DockCoV2
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 the spike protein priming. Thus in order to speed up the discovery of therapeutic agents, we develop DockCoV2, a drug database for SARS-CoV2. 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 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 validation information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV.
Figure. 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
Dependencies
Here is the dependency list for running the proposed pipeline in DockCoV2. Due to license issue, please download all of the 3rd-party packages for your own. For the docker user, please refer the Dockerfile in this repo to setup the environment.
Usage Example
python path_to/FindDock.py
-r path_to/receptor.pdb \
-l path_to/ligand_list.txt \
-o path/output_folder \
-d path_to/dowenload_sdf.py \
-b path_to/bin/obabel \
-a path_to/AutoDockTools/ \
-v path_to/bin/vina
The content in ligand list can be multipe drugs in interest, and one drug per line. For example:
Dactinomycin
Irinotecan
Gramicidin
For checking all the optional arguments, please use --help:
python path_to/FindDock.py -h
You will obtain the following argument list:
usage: FindDock [-h] -r R [-s S] (-l L | -k K) -o O [-n N] [-t T] -d D -b B -a A -v V
FindDock is a batch AutoDock Vina runner for the candidate drugs or a keyword developed by Yu-Chuan (Chester) Chang & all member of the Genomics Team at AILabs in Taiwan.
optional arguments:
-h, --help show this help message and exit
-r R the filename of receptor's .pdb file
-s S the filename of the active site list
-l L the filename of the ligand list
-k K the filename of the keyword
-o O the output filepath
-n N the number of replicates
-t T the number of threads
-d D the path of the script for downloading
-b B the path of openbabel
-a A the path of autodock tool
-v V the path of autodock vina
Citing
Please considering cite the following paper if you use DockCoV2 in a scientific publication:
[1] Ting-Fu Chen†, Yu-Chuan Chang†, Yi Hsiao†, Ko-Han Lee†, Yu-Chun Hsiao, Yu-Hsiang Lin, Yi-Chin Ethan Tu, Hsuan-Cheng Huang, Chien-Yu Chen*, Hsueh-Fen Juan*., DockCoV2: a drug database against SARS-CoV-2, Nucleic Acids Research (2020), gkaa861, https://doi.org/10.1093/nar/gkaa861