COVID-19 is a global pandemic, thus requiring multiple strategies to develop modalities against it. Herein, we designed multiple bioactive small molecules that target a functional structure within the SARS-CoV-2’s RNA genome, the causative agent of COVID-19. An analysis to characterize the structure of the RNA genome provided a revised model of the SARS-CoV-2 frameshifting element, in particular its attenuator hairpin. By studying an RNA-focused small molecule collection, we identified a drug-like small molecule ( C5 ) that avidly binds to the revised attenuator hairpin structure with a K d of 11 nM. The compound stabilizes the hairpin’s folded state and impairs frameshifting in cells. The ligand was further elaborated into a ribonuclease targeting chimera (RIBOTAC) to recruit a cellular ribonuclease to destroy the viral genome ( C5-RIBOTAC ) and into a covalent molecule ( C5-Chem-CLIP ) that validated direct target engagement and demonstrated its specificity for the viral RNA, as compared to highly expressed host mRNAs. The RIBOTAC lead optimization strategy improved the bioactivity of the compound at least 10-fold. Collectively, these studies demonstrate that the SARS-CoV-2 RNA genome should be considered druggable.

In addition to encoding RNA primary structures, genomes also encode RNA secondary and tertiary structures that play roles in gene regulation and, in the case of RNA viruses, genome replication. Methods for the identification of functional RNA structures in genomes typically rely on scanning analysis windows, where multiple partially-overlapping windows are used to predict RNA structures and folding metrics to deduce regions likely to form functional structure. Separate structural models are produced for each window, where the step size can greatly affect the returned model. This makes deducing unique local structures challenging, as the same nucleotides in each window can be alternatively base paired. We are presenting here a new approach where all base pairs from analysis windows are considered and weighted by favorable folding. This results in unique base pairing throughout the genome and the generation of local regions/structures that can be ranked by their propensity to form unusually thermodynamically stable folds. We applied this approach to the Zika virus (ZIKV) and HIV-1 genomes. ZIKV is linked to a variety of neurological ailments including microcephaly and Guillain–Barré syndrome and its (+)-sense RNA genome encodes two, previously described, functionally essential structured RNA regions. HIV, the cause of AIDS, contains multiple functional RNA motifs in its genome, which have been extensively studied. Our approach is able to successfully identify and model the structures of known functional motifs in both viruses, while also finding additional regions likely to form functional structures. All data have been archived at the RNAStructuromeDB (), a repository of RNA folding data for humans and their pathogens.

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.

Many proteins are refractory to targeting because they lack small-molecule binding pockets. An alternative to drugging these proteins directly is to target the messenger (m)RNA that encodes them, thereby reducing protein levels. We describe such an approach for the difficult-to-target protein α-synuclein encoded by the SNCA gene. Multiplication of the SNCA gene locus causes dominantly inherited Parkinson’s disease (PD), and α-synuclein protein aggregates in Lewy bodies and Lewy neurites in sporadic PD. Thus, reducing the expression of α-synuclein protein is expected to have therapeutic value. Fortuitously, the SNCA mRNA has a structured iron-responsive element (IRE) in its 5′ untranslated region (5′ UTR) that controls its translation. Using sequence-based design, we discovered small molecules that target the IRE structure and inhibit SNCA translation in cells, the most potent of which is named Synucleozid. Both in vitro and cellular profiling studies showed Synucleozid directly targets the α-synuclein mRNA 5′ UTR at the designed site. Mechanistic studies revealed that Synucleozid reduces α-synuclein protein levels by decreasing the amount of SNCA mRNA loaded into polysomes, mechanistically providing a cytoprotective effect in cells. Proteome- and transcriptome-wide studies showed that the compound’s selectivity makes Synucleozid suitable for further development. Importantly, transcriptome-wide analysis of mRNAs that encode intrinsically disordered proteins revealed that each has structured regions that could be targeted with small molecules. These findings demonstrate the potential for targeting undruggable proteins at the level of their coding mRNAs. This approach, as applied to SNCA, is a promising disease-modifying therapeutic strategy for PD and other α-synucleinopathies.

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.