[Microorganism Immobilization Device Using Artificial Siderophores].

Inspired by the mechanism by which microorganisms utilize siderophores to ingest iron, four different FeIII complexes of typical artificial siderophore ligands containing catecholate and/or hydroxamate groups, K3[FeIII-LC3], K2[FeIII-LC2H1], K[FeIII-LC1H2], and [FeIII-LH3], were prepared. They were modified on an Au substrate surface (Fe-L/Au) and applied as microorganism immobilization devices for fast, sensitive, selective detection of microorganisms, where H6LC3, H5LC2H1, H4LC1H2, and H3LH3 denote the tri-catecholate, biscatecholate-monohydroxamate, monocatecholate-bishydroxamate, and tri-hydroxamate type of artificial siderophores, respectively. Their adsorption properties for the several microorganisms were investigated using scanning electron microscopy (SEM), quartz crystal microbalance (QCM), and electric impedance spectroscopy (EIS) methods. The artificial siderophore-iron complexes modified on the Au substrates Fe-LC3/Au, Fe-LC2H1/Au, Fe-LC1H2/Au, and Fe-LH3/Au showed specific microorganism immobilization behavior with selectivity based on the structure of the artificial siderophores. Their specificities corresponded well with the structural characteristics of natural siderophores that microorganisms release from the cell and/or use to take up an iron. These findings suggest that release and uptake are achieved through specific interactions between the artificial siderophore-FeIII complexes and receptors on the cell surfaces of microorganisms. This study revealed that Fe-L/Au systems have specific potential to serve as effective immobilization probes of microorganisms for rapid, selective detection and identification of a variety of microorganisms.

Thermostability-assisted limited proteolysis-coupled mass spectrometry for capturing drug target proteins and sites.

BACKGROUND: Identifying drug-binding targets and their corresponding sites is crucial for drug discovery and mechanism studies. Limited proteolysis-coupled mass spectrometry (LiP-MS) is a sophisticated method used for the detection of compound and protein interactions. However, in some cases, LiP-MS cannot identify the target proteins due to the small structure changes or the lack of enrichment of low-abundant protein. To overcome this drawback, we developed a thermostability-assisted limited proteolysis-coupled mass spectrometry (TALiP-MS) approach for efficient drug target discovery. RESULTS: We proved that the novel strategy, TALiP-MS, could efficiently identify target proteins of various ligands, including cyclosporin A (a calcineurin inhibitor), geldanamycin (an HSP90 inhibitor), and staurosporine (a kinase inhibitor), with accurately recognizing drug-binding domains. The TALiP protocol increased the number of target peptides detected in LiP-MS experiments by 2- to 8-fold. Meanwhile, the TALiP-MS approach can not only identify both ligand-binding stability and destabilization proteins but also shows high complementarity with the thermal proteome profiling (TPP) and machine learning-based limited proteolysis (LiP-Quant) methods. The developed TALiP-MS approach was applied to identify the target proteins of celastrol (CEL), a natural product known for its strong antioxidant and anti-cancer angiogenesis effect. Among them, four proteins, MTHFD1, UBA1, ACLY, and SND1 were further validated for their strong affinity to CEL by using cellular thermal shift assay. Additionally, the destabilized proteins induced by CEL such as TAGLN2 and CFL1 were also validated. SIGNIFICANCE: Collectively, these findings underscore the efficacy of the TALiP-MS method for identifying drug targets, elucidating binding sites, and even detecting drug-induced conformational changes in target proteins in complex proteomes.

Rational Design of Drugs Targeting G-Protein-Coupled Receptors: A Structural Biology Perspective.

G protein-coupled receptors (GPCRs) play a key role in the transduction of extracellular signals to cells and regulation of many biological processes, which makes these membrane proteins one of the most important targets for pharmacological agents. A significant increase in the number of resolved atomic structures of GPCRs has opened the possibility of developing pharmaceuticals targeting these receptors via structure-based drug design (SBDD). SBDD employs information on the structure of receptor-ligand complexes to search for selective ligands without the need for an extensive high-throughput experimental ligand screening and can significantly expand the chemical space for ligand search. In this review, we describe the process of deciphering GPCR structures using X-ray diffraction analysis and cryoelectron microscopy as an important stage in the rational design of drugs targeting this receptor class. Our main goal was to present modern developments and key features of experimental methods used in SBDD of GPCR-targeting agents to a wide range of specialists.

