RAPID: Exploring Covid-19 RNA Viral Targets By Graph-Theory-Based Modeling

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The urgently needed treatments and vaccines for COVID-19 rely on a fundamental understanding of the complex viral apparatus. This project will determine the structural properties and drug-binding potential of two regions of the viral RNA essential for invasion and propagation of the COVID-19 genome in host cells: genes responsible for making spike and fusion proteins. Specifically, the project will develop new and efficient graph-theory based computational algorithms for identifying subregions in the COVID-19 viral genome that alter the RNA substructure when they are mutated. Identification of these subregions will aid in the discovery of anti-viral inhibitor compounds. Graph theory tools already developed in the PI’s lab offer coarse-grained approaches for RNA structural analysis and design. The PI will combine these tools with biomolecular modeling to examine the therapeutic potential of anti-viral inhibitors known from SARS, MERS, and other viruses. This project will produce structural insights into the RNA viral regions and identify critical nucleotides and candidate inhibitors that will help make progress against COVID-19. The research has profound impact to COVID-19 as well as other coronaviruses that could emerge in the future. The project offers unique interdisciplinary training in mathematics, biology, chemistry, and scientific computing for young scientists, including women and minorities. The research results will be shared rapidly with the COVID-19 research community at large.RNA-targeting approaches have therapeutic potential due to the high sequence and structure conservation of the viral genomes and the rapid emergence of CRISPR technology. They also present alternatives when protein-inhibiting compounds lead to invasion of the RNA viral genome itself. Such compounds that alter the RNA structure significantly are expected to inhibit viral invasion and replication. Because the fusion-protein coding region contains a pseudoknot (intertwined base-pair) substructure involved in a frame-shifting mechanism, the determination of critical mutation regions and associated compounds that destroy this pseudoknot will be invaluable. The project team has rich experience in biomolecular modeling and simulation of nucleic acid complexes and has developed a graph-theory framework for analyzing RNA motifs, predicting structures, and designing novel RNA folds. The graph-theory framework will be extended and applied in this project to address the COVID-19 pandemic by determining key regions in the RNA COVID-19 viral genome and associated chemical inhibitors that would interfere with viral fusion into and replication within host cells. With this award, the Mathematical Biology Program in the Division of Mathematical Sciences and the Chemistry of Life Processes Program in the Division of Chemistry are funding Dr. Schlick from New York University to determine the structural properties and drug-binding potential of the COVID-19 viral RNA.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds allocated to MPS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.