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Browsing by Author "Kasif, Simon"

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    Identifying Human Interactors of SARS-CoV-2 Proteins and Drug Targets for COVID-19 using Network-Based Label Propagation
    Law, Jeffrey N.; Akers, Kyle; Tasnina, Nure; Della Santina, Catherine M.; Kshirsagar, Meghana; Klein-Seetharaman, Judith; Crovella, Mark; Rajagopalan, Padmavathy; Kasif, Simon; Murali, T. M. (Virginia Tech, 2020-06-22)
    Motivated by the critical need to identify new treatments for COVID- 19, we present a genome-scale, systems-level computational approach to prioritize drug targets based on their potential to regulate host- virus interactions or their downstream signaling targets. We adapt and specialize network label propagation methods to this end. We demonstrate that these techniques can predict human-SARS-CoV- 2 protein interactors with high accuracy. The top-ranked proteins that we identify are enriched in host biological processes that are potentially coopted by the virus. We present cases where our methodology generates promising insights such as the potential role of HSPA5 in viral entry. We highlight the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. We identify tubulin proteins involved in ciliary assembly that are targeted by anti-mitotic drugs. Drugs that we discuss are already undergoing clinical trials to test their efficacy against COVID-19. Our prioritized list of human proteins and drug targets is available as a general resource for biological and clinical researchers who are repositioning existing and approved drugs or developing novel therapeutics as anti-COVID-19 agents.
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    Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2
    Law, Jeffrey N.; Akers, Kyle; Tasnina, Nure; Della Santina, Catherine M.; Deutsch, Shay; Kshirsagar, Meghana; Klein-Seetharaman, Judith; Crovella, Mark; Rajagopalan, Padmavathy; Kasif, Simon; Murali, T. M. (Oxford University Press, 2021-12-01)
    BACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. RESULTS: We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. CONCLUSIONS: We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.