Table of Contents
Journal of Computational Medicine
Volume 2013 (2013), Article ID 513537, 8 pages
http://dx.doi.org/10.1155/2013/513537
Research Article

LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening

1Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA
2National Exposure Research Laboratory, US-Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, NC 27711, USA
3SimBioSys, Inc., 135 Queen's Plate Drive, Suite 520, Toronto, ON, Canada M9W 6V1
4Royal Society of Chemistry, 904 Tamaras Circle, Wake Forest, NC 27587, USA

Received 1 February 2013; Accepted 28 May 2013

Academic Editor: Gabriela Mustata Wilson

Copyright © 2013 Sean Ekins et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nuclear receptors (NRs) are important biological macromolecular transcription factors that are implicated in multiple biological pathways and may interact with other xenobiotics that are endocrine disruptors present in the environment. Examples of important NRs include the androgen receptor (AR), estrogen receptors (ER), and the pregnane X receptor (PXR). In this study we have utilized the Ligand Activity by Surface Similarity Order (LASSO) method, a ligand-based virtual screening strategy to derive structural (surface/shape) molecular features used to generate predictive models of biomolecular activity for AR, ER, and PXR. For PXR, twenty-five models were built using between 8 to 128 agonists and tested using 3000, 8000, and 24,000 drug-like decoys including PXR inactive compounds . Preliminary studies with AR and ER using LASSO suggested the utility of this approach with 2-fold enrichment factors at 20%. We found that models with 64–128 PXR actives provided enrichment factors of 10-fold (10% actives in the top 1% of compounds screened). The LASSO models for AR and ER have been deployed and are freely available online, and they represent a ligand-based prediction method for putative NR activity of compounds in this database.