DrugQuery is a human proteome-scale docking and target prediction tool for small molecules. User submitted molecules are docked against the entire structural proteome. Docking score and additional structural filters are used to rank targets by predicted affinity and the ranked results are returned to the user.
Users submit their email address and a small molecule in any 2D or 3D structure file format recognized by Open Babel. Results from previous submissions are stored on the server such that the results can be returned instantaneously if the compound has been submitted before.
Molecular docking is performed with smina, a fork of Autodock Vina that focuses on improving scoring and minimization. User-submitted small molecules are docked to predicted ‘hot-spots’ on each protein in the DrugQuery database, which indicate where small molecules are most likely to bind to each protein. These ‘hot-spots’ are pre-computed using FTMap to achieve docking efficiency and accuracy above that of global docking.
The DrugQuery target database is a curated set of human protein crystal structures from the PDB and currently contains structural models for over 1000 human genes. Multiple structures are used in docking for multi-domain proteins and proteins that exhibit significant flexibility and conformational sampling.
After docking results have been compiled, users receive an email with a link to the output results page. Standard DrugQuery output consists of (1) a list of protein targets ranked by the top docking score of the input compound against any of the targets’ ‘hot-spots,’ and (2) a comprehensive, un-ranked log of all docking scores for the input compound against each ‘hot-spot’ on each target in the DrugQuery database. Using an interactive summary table on the results page, users can also elect to download structural docking models for their compound bound to any targets in the database.
DrugQuery was designed and built by Nicolas A. Pabon with the support of the Camacho Lab at the University of Pittsburgh School of Medicine, Department of Computational and Systems Biology.
To cite DrugQuery, please use:
This work was supported in part by the U.S. National Institute of Health (grant no. R01GM097082) and by the U.S. National Science Foundation (grant no. 1247842).
For support, please email Nicolas Pabon at npabon@pitt.edu.
DrugQuery