Sandhya Kortagere1, Dmitriy Chekmarev, William J Welsh, Sean Ekins. 1. Department of Pharmacology and Environmental Bioinformatics and Computational Toxicology Center (ebCTC), University of Medicine and Dentistry of New Jersey (UMDNJ)-Robert Wood Johnson Medical School, Piscataway, NJ, 08854, USA.
Abstract
PURPOSE: The human pregnane X receptor (PXR) is a transcriptional regulator of many genes involved in xenobiotic metabolism and excretion. Reliable prediction of high affinity binders with this receptor would be valuable for pharmaceutical drug discovery to predict potential toxicological responses MATERIALS AND METHODS: Computational models were developed and validated for a dataset consisting of human PXR (PXR) activators and non-activators. We used support vector machine (SVM) algorithms with molecular descriptors derived from two sources, Shape Signatures and the Molecular Operating Environment (MOE) application software. We also employed the molecular docking program GOLD in which the GoldScore method was supplemented with other scoring functions to improve docking results. RESULTS: The overall test set prediction accuracy for PXR activators with SVM was 72% to 81%. This indicates that molecular shape descriptors are useful in classification of compounds binding to this receptor. The best docking prediction accuracy (61%) was obtained using 1D Shape Signature descriptors as a weighting factor to the GoldScore. By pooling the available human PXR data sets we revealed those molecular features that are associated with human PXR activators. CONCLUSIONS: These combined computational approaches using molecular shape information may assist scientists to more confidently identify PXR activators.
PURPOSE: The humanpregnane X receptor (PXR) is a transcriptional regulator of many genes involved in xenobiotic metabolism and excretion. Reliable prediction of high affinity binders with this receptor would be valuable for pharmaceutical drug discovery to predict potential toxicological responses MATERIALS AND METHODS: Computational models were developed and validated for a dataset consisting of humanPXR (PXR) activators and non-activators. We used support vector machine (SVM) algorithms with molecular descriptors derived from two sources, Shape Signatures and the Molecular Operating Environment (MOE) application software. We also employed the molecular docking program GOLD in which the GoldScore method was supplemented with other scoring functions to improve docking results. RESULTS: The overall test set prediction accuracy for PXR activators with SVM was 72% to 81%. This indicates that molecular shape descriptors are useful in classification of compounds binding to this receptor. The best docking prediction accuracy (61%) was obtained using 1D Shape Signature descriptors as a weighting factor to the GoldScore. By pooling the available humanPXR data sets we revealed those molecular features that are associated with humanPXR activators. CONCLUSIONS: These combined computational approaches using molecular shape information may assist scientists to more confidently identify PXR activators.
Authors: Akash Khandelwal; Matthew D Krasowski; Erica J Reschly; Michael W Sinz; Peter W Swaan; Sean Ekins Journal: Chem Res Toxicol Date: 2008-06-12 Impact factor: 3.739
Authors: Sandhya Kortagere; Navid Madani; Marie K Mankowski; Arne Schön; Isaac Zentner; Gokul Swaminathan; Amy Princiotto; Kevin Anthony; Apara Oza; Luz-Jeannette Sierra; Shendra R Passic; Xiaozhao Wang; David M Jones; Eric Stavale; Fred C Krebs; Julio Martín-García; Ernesto Freire; Roger G Ptak; Joseph Sodroski; Simon Cocklin; Amos B Smith Journal: J Virol Date: 2012-05-30 Impact factor: 5.103
Authors: Shaili Aggarwal; Xiaonan Liu; Caitlyn Rice; Paul Menell; Philip J Clark; Nicholas Paparoidamis; You-Cai Xiao; Joseph M Salvino; Andréia C K Fontana; Rodrigo A España; Sandhya Kortagere; Ole V Mortensen Journal: ACS Chem Neurosci Date: 2019-06-24 Impact factor: 4.418
Authors: Sandhya Kortagere; William J Welsh; Joanne M Morrisey; Thomas Daly; Ijeoma Ejigiri; Photini Sinnis; Akhil B Vaidya; Lawrence W Bergman Journal: J Chem Inf Model Date: 2010-05-24 Impact factor: 4.956
Authors: Sandhya Kortagere; Matthew D Krasowski; Erica J Reschly; Madhukumar Venkatesh; Sridhar Mani; Sean Ekins Journal: Environ Health Perspect Date: 2010-06-17 Impact factor: 9.031
Authors: Sean Ekins; Sandhya Kortagere; Manisha Iyer; Erica J Reschly; Markus A Lill; Matthew R Redinbo; Matthew D Krasowski Journal: PLoS Comput Biol Date: 2009-12-11 Impact factor: 4.475