LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening. (23rd June 2013)

• Record Type: Journal Article
• Authors: Ekins, Sean; Goldsmith, Michael-R.; Simon, Aniko; Zsoldos, Zsolt; Ravitz, Orr; Williams, Antony J.
• Other Names: Mustata Wilson Gabriela Academic Editor.
• Is Part Of: Journal of computational medicine. Volume 2013(2013)
• Publication Date: 2013-06-23
• Subjects: Medical informatics -- Periodicals; Computational intelligence -- Periodicals; Computational intelligence; Medical informatics; Periodicals; Electronic journals; 610.285
• DOI: 10.1155/2013/513537
• Languages: English
• ISSNs: 2314-5080

Abstract

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 ( 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.