In Silico Prediction and Designing of Potential siRNAs to be used as Antivirals Against SARS-CoV-2

Author(s): Sayed S. Sohrab, Sherif A. El-Kafrawy, Aymn T. Abbas, Leena H. Bajrai, Esam I. Azhar*

Journal Name: Current Pharmaceutical Design

Volume 27 , Issue 32 , 2021


Become EABM
Become Reviewer
Call for Editor

Abstract:

Background: The unusual pneumonia outbreak that originated in the city of Wuhan, China in December 2019 was found to be caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19.

Methods: In this work, we have performed an in silico design and prediction of potential siRNAs based on genetic diversity and recombination patterns, targeting various genes of SARS-CoV-2 for antiviral therapeutics. We performed extensive sequence analysis to analyze the genetic diversity and phylogenetic relationships, and to identify the possible source of virus reservoirs and recombination patterns, and the evolution of the virus as well as we designed the siRNAs which can be used as antivirals against SARS-CoV-2.

Results: The sequence analysis and phylogenetic relationships indicated high sequence identity and closed clusters with many types of coronavirus. In our analysis, the full-genome of SARS-CoV-2 showed the highest sequence (nucleotide) identity with SARS-bat-ZC45 (87.7%). The overall sequence identity ranged from 74.3% to 87.7% with selected SARS viruses. The recombination analysis indicated the bat SARS virus is a potential recombinant and serves as a major and minor parent. We have predicted 442 siRNAs and finally selected only 19 functional, and potential siRNAs.

Conclusion: The siRNAs were predicted and selected based on their greater potency and specificity. The predicted siRNAs need to be validated experimentally for their effective binding and antiviral activity.

Keywords: SARS-CoV-2, SARS, MERS-CoV, in silico prediction, siRNAs, antivirals.

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 27
ISSUE: 32
Year: 2021
Published on: 11 January, 2021
Page: [3490 - 3500]
Pages: 11
DOI: 10.2174/1381612827999210111194101
Price: $65

Article Metrics

PDF: 202