A computational approach for designing and validating small interfering RNA against SARS-CoV-2 variants
Anupam Mukherjee, Kishore Dhotre, Debashree Dass, Anwesha Banerjee, Vijay Nema- Drug Discovery
- Molecular Medicine
- General Medicine
Aims:
The aim of this study is to develop a novel antiviral strategy capable of efficiently targeting a broad set of SARS-CoV-2 variants.
Background:
Since the first emergence of SARS-CoV-2, it has rapidly transformed into a global pandemic, posing an unprecedented threat to public health. SARS-CoV-2 is prone to mutation and continues to evolve, leading to the emergence of new variants capable of escaping immune protection achieved due to previous SARS-CoV-2 infections or by vaccination.
Objective:
RNA interference (RNAi) is a remarkable biological mechanism that can induce gene silencing by targeting complementary mRNA and inhibiting its translation.
Method:
In this study, using the computational approach, we predicted the most efficient siRNA capable of inhibiting SARS-CoV-2 variants of concern (VoCs).
Result:
The presented siRNA was characterized and evaluated for its thermodynamic properties, offsite-target hits, and in silico validation by molecular docking and molecular dynamics simulations (MD) with Human AGO2 protein.
Conclusion:
The study contributes to the possibility of designing and developing an effective response strategy against existing variants of concerns and preventing further.