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Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR
Journal article   Open access

Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR

Sean Ekins, Sandhya Kortagere, Manisha Iyer, Erica J Reschly, Markus A Lill, Matthew R Redinbo and Matthew D Krasowski
PLoS computational biology, v 5(12), pp e1000594-e1000594
Dec 2009
DOI: https://doi.org/10.1371/journal.pcbi.1000594
PMID: 20011107
Featured in Collection :   UN Sustainable Development Goals @ Drexel
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https://doi.org/10.1371/journal.pcbi.1000594View
Published, Version of Record (VoR) Open

Abstract

Protein Structure, Tertiary Receptors, Steroid - metabolism Humans Computational Biology Models, Molecular Crystallography, X-Ray Ligands Molecular Structure Receptors, Steroid - agonists Quantitative Structure-Activity Relationship Receptors, Steroid - chemistry
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 5alpha-androstan-3beta-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.

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96 citations in Web of Science
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Collaboration types
Domestic collaboration
Web of Science research areas
Biochemical Research Methods
Mathematical & Computational Biology
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