Highly biased agonism for GPCR ligands via nanobody tethering.

Ligand-induced activation of G protein-coupled receptors (GPCRs) can initiate signaling through multiple distinct pathways with differing biological and physiological outcomes. There is intense interest in understanding how variation in GPCR ligand structure can be used to promote pathway selective signaling ("biased agonism") with the goal of promoting desirable responses and avoiding deleterious side effects. Here we present an approach in which a conventional peptide ligand for the type 1 parathyroid hormone receptor (PTHR1) is converted from an agonist which induces signaling through all relevant pathways to a compound that is highly selective for a single pathway. This is achieved not through variation in the core structure of the agonist, but rather by linking it to a nanobody tethering agent that binds with high affinity to a separate site on the receptor not involved in signal transduction. The resulting conjugate represents the most biased agonist of PTHR1 reported to date. This approach holds promise for facile generation of pathway selective ligands for other GPCRs.

Asymmetric allostery in estrogen receptor-α homodimers drives responses to the ensemble of estrogens in the hormonal milieu.

The estrogen receptor-α (ER) is thought to function only as a homodimer but responds to a variety of environmental, metazoan, and therapeutic estrogens at subsaturating doses, supporting binding mixtures of ligands as well as dimers that are only partially occupied. Here, we present a series of flexible ER ligands that bind to receptor dimers with individual ligand poses favoring distinct receptor conformations-receptor conformational heterodimers-mimicking the binding of two different ligands. Molecular dynamics simulations showed that the pairs of different ligand poses changed the correlated motion across the dimer interface to generate asymmetric communication between the dimer interface, the ligands, and the surface binding sites for epigenetic regulatory proteins. By examining the binding of the same ligand in crystal structures of ER in the agonist vs. antagonist conformers, we also showed that these allosteric signals are bidirectional. The receptor conformer can drive different ligand binding modes to support agonist vs. antagonist activity profiles, a revision of ligand binding theory that has focused on unidirectional signaling from the ligand to the coregulator binding site. We also observed differences in the allosteric signals between ligand and coregulator binding sites in the monomeric vs. dimeric receptor, and when bound by two different ligands, states that are physiologically relevant. Thus, ER conformational heterodimers integrate two different ligand-regulated activity profiles, representing different modes for ligand-dependent regulation of ER activity.

Atomistic description of the OCTN1 recognition mechanism via in silico methods.

The Organic Cation Transporter Novel 1 (OCTN1), also known as SLC22A4, is widely expressed in various human tissues, and involved in numerous physiological and pathological processes remains. It facilitates the transport of organic cations, zwitterions, with selectivity for positively charged solutes. Ergothioneine, an antioxidant compound, and acetylcholine (Ach) are among its substrates. Given the lack of experimentally solved structures of this protein, this study aimed at generating a reliable 3D model of OCTN1 to shed light on its substrate-binding preferences and the role of sodium in substrate recognition and transport. A chimeric model was built by grafting the large extracellular loop 1 (EL1) from an AlphaFold-generated model onto a homology model. Molecular dynamics simulations revealed domain-specific mobility, with EL1 exhibiting the highest impact on overall stability. Molecular docking simulations identified cytarabine and verapamil as highest affinity ligands, consistent with their known inhibitory effects on OCTN1. Furthermore, MM/GBSA analysis allowed the categorization of substrates into weak, good, and strong binders, with molecular weight strongly correlating with binding affinity to the recognition site. Key recognition residues, including Tyr211, Glu381, and Arg469, were identified through interaction analysis. Ach demonstrated a low interaction energy, supporting the hypothesis of its one-directional transport towards to outside of the membrane. Regarding the role of sodium, our model suggested the involvement of Glu381 in sodium binding. Molecular dynamics simulations of systems at increasing levels of Na+ concentrations revealed increased sodium occupancy around Glu381, supporting experimental data associating Na+ concentration to molecule transport. In conclusion, this study provides valuable insights into the 3D structure of OCTN1, its substrate-binding preferences, and the role of sodium in the recognition. These findings contribute to the understanding of OCTN1 involvement in various physiological and pathological processes and may have implications for drug development and disease management.

