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De novo 3D models of SARS-CoV-2 RNA elements and small-molecule-binding RNAs to guide drug discovery
Ramya Rangan.
Andrew M. Watkins.
Wipapat Kladwang.
Rhiju Das.
Acceso Abierto
The rapid spread of COVID-19 motivates development of antivirals targeting conserved molecular machinery of the SARS-CoV-2 virus. The SARS-CoV-2 genome includes conserved RNA elements that offer potential targets for RNA-targeting small-molecule drugs, but 3D structures of most of these elements have not been experimentally characterized. Here, we provide a dataset called 'FARFAR2-SARS-CoV-2', a collection of 3D coordinates modeled using Rosetta's FARFAR2 algorithm, including de novo models for thirteen RNA elements in SARS-CoV-2 and homology models for a fourteenth. These elements comprise SL1, SL2, SL3, SL4, SL5, putative SL6 and SL7 in the extended 5' UTR, as well as the entire extended 5' UTR; the frameshifting element (FSE) from the SARS-CoV-2 ORF1a/b gene and a putative dimer of FSE; and the extended pseudoknot, hypervariable region, and the s2m of the 3' UTR, as well as the entire 3' UTR. For five of these elements (SL1, SL2, SL3, FSE, s2m), convergence of lowest predicted energy structures supports their accuracy in capturing low energy states that might be targeted for small molecule binding. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second benchmarking dataset called 'FARFAR2-Apo-Riboswitch', which consists of similarly prepared Rosetta-FARFAR2 models for RNA riboswitch aptamer regions that bind small molecules. Both datasets include up to 400 3D models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of low-energy excited states of RNA molecules.
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