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

Kishore      Dhotre; Debashree      Dass; Anwesha      Banerjee; Vijay      Nema; Anupam      Mukherjee
doi:10.2174/1573409920666230825111406
Abstract

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.
Methods: In this study, using the computational approach, we predicted the most efficient siRNA capable of inhibiting SARS-CoV-2 variants of concern (VoCs).
Results: 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.
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Graphical Abstract

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DOI https://dx.doi.org/10.2174/1573409920666230825111406	Print ISSN 1573-4099
Publisher Name Bentham Science Publisher	Online ISSN 1875-6697
Current Computer-Aided Drug Design

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

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