enrichment is not simply achieved by separating trivial chemical features. Both models successfully identified all known compounds embedded in the library. The quality of mapping was assessed by generating receiver operating characteristic (ROC) curves for each model (figure 3B), taking into consideration the ranking of fitness values of each virtual hit. The plots provide an objective, quantitative measure of whether a test discriminates between two populations. As can be seen from figure 3B, both models perform extremely well, generating almost a perfect curve. The difference in the curves highlights the difference in pharmacophore stringency. The stricter pharmacophore model 2 (which has an aromatic ring feature instead of a hydrophobic feature) performs best in identifying a large number of true positives while maintaining a low false positive rate. Thus, we used model 2 in the subsequent virtual screening experiments. Note that it is possible that some of the random molecules that were identified by the pharmacophore models, and received fitness values similar to known antagonists, may be potential hPKR binders. A list of these ZINC molecules is available in table S1. These compounds differ structurally from the known small-molecule hPKR antagonists because the maximal similarity score calculated using the Tanimoto coefficient, between them and the known antagonists, is 0.2626 (compounds that have Tanimoto coefficient values .0.85 are generally considered similar to each other). This analysis revealed that the ligand-based pharmacophore models can be used successfully in a VLS study and that they can identify completely different and novel scaffolds, which neverthe- less possess the required chemical features. hPKR1 as a potential off-target of known drugs Recent work by Keiser and colleagues [48] utilized a chemical similarity approach to predict new targets for established drugs. Interestingly, they showed that although drugs are intended to be selective, some of them do bind to several different targets, which can explain drug side effects and efficacy, and may suggest new indications for many drugs. Inspired by this work, we decided to explore the possibility that hPKRs can bind established drugs. Thus, we applied the virtual screening procedure to a dataset of molecules retrieved from the DrugBank database (release 2.0) [40]. The DrugBank database [40] combines detailed drug (chemical, pharmacological, and pharmaceutical) data with comprehensive drug target (sequence, structure, and pathway) information. It contains 4886 molecules, which include FDA-approved small- molecule drugs, experimental drugs, FDA-approved large-mole- cule (biotech) drugs and nutraceuticals. As a first step in the VLS procedure, the initial dataset was pre-filtered, prior to screening, according to the average molecular properties of known active compounds 6 4SD. The pre-filtered set consisted of 432 molecules that met these criteria. This set was then queried with the pharmacophore, using the ’ligand pharmacophore mapping’ module in DS2.5 (Accelrys, Inc.). A total of 124 hits were retrieved from the screening. Only those hits that had FitValues above a cutoff defined according to the pharmacophores’ enrichment curve, which identifies 100% of the known antago- nists, were further analyzed, to ensure that compatibility with the pharmacophore of the molecules selected is as good as for the known antagonists. This resulted in 10 hits with FitValues above the cutoff (see figure 4). These include 3 FDA-approved drugs and 7 experimental drugs. All these compounds target enzymes, identified by their EC numbers (corresponding to the chemical reactions they catalyze): most of the targets are peptidases (EC 3.4.11, 3.4.21 and 3.4.23), including aminopeptidases, serine proteases, and aspartic endopeptidases, and an additional single Small-Molecule Binders of Prokineticin Receptors compound targets a receptor protein-tyrosine kinase (EC 2.7.10). The fact that only two classes of enzymes were identified is quite striking, in particular, when taking into account that these two groups combined represent only 2.6% of the targets in the screened set. This may indicate the intrinsic ability of hPKRs to bind compounds originally intended for this set of targets. The calculated similarity between the known hPKR antagonists and the hits identified using the Tanimoto coefficients is shown in figure 4: the highest similarity score was 0.165563, indicating that the identified hits are dissimilar from the known hPKR antagonists, as was also observed for the ZINC hits (see Table S1). Interestingly, when calculating the structural similarity within the EC3.4 and 2.7.10 hits, the highest value is 0.679, indicating consistency in the ability to recognize structurally diverse compounds (see figure S4). To predict which residues in the receptor may interact with the key pharmacophores identified in the SAR analysis previously mentioned, and to assess whether the novel ligands harboring the essential pharmacophors fit into the binding site in the receptor, we carried out homology modeling and docking studies of the known and predicted ligands. Molecular Modeling of hPKR1 predicts the small- molecule binding site in the typical TM-bundle site of Family A GPCRs As a first step in analyzing small-molecule binding to hPKRs, we generated homology models of the two subtypes, hPKR1 and hPKR2. The models were built using the I-Tasser server [29]. These multiple-template models are based on X-ray structures of bovine Rhodopsin (PDB codes: 1L9H) [49], the human b2- adrenergic receptor (2RH1) [50], and the human A2A-adenosine receptor (3EML) [51]. The overall sequence identity shared between the PKR subtypes and each of the three templates is approximately 20%. Although this value is quite low, it is similar to cases in which modeling has been applied, and it satisfactorily recaptured the binding site and binding modes [52]. Furthermore, the sequence alignment of hPKRs and the three template receptors are in good agreement with known structural features of GPCRs (figure S1). Namely, all TM residues known to be highly conserved in family A GPCRs [33] (N1.50, D2.50, R3.50, W4.50, P5.50, P6.50) are properly aligned. The only exception is the NP7.50xxY motif in TM7, which aligns to NT7.50LCF in hPKR1. The initial crude homology model of hPKR1, obtained from I- TASSER, was further refined by energy minimization and side chain optimization. Figure 5 shows the general topology of the refined hPKR1 model. This model exhibits the major character- istics of family A GPCRs, including conservation of all key residues, and a palmitoylated cysteine in the C terminal tail, which forms a putative fourth intracellular loop. Also, similarly to family A GPCR X-ray structures, a conserved disulfide bridge connects the second extracellular loop (ECL2) with the extracellular end of TM3, formed between Cys217 and Cys137, respectively. Howev- er, both extracellular and intracellular loops are not very likely to be modeled correctly, due to their low sequence similarity with the template structures, and the fact that loop configurations are highly variable among GPCR crystal structures [42]. The emerging consensus in the field is that these models perform better in docking and virtual screening with no modeled loops at all than with badly modeled loops [43,44,45]. We therefore did not include the extracellular and intracellular loops in the subsequent analysis. Overall, our hPKR1 model has good conservation of key features shared among family A GPCR members. Conservation of this fold led us to hypothesize that hPKRs possess a 7TM-bundle PLoS ONE | www.plosone.org 7 November 2011 | Volume 6 | Issue 11 | e27990