Evitar: designing anti-viral RNA therapies against future RNA viruses.
Bioinformatics
; 38(9): 2437-2443, 2022 04 28.
Article
in English
| MEDLINE | ID: covidwho-1746947
ABSTRACT
MOTIVATION The coronavirus disease 2019 (COVID-19) pandemic has highlighted the threat of emerging respiratory viruses and has exposed the lack of availability of off-the-shelf therapeutics against new RNA viruses. Previous research has established the potential that siRNAs and RNA-targeting CRISPR have in combating known RNA viruses. However, the feasibility and tools for designing anti-viral RNA therapeutics against future RNA viruses have not yet been established. RESULTS:
We develop the Emerging-Virus-Targeting RNA (Evitar) pipeline for designing anti-viral siRNAs and CRISPR Cas13a guide RNA (gRNA) sequences. Within Evitar, we develop Greedy Algorithm with Redundancy and Similarity-weighted Greedy Algorithm with Redundancy to enhance the performance. Time simulations using known coronavirus genomes deposited as early as 10 years prior to the COVID-19 outbreak show that at least three SARS-CoV-2-targeting siRNAs are among the top 30 pre-designed siRNAs. In addition, among the top 19 pre-designed gRNAs, there are three SARS-CoV-2-targeting Cas13a gRNAs that could be predicted using information from 2011. Before-the-outbreak design is also possible against the MERS-CoV virus and the 2009-H1N1 swine flu virus. Designed siRNAs are further shown to suppress SARS-CoV-2 viral sequences using in vitro reporter assays. Our results support the utility of Evitar to pre-design anti-viral siRNAs/gRNAs against future viruses. Therefore, we propose the development of a collection consisting of roughly 30 pre-designed, safety-tested and off-the-shelf siRNA/CRISPR therapeutics that could accelerate responses to future RNA virus outbreaks. AVAILABILITY AND IMPLEMENTATION Codes are available at GitHub (https//github.com/dingyaozhang/Evitar). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Influenza A Virus, H1N1 Subtype
/
COVID-19
Type of study:
Prognostic study
Limits:
Animals
Language:
English
Journal:
Bioinformatics
Journal subject:
Medical Informatics
Year:
2022
Document Type:
Article
Affiliation country:
Bioinformatics
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