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Pharmacophore modeling improves virtual screening for novel peroxisome proliferator-activated receptor-gamma ligands

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Abstract

Peroxisome proliferator-activated receptor-gamma (PPARγ) is a nuclear hormone receptor involved in regulating various metabolic and immune processes. The PPAR family of receptors possesses a large binding cavity that imparts promiscuity of ligand binding not common to other nuclear receptors. This feature increases the challenge of using computational methods to identify PPAR ligands that will dock favorably into a structural model. Utilizing both ligand- and structure-based pharmacophore methods, we sought to improve agonist prediction by grouping ligands according to pharmacophore features, and pairing models derived from these features with receptor structures for docking. For 22 of the 33 receptor structures evaluated we observed an increase in true positive rate (TPR) when screening was restricted to compounds sharing molecular features found in rosiglitazone. A combination of structure models used for docking resulted in a higher TPR (40 %) when compared to docking with a single structure model (<20 %). Prediction was also improved when specific protein–ligand interactions between the docked ligands and structure models were given greater weight than the calculated free energy of binding. A large-scale screen of compounds using a marketed drug database verified the predictive ability of the selected structure models. This study highlights the steps necessary to improve screening for PPARγ ligands using multiple structure models, ligand-based pharmacophore data, evaluation of protein–ligand interactions, and comparison of docking datasets. The unique combination of methods presented here holds potential for more efficient screening of compounds with unknown affinity for PPARγ that could serve as candidates for therapeutic development.

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Acknowledgments

The authors would like to thank Lera Brannan for her initial efforts with identifying ligand-based pharmacophore screening methods to use for this study. The authors would also like to thank Dr. David Goodsell for his help with visualization techniques for the AutoLigand data. The authors acknowledge Advanced Research Computing at Virginia Tech for providing computational resources and technical support, which contributed to the results reported within this paper. http://www.arc.vt.edu. Funding was provided by the National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (NIH) (Grant Number 1F31DK091186-01A1).

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Authors and Affiliations

  1. Genetics, Bioinformatics, and Computational Biology Program, Virginia Tech, Blacksburg, VA, USA

    Stephanie N. Lewis, Raquel Hontecillas, Josep Bassaganya-Riera & David R. Bevan

  2. Department of Biochemistry, Virginia Tech, Blacksburg, VA, USA

    Stephanie N. Lewis & David R. Bevan

  3. Virginia College of Osteopathic Medicine, Virginia Tech, Blacksburg, VA, USA

    Zulma Garcia

  4. Nutritional Immunology and Molecular Medicine Laboratory, Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA

    Stephanie N. Lewis, Raquel Hontecillas & Josep Bassaganya-Riera

  5. Department of Biomedical Sciences and Pathobiology, Virginia-Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, USA

    Josep Bassaganya-Riera

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  1. Stephanie N. Lewis
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Correspondence to David R. Bevan.

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Lewis, S.N., Garcia, Z., Hontecillas, R. et al. Pharmacophore modeling improves virtual screening for novel peroxisome proliferator-activated receptor-gamma ligands. J Comput Aided Mol Des 29, 421–439 (2015). https://doi.org/10.1007/s10822-015-9831-x

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