In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations.

AIM: This study aimed to comprehensively assess the biocompatibility and toxicity profiles of poly(methyl methacrylate) (PMMA) and its monomeric unit, methyl methacrylate (MMA), crucial components in dental materials for interim prosthetic restorations. METHODOLOGY: Molecular docking was employed to predict the binding affinities, energetics, and steric features of MMA and PMMA with selected receptors involved in bone metabolism and tissue development, including RANKL, Fibronectin, BMP9, NOTCH2, and other related receptors. The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. Furthermore, molecular dynamics (MD) simulations over 100 nanoseconds were conducted using the GROMACS package to evaluate dynamic actions and structural stability. The LigandScout was utilized for pharmacophore modeling, which employs a shape-based screening approach to identify potential ligand binding sites on protein targets. RESULTS: The molecular docking studies elucidated promising interactions between PMMA and MMA with key biomolecular targets relevant to dental applications. MD simulation results provided strong evidence supporting the structural stability of PMMA complexes over time. Pharmacophore modeling highlighted the significance of carbonyl and hydroxyl groups as pharmacophoric features, indicating compounds with favorable biocompatibility profiles. CONCLUSION: This study underscores the potential of PMMA in dental applications, emphasizing its structural stability, molecular interactions, and safety considerations. These findings lay a foundation for future advancements in dental biomaterials, guiding the design and optimization of materials for enhanced biocompatibility. Future directions include experimental validation of computational findings and the development of PMMA-based dental materials with improved biocompatibility and clinical performance.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics.

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Chemogenetic activation of mammalian brain neurons expressing insect Ionotropic Receptors by systemic ligand precursor administration.

Chemogenetic approaches employing ligand-gated ion channels are advantageous regarding manipulation of target neuronal population functions independently of endogenous second messenger pathways. Among them, Ionotropic Receptor (IR)-mediated neuronal activation (IRNA) allows stimulation of mammalian neurons that heterologously express members of the insect chemosensory IR repertoire in response to their cognate ligands. In the original protocol, phenylacetic acid, a ligand of the IR84a/IR8a complex, was locally injected into a brain region due to its low permeability of the blood-brain barrier. To circumvent this invasive injection, we sought to develop a strategy of peripheral administration with a precursor of phenylacetic acid, phenylacetic acid methyl ester, which is efficiently transferred into the brain and converted to the mature ligand by endogenous esterase activities. This strategy was validated by electrophysiological, biochemical, brain-imaging, and behavioral analyses, demonstrating high utility of systemic IRNA technology in the remote activation of target neurons in the brain.

Synthesis, characterization, biological potency, and molecular docking of Co2+, Ni2+ and Cu2+ complexes of a benzoyl isothiocyanate based ligand.

The primary objective of the present study was to produce metal complexes of H4DAP ligand (N,N'-((pyridine-2,6-diylbis(azanediyl))bis(carbonothioyl))dibenzamide) derived from 2,6-diaminopyridine and benzoyl isothiocyanate with either ML or M2L stoichiometry. There are three distinct coordination complexes obtained with the formulas [Co(H2DAP)]·H2O, [Ni2(H2DAP)Cl2(H2O)2]·H2O, and [Cu(H4DAP)Cl2]·3H2O. The confirmation of the structures of all derivatives was achieved through the utilization of several analytical techniques, including FT-IR, UV-Vis, NMR, GC-MS, PXRD, SEM, TEM analysis, and QM calculations. Aiming to analyze various noncovalent interactions, topological methods such as QTAIM, NCI, ELF, and LOL were performed. Furthermore, the capacity of metal-ligand binding was examined by fluorescence emission spectroscopy. An in vitro investigation showed that the viability of MDA-MB-231 and HepG-2 cells was lower when exposed to the manufactured Cu2+ complex, in comparison to the normal cis-platin medication. The compounds were further evaluated for their in vitro antibacterial activity. The Ni2+ complex has shown promising activity against all tested pathogens, comparable to the reference drugs Gentamycin and Ketoconazole. Furthermore, a computational docking investigation was conducted to further examine the orientation, interaction, and conformation of the recently created compounds on the active site of the Bcl-2 protein.

RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.

GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking.

