An in silico map of the SARS-CoV-2 RNA Structurome
| Autor: | Hafeez S. Haniff, Disney, Walter N. Moss, Grefe M, Christopher C. Williams, Ryan J. Andrews, Jake Peterson, Jonathan L. Chen |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
AcademicSubjects/SCI01140
AcademicSubjects/SCI01060 In silico 0206 medical engineering Population AcademicSubjects/SCI00030 02 engineering and technology Computational biology Standard Article medicine.disease_cause AcademicSubjects/SCI01180 Article Transcriptome 03 medical and health sciences medicine Nucleic acid structure education 030304 developmental biology Coronavirus 0303 health sciences education.field_of_study biology Drug discovery RNA RNA virus biology.organism_classification 3. Good health AcademicSubjects/SCI00980 020602 bioinformatics |
| Zdroj: | NAR Genomics and Bioinformatics bioRxiv article-version (status) pre article-version (number) 1 |
| DOI: | 10.1101/2020.04.17.045161 |
| Popis: | SARS-CoV-2 has exploded throughout the human population. To facilitate efforts to gain insights into SARS-CoV-2 biology and to target the virus therapeutically, it is essential to have a roadmap of likely functional regions embedded in its RNA genome. In this report, we used a bioinformatics approach, ScanFold, to deduce the local RNA structural landscape of the SARS-CoV-2 genome with the highest likelihood of being functional. We recapitulate previously-known elements of RNA structure and provide a model for the folding of an essential frameshift signal. Our results find that SARS-CoV-2 is greatly enriched in unusually stable and likely evolutionarily ordered RNA structure, which provides a large reservoir of potential drug targets for RNA-binding small molecules. Results are enhanced via the re-analyses of publicly-available genome-wide biochemical structure probing datasets that are broadly in agreement with our models. Additionally, ScanFold was updated to incorporate experimental data as constraints in the analysis to facilitate comparisons between ScanFold and other RNA modelling approaches. Ultimately, ScanFold was able to identify eight highly structured/conserved motifs in SARS-CoV-2 that agree with experimental data, without explicitly using these data. All results are made available via a public database (the RNAStructuromeDB: https://structurome.bb.iastate.edu/sars-cov-2) and model comparisons are readily viewable at https://structurome.bb.iastate.edu/sars-cov-2-global-model-comparisons. |
| Databáze: | OpenAIRE |
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