High-throughput Activity Assay for Screening Inhibitors of the SARS-CoV-2 Mac1 Macrodomain

Macrodomains are a class of conserved ADP-ribosylhydrolases expressed by viruses of pandemic concern, including coronaviruses and alphaviruses. Viral macrodomains are critical for replication and virus-induced pathogenesis; therefore, these enzymes are a promising target for antiviral therapy. However, no potent or selective viral macrodomain inhibitors currently exist, in part due to the lack of a high-throughput assay for this class of enzymes. Here, we developed a high-throughput ADP-ribosylhydrolase assay using the SARS-CoV-2 macrodomain Mac1. We performed a pilot screen which identified dasatinib and dihydralazine as ADP-ribosylhydrolase inhibitors. Importantly, dasatinib does not inhibit MacroD2, the closest Mac1 homolog in humans. Our study demonstrates the feasibility of identifying selective inhibitors based on ADP-ribosylhydrolase activity, paving the way for screening large compound libraries to identify improved macrodomain inhibitors and explore their potential as antiviral therapies for SARS-CoV-2 and future viral threats.

Title: COVID-19Predict - Predicting Pandemic Trends.

BackgroundGiven the global public health importance of the COVID-19 pandemic, data comparisons that predict on-going infection and mortality trends across national, state and county-level administrative jurisdictions are vitally important. We have designed a COVID-19 dashboard with the goal of providing concise sets of summarized data presentations to simplify interpretation of basic statistics and location-specific current and short-term future risks of infection. MethodsWe perform continuous collection and analyses of publicly available data accessible through the COVID-19 dashboard hosted at Johns Hopkins University (JHU github). Additionally, we utilize the accumulation of cases and deaths to provide dynamic 7-day short-term predictions on these outcomes across these national, state and county administrative levels. FindingsCOVID-19Predict produces 2,100 daily predictions [or calculations] on the state level (50 States x3 models x7 days x2 cases and deaths) and 131,964 (3,142 Counties x3 models x7 days x2 cases and deaths) on the county level. To assess how robust our models have performed in making short-term predictions over the course of the pandemic, we used available case data for all 50 U.S. states spanning the period January 20 - August 16 2020 in a retrospective analysis. Results showed a 3.7% to -0.2% mean error of deviation from the actual case predictions to date. InterpretationOur transparent methods and admin-level visualizations provide real-time data reporting and forecasts related to on-going COVID-19 transmission allowing viewers (individuals, health care providers, public health practitioners and policy makers) to develop their own perspectives and expectations regarding public life activity decisions. FundingFinancial resources for this study have been provided by Case Western Reserve University.