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A high-throughput, computational system to predict if environmental contaminants can bind to human nuclear receptors
WANG Xiaoxiang 1,ZHANG Xiaowei 1,XIA Pu 1,ZHANG Junjiang 1,WANG Yuting 1,ZHANG Rui 2,J.P.Giesy 1,SHI Wei 3 *,YU Hongxia 4
1.State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210046
2.State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210046, School of Resources and Environment, University of Jinan, Jinan, Shandong 250022
3. Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, SK S7N5A2, Canada
4. School of Biological Sciences, University of Hong Kong, Hong Kong, SAR, 999077, PR China
*Correspondence author
#Submitted by
Subject:
Funding: Natural Science Foundation of Jiangsu Province(No.BK20130551), Natural Science Foundation of China (No.21577058 , 21307054), Major Science and Technology Program for Water Pollution Control and Treatment(No.2012ZX07101-003), Specialized Research Fund for the Doctoral Program of Higher Education(No.20130091120013), Non-profit industry research subject(No.201409040)
Opened online:20 October 2016
Accepted by: none
Citation: WANG Xiaoxiang,ZHANG Xiaowei,XIA Pu.A high-throughput, computational system to predict if environmental contaminants can bind to human nuclear receptors[OL]. [20 October 2016] http://en.paper.edu.cn/en_releasepaper/content/4706889
 
 
Some pollutants can bind to nuclear receptors (NRs) and modulate their activities. Predicting interactions of NRs with chemicals is required by various jurisdictions because these molecular initiating events can result in adverse, apical outcomes, such as survival, growth or reproduction. The goal of this study was to develop a high-throughput, computational method to predict potential agonists of NRs, especially for contaminants in the environment or to which people or wildlife are expected to be exposed, including both persistent and pseudo-persistent chemicals. A 3D-structure database containing 39 human NRs was developed. The database was then combined with AutoDock Vina to develop a System for Predicting Potential Effective Nuclear Receptors (SPEN), based on inverse docking of chemicals. The SPEN was further validated and evaluated by experimental results for a subset of 10 chemicals. Finally, to assess the robustness of SPEN, its ability to predict potentials of 40 chemicals to bind to some of the most studied receptors was evaluated. SPEN is rapid, cost effective and powerful for predicting binding of chemicals to NRs. SPEN was determined to be useful for screening chemicals so that pollutants in the environment can be prioritized for regulators or when considering alternative compounds to replace known or suspected contaminants with poor environmental profiles.
Keywords:Nuclear Receptors, Inverse Docking, Agonists, Endocrine disrupting activities
 
 
 

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