ABSTRACT
Objective: The acute cases of pneumonia (COVID-19) were first reported from China in December 2019, and the pathogen was identified as SARS-CoV-2. Currently, many vaccines have been developed against this virus by using multiple genes, applying different platforms, and used for the vaccinations of the human population. Spike protein genes play an important role in host cell attachment and viral entry and have been extensively used for the development of vaccine and antiviral therapeutics. Short interfering RNA is also known as silencing RNA and contribute a significant role to regulate the expression of a specific gene. By using this technology, virus inhibition has been demonstrated against many viral diseases. Methods: In this work, we have reported the Insilico prediction, designing, and experimental validation of siRNAs antiviral potency against SARS-CoV-2-S-RBD. The siDirect 2.0 was selected for siRNAs prediction, and secondary structure was predicted by RNAfold while the HNADOCK was used for molecular docking analysis and specific binding of siRNAs to the selected target. We have used and evaluated four siRNAs for cellular toxicity and determination of antiviral efficiency based on the Ct value of q-real-time PCR in Vero E6 cells. Results: Based on the experimental evaluation and analysis of results from generated data, we observed that there is no cytotoxicity for any tested siRNAs in Vero E6 cells. Total four siRNA were filtered out from twenty-one siRNAs following the strict selection and scoring criteria. The better antiviral efficiency was observed in 3rd siRNAs based on the Ct value of q-real-time PCR. The results that emerged from this study encouraged us to validate the efficiency of these siRNAs in multiple cells by using alone and in a combination of two or more siRNAs to inhibit the SARS-CoV-2 proliferation. Conclusion: The Insilico prediction, molecular docking analysis provided the selection of better siRNAs. Based on the experimental evaluation only 3rd siRNA was found to be more effective than others and showed better antiviral efficiency. These siRNAs should also be evaluated in other cell lines either separately or in combination against SARS-CoV-2 to determine their antiviral efficiency.
ABSTRACT
Objectives: The COVID-19 was identified for the first time from the sea food market, Wuhan city, China in 2019 and the pathogenic organism was identified as SARS-CoV-2. Currently, this virus has spread to 223 countries and territories and known as a serious issue for the global human community. Many vaccines have been developed and used for immunization. Methods: We have reported the insilico prediction, designing, secondary structure prediction, molecular docking analysis, and in vitro assessment of siRNAs against SARS-CoV-2. The online bioinformatic approach was used for siRNAs selection and designing. The selected siRNAs were evaluated for antiviral efficacy by using Lipofectamine 2000 as delivery agent to HEK-293 cells. The MTT assay was used for cytotoxicity determination. The antiviral efficacy of potential siRNAs was determined based on the Ct value of q-RT-PCR and the data analysis was done by Prism-GraphPad software. Results: The analyzed data resulted in the selection of only three siRNAs out of twenty-six siRNAs generated by online software. The secondary structure prediction and molecular docking analysis of siRNAs revealed the efficient binding to the target. There was no cellular toxicity observed in the HEK-293 cells at any tested concentrations of siRNAs. The purification of RNA was completed from inoculated cells and subjected to q-RT-PCR. The highest Ct value was observed in siRNA 3 than the others. The results offered valuable evidence and invigorated us to assess the potency of siRNAs by using alone or in combination in other human cells. Conclusion: The data generated from this study indicates the significance of in silico prediction and narrow down the potential siRNA' against SARS-CoV-2, and molecular docking investigation offered the effective siRNAs binding with the target. Finally, it is concluded that the online bioinformatics approach provided the prediction and selection of siRNAs with better antiviral efficacy. The siRNA-3 was observed to be the best for reduction of viral RNA in cells.