Poster abstracts

Poster number 17 submitted by Peter Forstmeier

Computational discovery and validation of novel functional RNAs in SARS-CoV-2

Peter C. Forstmeier (Pennsylvania State University, Department of Biochemistry and Molecular Biology), McCauley O. Meyer (Pennsylvania State University, Department of Biochemistry and Molecular Biology), Philip C. Bevilacqua (Pennsylvania State University, Department of Biochemistry and Molecular Biology, Department of Chemistry)

Abstract:
The COVID-19 pandemic continues to have a profound impact on the world. With millions affected in the United States alone, it is critical to quickly develop therapeutics against the virus. The causal virus of COVID-19, SARS-CoV-2, is an RNA virus known to contain functional RNA structures1, which are good therapeutic targets. Finding and characterizing them in a rapid and accurate manner is imperative. Regions with putative RNA structures were identified via a novel computational pipeline that we describe here for the first time, called ScanFilter. In phase one of ScanFilter, the program ScanFold2 is used to find structured regions, while two other programs are used to filter out known structures. In phase two of the pipeline, SPOT-RNA, a pseudoknot-predicting program, selects for structured regions that contain pseudoknots3. We chose pseudoknots as an indicator of functionality because most functional RNA elements contain at least one pseudoknot4. In order to assess the functionality of these regions, a series of programs was added to the third phase of the pipeline. These programs find the SNPs from the ~24,000 (as of 9/24/2020) sequenced genomes of SARS-CoV-2 in the putatively functional regions and determine if a SNP causes a change in the ensemble structure of the RNA or disrupts a pseudoknot. Disruptions in the functional region could change its ability to fold and prevent its function. Regions that do not contain many changes or disruptions may be functional because their invariant structure serves a vital biological function. The presented pipeline is a powerful tool that can computationally find and validate novel therapeutic targets for SARS-CoV-2 using existing data.

References:
(1) Kim, D.; Lee, J. Y.; Yang, J. S.; Kim, J. W.; Kim, V. N.; Chang, H. The Architecture of SARS-CoV-2 Transcriptome. Cell 2020, 181 (4), 914-921.e10. https://doi.org/10.1016/j.cell.2020.04.011.
(2) Andrews, R. J.; Roche, J.; Moss, W. N. ScanFold: An Approach for Genome-Wide Discovery of Local RNA Structural Elements—Applications to Zika Virus and HIV. PeerJ 2018, 6, e6136. https://doi.org/10.7717/peerj.6136.
(3) Singh, J.; Hanson, J.; Paliwal, K.; Zhou, Y. RNA Secondary Structure Prediction Using an Ensemble of Two-Dimensional Deep Neural Networks and Transfer Learning. Nat. Commun. 2019, 10 (1), 5407.
(4) Staple, D. W.; Butcher, S. E. Pseudoknots: RNA Structures with Diverse Functions. PLoS Biol. 2005, 3 (6), e213. https://doi.org/10.1371/journal.pbio.0030213.

Keywords: SARS-CoV-2, RNA, Pseudoknots