Computational prediction of potential siRNA and human miRNA sequences to silence orf1ab associated genes for future therapeutics against SARS-CoV-2

The coronavirus disease 2019 (COVID-19) is an ongoing pandemic caused by an RNA virus termed as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). SARS-CoV-2 possesses an almost 30kbp long genome. The genome contains open-reading frame 1ab (ORF1ab) gene, the largest one of SARS-CoV-2, encoding polyprotein PP1ab and PP1a responsible for viral transcription and replication. Several vaccines have already been approved by the respective authorities over the world to develop herd immunity among the population. In consonance with this effort, RNA interference (RNAi) technology holds the possibility to strengthen the fight against this virus. Here, we have implemented a computational approach to predict potential short interfering RNAs including small interfering RNAs (siRNAs) and microRNAs (miRNAs), which are presumed to be intrinsically active against SARS-CoV-2. In doing so, we have screened miRNA library and siRNA library targeting the ORF1ab gene. We predicted the potential miRNA and siRNA candidate molecules utilizing an array of bioinformatic tools. By extending the analysis, out of 24 potential pre-miRNA hairpins and 131 siRNAs, 12 human miRNA and 10 siRNA molecules were sorted as potential therapeutic agents against SARS-CoV-2 based on their GC content, melting temperature (Tm), heat capacity (Cp), hybridization and minimal free energy (MFE) of hybridization. This computational study is focused on lessening the extensive time and labor needed in conventional trial and error based wet lab methods and it has the potential to act as a decent base for future researchers to develop a successful RNAi therapeutic