Prediction of Putative Potential Sirnas for Inhibiting SARS-CoV-2 Strains, Including Variants of Concern and Interest

Abstract

Aim: To predict siRNAs as a therapeutic intervention for highly infectious new variants of SARS-CoV-2. Methods: Conserved coding sequence regions of 11 SARS-CoV-2 proteins were used to construct siRNAs through sampling of metadata comprising 214,256 sequences. Results: Predicted siRNAs S1: 5′-UCAUUGAGAAAUGUUUACGCA-3′ and S2: 5′-AAAGACAUCAGCAUACUCCUG-3′ against RdRp of SARS-CoV-2 satisfied all the stringent filtering processes and showed good binding characteristics. The designed siRNAs are expected to inhibit viral replication and transcription of various coronavirus strains encompassing variants of concern and interest. Conclusion: The predicted siRNAs are expected to be potent against SARS-CoV-2, and following in vitro and in vivo validations may be considered as potential therapeutic measures.

Keywords::

• COVID-19
• novel coronavirus
• SARS
• SARS-CoV-2
• siRNA

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.tandfonline.com/doi/suppl/10.2217/fmb-2021-0130

Author contributions

AHM Nurun Nabi conceived the idea and supervised the research. A Al Saba and M Adiba retrieved the data and performed the analyses. S Chakraborty validated the prediction and docking process. AHM Nurun Nabi, A Al Saba and M Adiba prepared the first draft and S Chakraborty critically reviewed it. All authors approved the manuscript.

Financial & competing interests disclosure

Part of this research work was supported by a grant (1280101-120008431-3631107) provided by the Information and Communication Technology Division Under the Ministry of Posts, Telecommunications and Information Technology, Government of the People's Republic of Bangladesh (2019–2020). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Additional information

Funding

Part of this research work was supported by a grant (1280101-120008431-3631107) provided by the Information and Communication Technology Division Under the Ministry of Posts, Telecommunications and Information Technology, Government of the People's Republic of Bangladesh (2019–2020). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.