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformations from a set of poses, GSScore takes atoms as nodes and then establishes the docking interface of protein-ligand into multiple bipartite graphs within different shell ranges. Benefiting from the Graphormer and Shell-like graph architecture, GSScore can effectively capture the subtle differences between energetically favorable near-native conformations and unfavorable non-native poses without extra information. GSScore was extensively evaluated on diverse test sets including a subset of PDBBind version 2019, CASF2016 as well as DUD-E, and obtained significant improvements over existing methods in terms of RMSE, $R$ (Pearson correlation coefficient), Spearman correlation coefficient and Docking power.

Role of CSF1R 550th-tryptophan in kusunokinin and CSF1R inhibitor binding and ligand-induced structural effect.

Binding affinity is an important factor in drug design to improve drug-target selectivity and specificity. In this study, in silico techniques based on molecular docking followed by molecular dynamics (MD) simulations were utilized to identify the key residue(s) for CSF1R binding affinity among 14 pan-tyrosine kinase inhibitors and 15 CSF1R-specific inhibitors. We found tryptophan at position 550 (W550) on the CSF1R binding site interacted with the inhibitors' aromatic ring in a π-π way that made the ligands better at binding. Upon W550-Alanine substitution (W550A), the binding affinity of trans-(-)-kusunokinin and imatinib to CSF1R was significantly decreased. However, in terms of structural features, W550 did not significantly affect overall CSF1R structure, but provided destabilizing effect upon mutation. The W550A also did not either cause ligand to change its binding site or conformational changes due to ligand binding. As a result of our findings, the π-π interaction with W550's aromatic ring could be still the choice for increasing binding affinity to CSF1R. Nevertheless, our study showed that the increasing binding to W550 of the design ligand may not ensure CSF1R specificity and inhibition since W550-ligand bound state did not induce significantly conformational change into inactive state.

Tailoring FXR Modulators for Intestinal Specificity: Recent Progress and Insights.

While FXR has shown promise in regulating bile acid synthesis and maintaining glucose and lipid homeostasis, undesired side effects have been observed in clinical trials. To address this issue, the development of intestinally restricted FXR modulators has gained attention as a new avenue for drug design with the potential for safer systematic effects. Our review examines all currently known intestinally restricted FXR ligands and provides insights into the steps taken to enhance intestinal selectivity.

Development of Novel Immobilized Copper-Ligand Complex for Click Chemistry of Biomolecules.

Copper-catalyzed azide-alkyne cycloaddition click (CuAAC) reaction is widely used to synthesize drug candidates and other biomolecule classes. Homogeneous catalysts, which consist of copper coordinated to a ligand framework, have been optimized for high yield and specificity of the CuAAC reaction, but CuAAC reaction with these catalysts requires the addition of a reducing agent and basic conditions, which can complicate some of the desired syntheses. Additionally, removing copper from the synthesized CuAAC-containing biomolecule is necessary for biological applications but inconvenient and requires additional purification steps. We describe here the design and synthesis of a PNN-type pincer ligand complex with copper (I) that stabilizes the copper (I) and, therefore, can act as a CuAAC catalyst without a reducing agent and base under physiologically relevant conditions. This complex was immobilized on two types of resin, and one of the immobilized catalyst forms worked well under aqueous physiological conditions. Minimal copper leaching was observed from the immobilized catalyst, which allowed its use in multiple reaction cycles without the addition of any reducing agent or base and without recharging with copper ion. The mechanism of the catalytic cycle was rationalized by density functional theory (DFT). This catalyst's utility was demonstrated by synthesizing coumarin derivatives of small molecules such as ferrocene and sugar.

Hyaluronic Acid as a LYVE-1 Receptor Ligand in the Lymphatic System of Healthy Human Skin.

Immunohistochemical detection of the LYVE-1 marker in healthy human full-thickness skin (the epidermis and the dermis) was carried out. LYVE-1 expression was found in the endothelium of lymphatic capillaries located in the papillary dermis, in the endothelium of larger lymphatic vessels of the reticular dermis, and in fibroblasts, which indicates their joint participation in hyaluronan metabolism. LYVE-1+ staining detected for the first time in cells of the stratum basale, the stratum spinosum, and the stratum granulosum of healthy human epidermis indicates their participation in hyaluronan metabolism and allows us to consider the spaces between epidermis cells as prelimphatics.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

Two {CuI[P4Mo6]2}-Based Coordination Polymers Incorporating In Situ Converted Tetrapyridyl Ligands for Trace Analysis of Nitrofuran Antibiotics.

