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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
Copyright: 2014 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In 2009, AutoDock Vina [5] was released. As the successor of AutoDock 4 [4], AutoDock Vina significantly improves the average accuracy of the binding mode predictions while running two orders of magnitude faster with multithreading [5]. It was compared to AutoDock 4 on selecting active compounds against HIV protease, and was recommended for docking large molecules [10]. Its functionality of semi-flexible protein docking by enabling flexibility of side-chain residues was evaluated on VEGFR-2 [11]. To further facilitate the usage of AutoDock Vina, auxiliary tools were subsequently developed, including a PyMOL [12] plugin for program settings and visualization [13], a bootable operating system for computer clusters [14], a console application for virtual screening on Windows [15], and a GUI for virtual screening on Windows [16].
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. When benchmarked on docking 10,928 drug-like ligands against HIV reverse transcriptase, idock 1.0 achieved a speedup of 3.3 in terms of CPU time and a speedup of 7.5 in terms of elapsed time on average compared to AutoDock Vina, making idock one of the fastest docking software.
Having released idock, we kept receiving docking requests from our colleagues and collaborators. They are mostly biochemists and pharmacologists, outsourcing the docking research to us after discovering pharmaceutical protein targets for certain diseases of therapeutic interest. Consequently, we had to grab the protein structure, do format conversion, define search space, set up docking parameters, and keep running idock in batch for months. Tedious enough, all the above work was done manually, resulting in very low research productivity. In order to automate large-scale protein-ligand docking using our idock, we have therefore developed a web platform called istar.
The input to idock includes a rigid receptor, a set of flexible ligands, and a cubic box, which is used to restrict the conformational space to a particular binding site of the receptor. The output from idock includes predicted conformations and their predicted binding affinity.
The conformation-independent part penalizes for ligand flexibility. The predicted free energy of the th conformation for output, denoted as , is calculated from equation (10) where is the subscript for conformation, is the conformation-dependent score of the th conformation calculated from equation (1), is the of the first, i.e. lowest-scoring conformation, is the number of active torsions and is the number of inactive torsions of the ligand. Note that , rather than , is subtracted in order to preserve the ranking.(10)
On the other hand, in order to fast evaluate , idock precalculates all its possible values by building grid maps. A grid map of atom type t is constructed by placing virtual probe atoms of atom type t along the X, Y, Z dimensions of the search box at a certain granularity. The value of these probe atoms are precalculated from equation (2). Subsequently, given a sampled conformation, idock approximates the true values of of ligand heavy atoms by table lookup rather than linear interpolation as used in AutoDock Vina. In fact, when we profiled AutoDock Vina, its linear interpolation of the 8 nearest corner probe atoms turned out to be a performance bottleneck because it involves 8 readings, 12 subtractions, 24 multiplications, and 7 additions. The grid granularity is hard-coded to be a coarse value of 0.375 in AutoDock Vina, while in idock it is exposed as a program option for users to adjust accordingly and has a default fine value of 0.15625.
Likewise in AutoDock Vina, idock also uses Broyden-Fletcher-Goldfarb-Shanno (BFGS) [34] Quasi-Newton method for local optimization. In each BFGS iteration, a conformational mutation and a line search are taken, with each sampled conformation being accepted according to the Metropolis criterion. The number of iterations correlates to the complexity of the ligand regarding number of heavy atoms and number of torsions. BFGS approximates the inverse Hessian matrix, i.e. it uses not only the value of the scoring function but also its gradient, which are the derivatives of the scoring function with respect to the position, orientation and torsions of the ligand. Although both programs share similar optimization algorithms, their internal implementations differ. In idock, the BFGS local optimization stops if and only if no appropriate step length can be obtained by line search, thus increasing the probability of finding optimal local minimums. More optimization runs with fewer number of BFGS iterations are executed, better balancing high conformational diversity and short execution time.
RF-Score [31] is a member of a new class of scoring functions that use non-parametric machine learning approach to predict binding affinity in an entirely data-driven manner. RF-Score has been rigorously shown [31], [35] to perform better than 16 classical scoring functions in ranking protein-ligand complexes according to predicted binding affinity. It has also been shown to be useful in the discovery of new molecular scaffolds in antibacterial hit identification [36].
RF-Score is the first application of Random Forests [37] to predicting protein-ligand binding affinity. In RF-Score, each feature comprises the number of occurrences of a particular protein-ligand atom type pair interacting within a certain distance range. Four common atom types for the protein (i.e. C,N,O,S) and nine common atom types for the ligand (i.e. C,N,O,F,P,S,Cl,Br,I) constitute a vector of 36 features, and the distance cutoff is chosen to be as sufficiently large as 12 to implicitly capture solvation effects.
Equation (14) transforms the predicted free energy output by idock in into binding affinity in unit. The consensus score is thus defined in equation (15) so that it directly reflects the predicted potency in unit.(15)
Figure S1 shows the overall architecture of istar. On our istar website, the first section displays summary of existing jobs and the second section allows new job submission. A job comprises compulsory fields and optional fields. Compulsory fields include a receptor in PDB format, a search space defined by a cubic box, a brief description about the job, and an email to receive completion notification. Optional fields include nine ligand filtering conditions. The nine ligand filtering conditions are molecular weight, partition coefficient xlogP, apolar desolvation, polar desolvation, number of hydrogen bond donors, number of hydrogen bond acceptors, topological polar surface area tPSA, net charge, and number of rotatable bonds. These nine molecular descriptors are directly retrieved from our data source, i.e. the ZINC database [20], [21], in which the nine descriptors are already precalculated. Note that although molecular mass in Dalton unit may be a more appropriate descriptor than molecular weight in g/mol unit, we stick to the latter in order to maintain consistency with ZINC, in which the g/mol unit is used for molecular weight.
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