Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

Abstract

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches.

Identification and Validation of Novel Human Pregnane X Receptor Activators among Prescribed Drugs via Ligand-Based Virtual Screening

“…The integrated use of docking and structureactivity relationship models to determine ligand, substrate, or inhibitor specificity has greatly advanced our understanding of the mechanism of receptors and drug transporters (Khandelwal et al, 2008;Krueger et al, 2009). To date, several combinations of structure-and/or ligand-based methods have been reported to characterize hPXR and activator interactions (Gao et al, 2007;Ekins et al, 2008;Khandelwal et al, 2008;Yasuda et al, 2008), but application of this strategy to Previous docking studies on hPXR indicated that directly using the "cutoff score" from docking programs was limited for prediction and classification of hPXR activators and nonactivators (Ekins et al, , 2009Khandelwal et al, 2008;Yasuda et al, 2008). To overcome this limitation, our present work applied Bayesian models for identification of hPXR activators.…”

“…Several key amino acid residues are responsible for the high flexibility of its binding site that is critical for recognizing promiscuous ligands of various dimensions and chemical properties (Ekins et al, 2009). Probably because of the flexibility of the hPXR LBD and the limitation of docking algorithms, docking of structurally diverse molecules is a challenge (Ekins et al, , 2009Khandelwal et al, 2008;Yasuda et al, 2008). Therefore, docking methods have been suggested for use in combination with other computational methods to improve prediction (Khandelwal et al, 2008;Yasuda et al, 2008;Ekins et al, 2009).…”

“…Probably because of the flexibility of the hPXR LBD and the limitation of docking algorithms, docking of structurally diverse molecules is a challenge (Ekins et al, , 2009Khandelwal et al, 2008;Yasuda et al, 2008). Therefore, docking methods have been suggested for use in combination with other computational methods to improve prediction (Khandelwal et al, 2008;Yasuda et al, 2008;Ekins et al, 2009). The flexibility and large size of the hPXR LBD necessitates development of multiple pharmacophores for consensus prediction by considering interactions between a ligand and various binding sites in protein (Yasuda et al, 2008).…”

“…The flexibility and large size of the hPXR LBD necessitates development of multiple pharmacophores for consensus prediction by considering interactions between a ligand and various binding sites in protein (Yasuda et al, 2008). In a recent study, ligand-based structure-activity relationship approaches, such as machine learning methods (Khandelwal et al, 2008) and Bayesian statistics (Ekins et al, 2009;Zientek et al, 2010), have been applied to generate models by using just binary classification of ligands (e.g., activator and nonactivator) instead of quantitative data while using two-dimensional instead of three-dimensional descriptors.…”

Pregnane X receptor‐ and CYP3A4‐humanized mouse models and their applications

“…Alb, albumin promoter; AUC, area under the curve; CAR, constitutive androstane receptor; CYP3A4, cytochrome P450-3A4; CYP3A4-A, mice with albumin promoter-targeted liver-specific expression of CYP3A4; CYP3A4-humanized mice, CYP3A4-humanized mice lacking of mouse Cyp3a; CYP3A4-V, mice with villin promoter-targeted intestinespecific expression of CYP3A4; Cyp3a-null, Cyp3a knockout mice; DBD, DNA-binding domain; FABP, fatty acid-binding protein; FXR, farnesoid X receptor; hPXR mice, PXR-humanized mice; IBD, inflammatory bowel disease; LBD, ligandbinding domain; LCA, lithocholic acid; LXR, liver X receptor; PPAR, peroxisome proliferator-activated receptor; PXR, pregnane X receptor; Pxr-null, Pxr knockout mice; RXR, retinoid X receptor; Tg3A4, transgenic CYP3A4 (CYP3A7) mice with the mouse Cyp3a background; Tg3A4/hPXR, CYP3A4 and human PXR double transgenic mice lacking of mouse PXR Human pregnane X receptor (PXR), encoded by the nuclear receptor subfamily 1, group I, member 2 (NR1I2) gene, is located on chromosome 3q12-q13.3 and consists of nine exons; exons 2 to 9 contain the coding region for a 434 amino acid protein (Ekins et al, 2009). Similar to other nuclear receptors, PXR possesses the common modulator structure of a conserved N-terminal DNA-binding domain (DBD) and C-terminal ligand-binding domain (LBD).…”