Structural model sets predicted for SARS-CoV-2 genomic elements
Abstract/Contents
- Abstract
- The rapid spread of COVID-19 is motivating 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 collection of 3D models based on Rosetta’s FARFAR2 algorithm, including de novo models for fifteen RNA elements in SARS-CoV-2 and homology models for a sixteenth. These elements comprise the individual stems SL1-8 in the extended 5′ UTR along with the entire extended 5′ UTR; the reverse complement of SL1-4 in the 5′ UTR; the frameshift stimulating 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 along with the entire 3′ UTR. For ten of these elements (SL1, SL2, SL3, SL4, SL5, SL6, SL7, SL8, the reverse complement of SL1-4, FSE, and s2m), convergence of lowest predicted energy structures supports their accuracy in capturing low energy states that might be targeted for small molecule binding; and subsequent cryo-EM characterization of FSE confirms the accuracy of the modeling. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of models which consists of similarly prepared Rosetta-FARFAR2 models for RNA riboswitch aptamer regions that bind small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’, https://github.com/DasLab/FARFAR2-SARS-CoV-2; and ‘FARFAR2-Apo-Riboswitch’, at https://github.com/DasLab/FARFAR2-Apo-Riboswitch’) 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.
Description
| Type of resource | software, multimedia |
|---|---|
| Date created | 2020 |
Creators/Contributors
| Author | Watkins, Andrew Martin |
|---|---|
| Author | Rangan, Ramya |
| Author | Rynge, Mats |
| Author | Thain, Gregory |
| Author | Chacon, Jose |
| Author | Das, Rhiju |
Subjects
| Subject | RNA |
|---|---|
| Subject | 3D modeling |
| Subject | School of Medicine |
| Subject | Biochemistry |
| Genre | Dataset |
Bibliographic information
| Related Publication | https://www.biorxiv.org/content/10.1101/2020.04.14.041962v1 |
|---|---|
| Related item | |
| Location | https://purl.stanford.edu/pp620tj8748 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial Share Alike 3.0 Unported license (CC BY-NC-SA).
Preferred citation
- Preferred Citation
- Watkins, Andrew Martin and Rangan, Ramya and Rynge, Mats and Thain, Gregory and Chacon, Jose and Das, Rhiju. (2020). Structural model sets predicted for SARS-CoV-2 genomic elements. Stanford Digital Repository. Available at: https://purl.stanford.edu/pp620tj8748
Collection
Stanford Research Data
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- Contact
- amw579@stanford.edu
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