Repositioning Drugs Against COVID-19 Using Network-Based Label Propagation
Motivated by the critical need to understand the pathogenesis of COVID-19, we present a genome-scale, systems-level computational methodology that aims to reveal host biological processes that may be used by SARS-CoV-2 to infect cells. We adapt and specialize network label propagation methods to predict human proteins that may directly or indirectly interact with SARS-CoV-2. Our results shed light on new aspects of infection mechanisms used by the virus and prioritize drug targets for COVID-19. The top-ranked proteins we identify are enriched in host biological processes that are potentially co-opted by the virus.
We discuss insights generated by our methodology on abnormal blood coagulation observed in COVID-19 patients. Our prioritized lists of human proteins, protein networks, and drug targets are available as resources for researchers who are studying host-SARS-CoV-2 biology or repositioning drugs or developing therapeutics as anti-COVID-19 agents. The reproducible methodology we develop can be used virtually without modifications for other viruses.
T. M. Murali is a professor in the Department of Computer Science at Virginia Tech. He is the associate director for the Computational Tissue Engineering interdisciplinary graduate education program. His research group develops phenomenological and predictive models dealing with the function, behavior, and properties of large-scale molecular interaction networks in the cell. He received his undergraduate degree in computer science from the Indian Institute of Technology, Madras and his Sc. M. and Ph. D. degrees from Brown University. Murali is a Distinguished Scientist of the Association for Computing Machinery.