(Ekins et al. 2008b; Reschly and Krasowski 2006; Reschly et al. 2007, 2008a, 2008b), we recently generated a large volume of experi- mental data for classes of steroidal compounds (Ekins et al. 2008b) and used it to evaluate various modeling approaches such as Bayesian classification modeling with 2-dimensional (2D) fingerprints, various QSAR approaches, and molecular docking into the available hPXR crystal structures (Ekins et al. 2009). Docking coupled with hybrid scoring 5D-QSAR meth- ods performed significantly better than other QSAR methods in identifying agonists among these steroidal ligands (Ekins et al. 2009). With a promiscuous protein such as PXR, it is prob- ably important to have global models or meth- ods that can make predictions for a structurally diverse array of molecules rather than for a nar- row structural series. In previous studies we used structure- based docking, employing FlexX (BioSolveIT GmbH, Sankt Augustin, Germany) com- bined with logistic regression (Khandelwal et al. 2008), and GoldScore (Cambridge Crystallographic Data Centre, Cambridge, UK) combined with other descriptors as a weighting factor (Kortagere et al. 2009). Both FlexX and GOLD had mixed success in pre- dicting a large set of structurally diverse hPXR agonists (Khandelwal et al. 2008; Kortagere et al. 2009), possibly because of the size and flexibility of the LBP, as described above (Ekins et al. 2009). T o x C a s t r e p r e s e n t s a m a j o r U . S . Environmental Protection Agency (U.S. EPA) initiative for prioritizing the timely toxicity testing of large numbers of pesticides and other industrial chemicals (Dix et al. 2007; Houck et al. 2009; Judson et al. 2009, 2010; Knight et al. 2009) that may indicate various toxic- ity end points. In this study we initially used docking and scoring approaches to classify the hPXR agonist activity of these ToxCast compounds and prioritized them for in vitro screening prior to release of U.S. EPA experi- mental data. Our aim was to select a small sub- set of the compounds for testing to show that we could readily identify PXR agonists and PXR nonagonists without the need for screen- ing all the compounds in vitro. We have also used the ToxCast data set to further evaluate whether docking coupled with a hybrid scor- ing scheme was useful as a predictive method for PXR, especially when screening large data sets of molecules. Although most ToxCast compounds are pesticides or other industrial chemicals, in this case there may be some over- lap with the chemical space of pharmaceuti- cals, making this data set of interest for general PXR-agonist prediction. In addition, for com- parison and to illustrate the difficulties of using local ligand-based models, we used a recently generated Bayesian model with 115 steroidal PXR agonists and nonagonists (Ekins et al. Environmental Health Perspectives • volume 118 | number 10 | October 2010 Computational discovery of new PXR agonists 2009) to classify the ToxCast compounds. While the present study was in progress, the ToxCast initiative generated data on all the compounds from the NIH (National Institutes of Health) Chemical Genomics Center (NCGC) (Judson et al. 2010). Therefore, we evaluated all available ToxCast hPXR in vitro data for these compounds using these compu- tational methods to make predictions. Materials and Methods Materials. We purchased the DPX-2 cell line and the corresponding dosing and culturing medium from Puracyp Inc. (Carlsbad, CA). The creation of a HepG2 (human liver) cell line stably expressing the human Na+-taurocholate cotransporter (NTCP) has been previously reported and described in detail (Krasowski et al. 2005b). For tissue culture, we obtained BD Falcon Petri dishes from BD Biosciences (San Jose, CA); opaque treated sterile white 96-well assay plates with lids, and flat-bottom treated sterile (white with clear bottom) assay plates from Corning Inc. (Corning, NY). Disposable sterile pipette tips (low-adhesion tips) were purchased from BioTek (Winooski, VT), and the CellTiter-Fluor and Bright Glow assay system from Promega (Madison, WI). We used the Synergy Mx monochromator- based multimode microplate reader, EL406 Combination Washer Dispenser, and Precision XS microplate sample processor (BioTeK) for cell plating, drug dilution and plating, and reagent addition. Docking and scoring. We obtained chemi- cal structures of the ToxCast molecules from the U.S. EPA Distributed Structure- Searchable Toxicity Database Network web site (U.S. EPA 2009); these structures were docked into the five crystal structures of hPXR (1M13, 1NRL, 1SKX, 2O9I, and 2QNV) using the docking program GOLD (version 4) as described by Jones et al. (1997). GOLD uses a genetic algorithm to explore the various conformations of ligands and flex- ible receptor side chains in the LBP. We per- formed 20 independent docking runs for each ligand, and the complexes were scored using GoldScore. In all cases before now, the crystal structure ligand was removed, and hydro- gen atoms were added to the amino acids. All amino acids within 6 Å of the cocrystallized ligand were identified as the binding site. From the entire set of ToxCast molecules that were docked to all five crystal structures, we chose 13 high-scoring and 2 low-scoring compounds to form the sample set. In addi- tion, 13 compounds reported by Lemaire et al. (2006)—that docked to the five crystal structures and were scored using GoldScore— were added to our sample set. For all 28 com- pounds in the sample data set, we performed the GoldScore-based classification by choosing 80% of the GoldScore of the corresponding cocrystal ligand as a cutoff for whether a com- pound was an agonist (Table 1). The com- plexes were also scored using a hybrid scoring scheme, which was designed as GoldScore weighted with similarity scores (Kortagere et al. 2009). The similarity scores were based on 2D similarity (Sheridan et al. 2004; Willett 2003) encoded in MDL (Molecular Design Limited) fingerprint keys calculated using Discovery Studio 2.1 (Accelrys, San Diego, CA). The Tanimoto coefficient was used as the metric to compare the resulting molecular fingerprints. The coefficients varied between 0 (dissimilar) and 1 (similar) and were com- puted for all 28 compounds, with reference to the five cocrystal structure ligands (HYF, SRL, RFP, 444, and CDZ). Further, the weighted docking score of an active compound i with j conformations can be computed by Si,j = wisij, [1] where sij is the original GoldScore for the compound i in its jth conformation and wi is the similarity score for compound i with respect to the cocrystal structure ligand. For each of the 28 compounds, we calculated the average weighted score across all five crys- tal structures [see Supplemental Material, Table 1 (doi:10.1289/ehp.1001930)], and this score (average Sij ~ 15) was used as a cutoff for the classification. The GoldScores of the compounds and their similarity and weighted scores and classification are listed in Supplemental Material, Table 1. The con- sensus classification prediction was based on the majority vote across the five structures for each molecule. We classified complexes of all compounds from the ToxCast data- base with the five crystal structures using the GoldScore and hybrid score as described for the sample data set. We computed an average PXR structure (PDB code) 1M13 1NRL 1SKX 2O9I 2QNV Table 1. GoldScores for the PXR cocrystallized ligands and their corresponding cutoff scores used for classification of a sample data set and all ToxCast compounds using GoldScore and 2D similarity- weighted hybrid score. Cocrystallized ligand PDB code GoldScore Cutoff score for sample data set GoldScore Hybrid score Cutoff score for ToxCast data set GoldScore Hybrid score HYF SRL RFP 444 CDZ 82.06 48.31 65.44 48.69 55.19 66 39 52 39 44 15 15 15 15 15 51 46 48 46 44 13 14 15 13 11 1413