Strategic Computational Design of siRNA Molecules Targeting Structural Genes of SARS-CoV-2.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has had a profound impact on global health and economies. The genomic structure of SARS-CoV-2 closely resembles that of the previously known severe acute respiratory syndrome coronavirus (SARS-CoV), leading to its classification as SARS-CoV-2. The pandemic resulted in widespread loss of life and significant economic disruption, worldwide. Although vaccines have made considerable progress in preventing severe illness, there is ongoing need for additional therapeutic options. RNA interference (RNAi) is a promising antiviral strategy, utilized the small interfering RNA (siRNA) molecules to degrade target mRNA in a sequence-specific manner. This study aimed to design potential siRNA candidates targeting the structural protein-coding genes; spike (S), envelope (E), membrane (M), and nucleocapsid (N) of SARS-CoV-2 using in silico methods. Web-based algorithms were employed to predict a range of 19-mer siRNAs targeting these genes, which were subsequently evaluated based on parameters such as sequence score, configuration, off-target effects, seed-target stability, free energy of folding, and hybridization with target mRNA. In addition, molecular docking studies were performed and the binding strengths of the siRNAs targeting corresponding viral proteins were calculated. The top three siRNAs for each gene were selected. Preliminary results suggest that these siRNA candidates possess strong anti-SARS-CoV-2 potential, though further in vitro and in vivo validation is needed.