The infectious SARS-CoV-2 causes COVID-19, which is now a global pandemic. Aiming for effective treatments, we focused on the key drug target, the viral 3C-like (3CL) protease. We modeled a big dataset with 42 SARS-CoV-2 3CL protease–ligand complex structures from ∼98.7% similar SARS-CoV 3CL protease with abundant complex structures. The diverse flexible active site conformations identified in the dataset were clustered into six protease pharmacophore clusters (PPCs). For the PPCs with distinct flexible protease active sites and diverse interaction environments, we identified pharmacophore anchor hotspots. A total of 11 “PPC consensus anchors” (a distinct set observed in each PPC) were observed, of which three “PPC core anchors” EHV2, HV1, and V3 are strongly conserved across PPCs. The six PPC cavities were then applied in virtual screening of 2122 FDA drugs for repurposing, using core anchor-derived “PPC scoring S” to yield seven drug candidates. Experimental testing by SARS-CoV-2 3CL protease inhibition assay and antiviral cytopathic effect assays discovered active hits, Boceprevir and Telaprevir (HCV drugs) and Nelfinavir (HIV drug). Specifically, Boceprevir showed strong protease inhibition with micromolar IC50 of 1.42 μM and an antiviral activity with EC50 of 49.89 μM, whereas Telaprevir showed moderate protease inhibition only with an IC50 of 11.47 μM. Nelfinavir solely showed antiviral activity with a micromolar EC50 value of 3.28 μM. Analysis of binding mechanisms of protease inhibitors revealed the role of PPC core anchors. Our PPCs revealed the flexible protease active site conformations, which successfully enabled drug repurposing.

BackgroundComputational drug design approaches are important for shortening the time and reducing the cost for drug discovery and development. Among these methods, molecular docking and quantitative structure activity relationship (QSAR) play key roles for lead discovery and optimization. Here, we propose an integrated approach with core strategies to identify the protein-ligand hot spots for QSAR models and lead optimization. These core strategies are: 1) to generate both residue-based and atom-based interactions as the features; 2) to identify compound common and specific skeletons; and 3) to infer consensus features for QSAR models.ResultsWe evaluated our methods and new strategies on building QSAR models of human acetylcholinesterase (huAChE). The leave-one-out cross validation values q 2 and r 2 of our huAChE QSAR model are 0.82 and 0.78, respectively. The experimental results show that the selected features (resides/atoms) are important for enzymatic functions and stabling the protein structure by forming key interactions (e.g., stack forces and hydrogen bonds) between huAChE and its inhibitors. Finally, we applied our methods to arthrobacter globiformis histamine oxidase (AGHO) which is correlated to heart failure and diabetic.ConclusionsBased on our AGHO QSAR model, we identified a new substrate verified by bioassay experiments for AGHO. These results show that our methods and new strategies can yield stable and high accuracy QSAR models. We believe that our methods and strategies are useful for discovering new leads and guiding lead optimization in drug discovery.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3503-2) contains supplementary material, which is available to authorized users.

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