Abstract
Protein-ligand docking is a key computational method in the design of starting points for the drug discovery process. We are motivated by the desire to automate large-scale docking using our popular docking engine idock and thus have developed a publicly-accessible web platform called istar. Without tedious software installation, users can submit jobs using our website. Our istar website supports 1) filtering ligands by desired molecular properties and previewing the number of ligands to dock, 2) monitoring job progress in real time, and 3) visualizing ligand conformations and outputting free energy and ligand efficiency predicted by idock, binding affinity predicted by RF-Score, putative hydrogen bonds, and supplier information for easy purchase, three useful features commonly lacked on other online docking platforms like DOCK Blaster or iScreen. We have collected 17,224,424 ligands from the All Clean subset of the ZINC database, and revamped our docking engine idock to version 2.0, further improving docking speed and accuracy, and integrating RF-Score as an alternative rescoring function. To compare idock 2.0 with the state-of-the-art AutoDock Vina 1.1.2, we have carried out a rescoring benchmark and a redocking benchmark on the 2,897 and 343 protein-ligand complexes of PDBbind v2012 refined set and CSAR NRC HiQ Set 24Sept2010 respectively, and an execution time benchmark on 12 diverse proteins and 3,000 ligands of different molecular weight. Results show that, under various scenarios, idock achieves comparable success rates while outperforming AutoDock Vina in terms of docking speed by at least 8.69 times and at most 37.51 times. When evaluated on the PDBbind v2012 core set, our istar platform combining with RF-Score manages to reproduce Pearson's correlation coefficient and Spearman's correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. istar is freely available at http://istar.cse.cuhk.edu.hk/idock.
Highlights
Protein-ligand docking predicts the preferred conformation and binding affinity of a small ligand as non-covalently bound to the specific binding site of a protein
In 2011, inspired by AutoDock Vina, we developed idock 1.0 [17], a multithreaded virtual screening tool for flexible ligand docking. idock introduces plenty of innovations, such as caching receptor and grid maps in memory to permit efficient large-scale docking, revised numerical model for much faster energy approximation, and capability of automatic detection of inactive torsions for dimensionality reduction
Since both AutoDock Vina and idock are trained on the PDBbind v2007 refined set (N = 1,300), in order to make a fair comparison, in this benchmark we have re-trained RF-Score on the same training set
Summary
Protein-ligand docking predicts the preferred conformation and binding affinity of a small ligand as non-covalently bound to the specific binding site of a protein. Docking can be used to determine whether a ligand binds, and to understand how it binds. The latter is subsequently important to improve the potency and selectivity of binding. There are hundreds of docking programs [1,2]. The AutoDock series [3,4,5] is the most cited docking software in the research community, with over 5,000 citations according to Google Scholar. Several parallel implementations were developed using either multithreading or computer cluster [7,8,9]
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