Computational estimation of rainbow trout estrogen receptor binding affinities for environmental estrogens.

Environmental estrogens have been the subject of intense research due to their documented detrimental effects on the health of fish and wildlife and their potential to negatively impact humans. A complete understanding of how these compounds affect health is complicated because environmental estrogens are a structurally heterogeneous group of compounds. In this work, computational molecular dynamics simulations were utilized to predict the binding affinity of different compounds using rainbow trout (Oncorhynchus mykiss) estrogen receptors (ERs) as a model. Specifically, this study presents a comparison of the binding affinity of the natural ligand estradiol-17β to the four rainbow trout ER isoforms with that of three known environmental estrogens 17α-ethinylestradiol, bisphenol A, and raloxifene. Two additional compounds, atrazine and testosterone, that are known to be very weak or non-binders to ERs were tested. The binding affinity of these compounds to the human ERα subtype is also included for comparison. The results of this study suggest that, when compared to estradiol-17β, bisphenol A binds less strongly to all four receptors, 17α-ethinylestradiol binds more strongly, and raloxifene has a high affinity for the α subtype only. The results also show that atrazine and testosterone are weak or non-binders to the ERs. All of the results are in excellent qualitative agreement with the known in vivo estrogenicity of these compounds in the rainbow trout and other fishes. Computational estimation of binding affinities could be a valuable tool for predicting the impact of environmental estrogens in fish and other animals. Copyright © 2010 Elsevier Inc. All rights reserved.

Citation

Conrad Shyu, Timothy D Cavileer, James J Nagler, F Marty Ytreberg. Computational estimation of rainbow trout estrogen receptor binding affinities for environmental estrogens. Toxicology and applied pharmacology. 2011 Feb 1;250(3):322-6

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PMID: 21075131

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