Abstract
Ebola virus disease (EVD) is extremely virulent with an estimated mortality rate of up to 90%. However, the state-of-the-art treatment for EVD is limited to quarantine and supportive care. The 2014 Ebola epidemic in West Africa, the largest in history, is believed to have caused more than 11,000 fatalities. The countries worst affected are also among the poorest in the world. Given the complexities, time, and resources required for a novel drug development, finding efficient drug discovery pathways is going to be crucial in the fight against future outbreaks. We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for rapid therapeutic repurposing and discovery. We used the CANDO platform to identify and rank FDA-approved drug candidates that bind and inhibit all proteins encoded by the genomes of five different Ebola virus strains. Top ranking drug candidates for EVD treatment generated by CANDO were compared to in vitro screening studies against Ebola virus-like particles (VLPs) by Kouznetsova et al. and genetically engineered Ebola virus and cell viability studies by Johansen et al. to identify drug overlaps between the in virtuale and in vitro studies as putative treatments for future EVD outbreaks. Our results indicate that integrating computational docking predictions on a proteomic scale with results from in vitro screening studies may be used to select and prioritize compounds for further in vivo and clinical testing. This approach will significantly reduce the lead time, risk, cost, and resources required to determine efficacious therapies against future EVD outbreaks.
Highlights
The 2014 Ebola epidemic was caused by a divergent strain of the Zaire Ebola Virus [1] and is believed to have affected more than 28,000 individuals globally, with an estimated mortality of 74%in confirmed Ebola cases [2]
We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery
The rough poses of compound–proteome interactions are determined using chem- and bioinformatics methods and hierarchically refined using fragment-based docking with dynamics simulations of all the atoms in the system, which we have shown previously to be necessary for the accurate calculation of binding energies [34,35]
Summary
The 2014 Ebola epidemic was caused by a divergent strain of the Zaire Ebola Virus [1] and is believed to have affected more than 28,000 individuals globally, with an estimated mortality of 74%in confirmed Ebola cases [2]. Molecules 2016, 21, 1537 among the poorest in the world, finding an alternate cheaper route for future EVD outbreak treatments is of paramount importance. Traditional approaches to drug discovery are highly specific to single targets (molecules and among the poorest inon thea world, finding alternate cheaper route for future EVDprotein outbreak treatments indications), focusing limited set ofaninteractions between individual targets andissmall of paramount importance. Traditional approaches to drug discovery are highly specific to single targets (molecules and generally is to target an essential protein responsible for pathogenesis so as to completely inhibit its indications), focusing on a limited set of interactions between individual protein targets and small function, and determine its toxicity or side effect profile for human use. Almost all current drugs molecule compounds, but applying the resulting treatments universally to all patients. The goal have generally been developed by an this approach
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