A Computational Approach for Designing and Validating Small Interfering RNA against SARS-CoV-2 Variants.

Abstract

The aim of this study is to develop a novel antiviral strategy capable of efficiently targeting a broad set of SARS-CoV-2 variants. 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. RNA interference (RNAi) is a remarkable biological mechanism that can induce gene silencing by targeting complementary mRNA and inhibiting its translation. In this study, using the computational approach, we predicted the most efficient siRNA capable of inhibiting SARS-CoV-2 variants of concern (VoCs). 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. The study contributes to the possibility of designing and developing an effective response strategy against existing variants of concerns and preventing further.