Anin silicomap of the SARS-CoV-2 RNA Structurome

Abstract

SARS-CoV-2 is a positive-sense single-stranded RNA virus that has exploded throughout the global human population. This pandemic coronavirus strain has taken scientists and public health researchers by surprise and knowledge of its basic biology (e.g. structure/function relationships in its genomic, messenger and template RNAs) and modes for therapeutic intervention lag behind that of other human pathogens. In this report we used a recently-developed bioinformatics approach, ScanFold, to deduce the RNA structural landscape of the SARS-CoV-2 transcriptome. We recapitulate known elements of RNA structure and provide a model for the folding of an essential frameshift signal. Our results find that the SARS-CoV-2 is greatly enriched in unusually stable and likely evolutionarily ordered RNA structure, which provides a huge reservoir of potential drug targets for RNA-binding small molecules. Our results also predict regions that are accessible for intermolecular interactions, which can aid in the design of antisense therapeutics. All results are made available via a public database (the RNAStructuromeDB) where they may hopefully drive drug discovery efforts to inhibit SARS-CoV-2 pathogenesis. Δ G° z-score region is the 3′UTR, which was found to yield mostly positive z-scores; despite a higher than average GC content for this region (0.45 on average; Table S1), MFE values here were less stable than expected, averaging Δ G° z-scores of +0.98 (or roughly one standard deviation less stable than random). Scans were also performed in the negative sense of the genome, revealing slightly less propensity for structure. The MFE values ranged from -42.9 to -6.0 and averaged -23.25 kcal/mol; z-scores in the negative sense had a similar range of Δ G° z-scores as the positive sense (-5.76 to 2.66) but averaged lower at -1.12 (Table S1). Interestingly, Δ G° z-scores for the 3′ UTR in negative sense were more skewed to the negative (finding minimums as low as -2.32) resulting in an average z-score of 0.16 for the region.

Whole genome identification of potential G-quadruplexes and analysis of the G-quadruplex binding domain for SARS-CoV-2

“…Nine PG4s were scattered on the negative-sense strand. To further characterize the potential canonical secondary structures competitive with Gquadruplexes, the landscape of thermodynamic stability of the SARS-CoV-2 genome was depicted by using ΔG°z-score [55]. In general, a positive ΔG°z-score implies that the secondary structure of this region tends to be less stable than the randomly shuffled sequence with the identical nucleotide composition, while a negative ΔG°z-score signifies higher stability than the randomly shuffled sequence.…”

Specific viral RNA drives the SARS CoV-2 nucleocapsid to phase separate

“…End and Frameshifting-regions and compared to the rest of the gRNA. Most of the RNA genome has local minimum free energies for predicted structures, and A/U-content, ∆G z-score and ensemble diversity (ED) similar to that of the Frameshifting-region ( Figure 3E, Figure S4F/G) (18). Interestingly, the two major LLPS-promoting sequences, the 5'-End and Nucleocapsid-encoding region at the 3' end of the genome ( Figure 1E…”

“…Python library to distinguish between 120-base pair sliding windows in the 5'-End and the Frameshifting-region of SARS-CoV-2 (NC_045512.2 ) based on the following features: percent A content, percent U content, mean free energy ΔG, ΔG Z-score, and ensemble diversity, where mean free energy ΔG, ΔG Z-score, and ensemble diversity values were taken from(18). Features were scaled before classification to have mean of 0 and standard deviation of 1.…”

“…Mean for each feature is computed over all 120 base pair windows with center in the region of interest. MFE, dG z-score, and ensemble diversity are defined in(18). F) Model of for LLPS: Upper panel: 5'-End LLPS coincides with an increase in valency.…”

Implications of SARS-CoV-2 mutations for genomic RNA structure and host microRNA targeting

“…However, nucleic acid secondary structure and sub-translational events have a critical impact on genome replication [4], virus maturation and genome packaging [5]. In addition, the RNA secondary structures of SARS-CoV-2 genes have been proposed to be druggable targets [6][7][8]. Because little is known of the influence of SARS-CoV-2 mutations on the RNA secondary structure, and its possible implications for inhibition by host miRNA, we have modeled the impact of common mutations of the structure and susceptibility of the SARS-CoV-2 genome to interference from host miRNA.…”