiency of these drugs against mutated E-protein increases another level of confidence to fight against this rapidly changing deadly RNA virus and subsequently needs to be validated in clinical settings.