You are here:
LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening
Citation:
Elkins, S., M. GOLDSMITH, A. Simon, Z. Zsoldos, O. Ravitz, AND A. J. Williams. LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening. Journal of Computational Medicine. Hindawi Publishing Corporation, New York, NY, 2013(513537):1-8, (2013).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
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 (N = 228). 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.
URLs/Downloads:
Journal of Computational Medicine
LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening (PDF, NA pp, 260 KB, about PDF)