Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

MOTIVATION: Computational modeling of protein-DNA complexes remains a challenging problem in structural bioinformatics. One of the key factors for a successful protein-DNA docking is a potential function that can accurately discriminate the near-native structures from decoy complexes and at the same time make conformational sampling more efficient. Here, we developed a novel orientation-dependent, knowledge-based, residue-level potential for improving transcription factor (TF)-DNA docking. RESULTS: We demonstrated the performance of this new potential in TF-DNA binding affinity prediction, discrimination of native protein-DNA complex from decoy structures, and most importantly in rigid TF-DNA docking. The rigid TF-DNA docking with the new orientation potential, on a benchmark of 38 complexes, successfully predicts 42% of the cases with root mean square deviations lower than 1 Å and 55% of the cases with root mean square deviations lower than 3 Å. The results suggest that docking with this new orientation-dependent, coarse-grained statistical potential can achieve high-docking accuracy and can serve as a crucial first step in multi-stage flexible protein-DNA docking. AVAILABILITY AND IMPLEMENTATION: The new potential is available at http://bioinfozen.uncc.edu/Protein_DNA_orientation_potential.tar.