Nitrofurans are important synthetic broad-spectrum antibacterial drugs with the basic structure of 5-nitrofuran. Due to their toxicity, it is essential to develop a sensitive sensor with strong anti-interference capabilities for their detection. In this work, two {P4Mo6O31}12--based compounds, [H4(HPTTP)]2{CuI[Mo12O24(OH)6(PO4)3(HPO4)(H2PO4)4]}·xH2O (x = 13 for (1), 7 for (2); HPTTP = 4,4',4″,4‴-(1H-pyrrole-2,3,4,5-tetrayl)tetrapyridine), exhibiting similar coordination but distinct stacking modes. Both compounds were synthesized and used for the electrochemical detection of nitrofuran antibiotics. The tetrapyridine-based ligand was generated in situ during assembly, and its potential mechanism was discussed. Composite electrode materials, formed by mixing graphite powder with compounds 1-2 and physically grinding them, proved to be highly effective in the electrochemical trace detection of furazolidone (FZD) and furaltadone hydrochloride (FTD·HCl) under optimal conditions. Besides, the possible electrochemical detection mechanisms of two nitro-antibiotics were studied.

The role of the size of affinity ligands in the detection and characterization of extracellular vesicles.

Surface proteins on the membrane of nano-sized extracellular vesicles (EVs) not only play crucial roles in cell-to-cell communication, but also are specific binding targets for EV detection, isolation and tracking. The low abundance of protein biomarkers on EV surface, the formation of clusters and the complex EV surface network impose significant challenges to the study of EVs. Employing bulky sized affinity ligands, such as antibodies, in the detection and characterization of these vesicles often result in reduced sensitivity of detection or poor quantification of proteins on the EV surface. By virtue of their small size and high specificity, Affibody molecules emerge as a potential alternative to their monoclonal antibody counterparts as robust affinity ligands in EV research. In this study, we present a theoretical framework on the superiority of anti-HER2 Affibodies over anti-HER2 antibodies in labeling and detecting HER2-positive EVs, followed by the demonstration of the advantages of HER2 Affibodies in accessing EV surface and the detection of EVs through multiple types of approaches including fluorescence intensity, colorimetry, and fluorescence polarization. HER2 Affibodies outperformed by 10-fold over three HER2 antibody clones in accessing HER2-positive EVs derived from different human cancer cell lines. Furthermore, HRP-Affibody molecules could detect EVs from cancer cells spiked into human serum with at least a 2-fold higher sensitivity compared with that of their antibody counterparts. In addition, in fluorescence polarization assays in which no separation of free from bound ligand is required, FITC-labeled HER2 Affibodies could sensitively detect HER2-positive EVs with a clinically relevant limit of detection, whilst HER2 antibodies failed to detect EVs in the same conditions. With the demonstrated superiority in accessing and detecting surface targets over bulky-sized antibodies in EVs, Affibodies may become the next-generation of affinity ligands in the precise characterization and quantification of molecular architecture on the surface of EVs.

Chemical manipulation of an activation/inhibition switch in the nuclear receptor PXR.

Nuclear receptors are ligand-activated transcription factors that can often be useful drug targets. Unfortunately, ligand promiscuity leads to two-thirds of receptors remaining clinically untargeted. PXR is a nuclear receptor that can be activated by diverse compounds to elevate metabolism, negatively impacting drug efficacy and safety. This presents a barrier to drug development because compounds designed to target other proteins must avoid PXR activation while retaining potency for the desired target. This problem could be avoided by using PXR antagonists, but these compounds are rare, and their molecular mechanisms remain unknown. Here, we report structurally related PXR-selective agonists and antagonists and their corresponding co-crystal structures to describe mechanisms of antagonism and selectivity. Structural and computational approaches show that antagonists induce PXR conformational changes incompatible with transcriptional coactivator recruitment. These results guide the design of compounds with predictable agonist/antagonist activities and bolster efforts to generate antagonists to prevent PXR activation interfering with other drugs.