DockCoV2: a drug database against SARS-CoV-2

Abstract

The current state of the COVID-19 pandemic is a global health crisis. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus replication. Currently, we know that the SARS-CoV-2 virus encodes about 29 proteins such as spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papain-like protease (PLpro), and nucleocapsid (N) protein. SARS-CoV-2 uses human angiotensin-converting enzyme 2 (ACE2) for viral entry and transmembrane serine protease family member II (TMPRSS2) for spike protein priming. Thus in order to speed up the discovery of potential drugs, we develop DockCoV2, a drug database for SARS-CoV-2. DockCoV2 focuses on predicting the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with the seven proteins mentioned above. This database contains a total of 3,109 drugs. DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. Users can download their drug-protein docking data of interest and examine additional drug-related information on DockCoV2. Furthermore, DockCoV2 provides experimental information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV. DockCoV2 is available at https://covirus.cc/drugs/.

Figure 1.

The overview of the database content. In addition to the docking scores, DockCoV2 designed a joint panel section to provide the following related information: docking structure, ligand information and experimental data.

Figure 2.

The virtual screening pipeline. We used AutoDock Vina as the core docking utility to reconstruct the virtual screening pipeline. In addition, the Kubernetes server and Redis data store were adopted for parallel computing. After finishing docking, the top 20 docking poses were taken to build a protein heatmap for visualization.

Figure 3.

The docking score distribution of each protein. The x-axis is the minimum docking score of each protein–ligand pair, and the y-axis is the probability density estimated by kernel density estimation.

Figure 4.

A demonstration of the bound structure of 3CLpro (PDB ID: 6LU7, where the ligand was colored in red) with molecular docking. The left-hand side shows the results of the top 20 docking poses (colored in blue). Eight out of 20 docking poses are located in the binding pocket of the protein. The right-hand side zooms in the binding pocket and shows that the top docking pose (colored in blue) is close to the bound ligand (colored in red).