idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach

Jui-Chih Wang 2 Pei-Ying Chu 1 Chung-Ming Chen 2 Jung-Hsin Lin 0 1 3 0 School of Pharmacy, National Taiwan University 1 Division of Mechanics, Research Center for Applied Sciences , Academia Sinica 2 Institute of Biomedical Engineering, National Taiwan University 3 Institute of Biomedical Science , Academia Sinica, Taipei, Taiwan Identification of possible protein targets of small chemical molecules is an important step for unravelling their underlying causes of actions at the molecular level. To this end, we construct a web server, idTarget, which can predict possible binding targets of a small chemical molecule via a divide-andconquer docking approach, in combination with our recently developed scoring functions based on robust regression analysis and quantum chemical charge models. Affinity profiles of the protein targets are used to provide the confidence levels of prediction. The divide-and-conquer docking approach uses adaptively constructed small overlapping grids to constrain the searching space, thereby achieving better docking efficiency. Unlike previous approaches that screen against a specific class of targets or a limited number of targets, idTarget screen against nearly all protein structures deposited in the Protein Data Bank (PDB). We show that idTarget is able to reproduce known off-targets of drugs or drug-like compounds, and the suggested new targets could be prioritized for further investigation. idTarget is freely available as a web-based server at http://idtarget.rcas.sinica.edu.tw. - Identification of targets of small chemical molecules is essential for unravelling the underlying molecular causes of actions. Often, natural products, i.e. compounds discovered from plants, animals, marine lives or other living organism, exhibit useful pharmaceutical effects, e.g. anti-inflammatory, anti-cancer and anti-viral effects, yet their molecular mechanisms remain elusive. On the other hand, many drugs are known to be accompanied with unpleasant adverse effects, but the molecular targets of such effects are largely unknown. On the contrary, there are also some old drugs whose additional beneficiary effects are discovered only recently. For example, the epigenetic mechanism of the anticancer effect of cholesterol-lowering drugs, statins, was uncovered rather recently (1). Conventional virtual screening of chemical libraries has been used widely to search for new leads in drug development for a protein target (2). As the deposited structures of biomolecules in the Protein Data Bank (PDB) increase substantially in the past decades, searching for the targets of a given drug or small compounds (also known as inverse screening, target fishing, off-target prediction, etc.) has become a useful approach (37). One of the major hurdles for target identification is the effectiveness of scoring functions (7,8). To evaluate the binding affinity of the small ligand and a protein target, an accurate yet generally applicable scoring function is essential. We recently developed three robust scoring functions, AutoDock4RRP, AutoDock4RAP and AutoDock4RGG (9) based on the energetic terms and the formulation of AutoDock4 (10). These scoring functions report the binding free energy in the experimental scale, which allows direct comparison among different protein ligand systems. Two of these three robust scoring functions were constructed using atomic charges from quantum chemical calculations, namely, RESP (11) and AM1-BCC (12), and the robust regression analysis (13) was employed to mitigate the influence of outliers for the calibration of the scoring functions. These robust AutoDock4 scoring functions have been benchmarked for their capability in binding affinity prediction and binding pose prediction (9). For the assessment of binding affinity prediction with a large external set of 1427 complexes from PDBbind v2009, AutoDock4RAP obtained root-mean-square errors of 2.176 kcal/mol, while the size of the training set is only 147. Benchmarked by using two decoy sets (14,15), the robust AutoDock4 scoring functions outperformed most of other scoring functions for the binding pose prediction (9). Here, we utilize an efficient docking approach to screen the protein targets. Evaluation of potential targets is carried out by using the AutoDock4 robust scoring functions and the affinity profile analysis to enhance the confidence level of prediction. MATERIALS AND METHODS Docking and scoring The search engine of idTarget web server is MEDock (16), which generates initial docking poses of the small ligand. The global search algorithm used in MEDock has also been tested recently by random mathematical functions simulating rugged free energy landscapes with different dimensionalities (17). It was shown that this global search algorithm maintained very high searching efficiency even at the dimensionality of 30 (17), which should be sufficient for applying to most proteinligand systems. It was also shown that the tradition (...truncated)