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  • Title: Prediction of ligand binding affinity and orientation of xenoestrogens to the estrogen receptor by molecular dynamics simulations and the linear interaction energy method.
    Author: van Lipzig MM, ter Laak AM, Jongejan A, Vermeulen NP, Wamelink M, Geerke D, Meerman JH.
    Journal: J Med Chem; 2004 Feb 12; 47(4):1018-30. PubMed ID: 14761204.
    Abstract:
    Exposure to environmental estrogens has been proposed as a risk factor for disruption of reproductive development and tumorigenesis of humans and wildlife (McLachlan, J. A.; Korach, K. S.; Newbold, R. R.; Degen, G. H. Diethylstilbestrol and other estrogens in the environment. Fundam. Appl. Toxicol. 1984, 4, 686-691). In recent years, many structurally diverse environmental compounds have been identified as estrogens. A reliable computational method for determining estrogen receptor (ER) binding affinity is of great value for the prediction of estrogenic activity of such compounds and their metabolites. In the presented study, a computational model was developed for prediction of binding affinities of ligands to the ERalpha isoform, using MD simulations in combination with the linear interaction energy (LIE) approach. The linear interaction energy approximation was first described by Aqvist et al. (Aqvist, J.; Medina, C.; Samuelsson, J. E. A new method for predicting binding affinity in computer-aided drug design. Protein Eng. 1994, 7, 385-391) and relies on the assumption that the binding free energy (DeltaG) depends linearly on changes in the van der Waals and electrostatic energy of the system. In the present study, MD simulations of ligands in the ERalpha ligand binding domain (LBD) (Shiau, A. K.; Barstad, D.; Loria, P. M.; Cheng, L.; Kushner, P. J.; Agard, D. A.; Greene, G. L. The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen. Cell 1998, 95, 927-937), as well as ligands free in water, were carried out using the Amber 6.0 force field (http://amber.scripps.edu/). Contrary to previous LIE methods, we took into account every possible orientation of the ligands in the LBD and weighted the contribution of each orientation to the total binding affinity according to a Boltzman distribution. The training set (n = 19) contained estradiol (E2), the synthetic estrogens diethylstilbestrol (DES) and 11beta-chloroethylestradiol (E2-Cl), 16alpha-hydroxy-E2 (estriol, EST), the phytoestrogens genistein (GEN), 8-prenylnaringenin (8PN), and zearalenon (ZEA), four derivatives of benz[a]antracene-3,9-diol, and eight estrogenic monohydroxylated PAH metabolites. We obtained an excellent linear correlation (r(2) = 0.94) between experimental (competitive ER binding assay) and calculated binding energies, with K(d) values ranging from 0.15 mM to 30 pM, a 5 000 000-fold difference in binding affinity. Subsequently, a test set (n = 12) was used to examine the predictive value of our model. This set consisted of the synthetic estrogen 5,11-cis-diethyl-5,6,11,12-tetrahydrochrysene-2,8-diol (THC), daidzein (DAI), equol (EQU) and apigenin (API), chlordecone (KEP), progesterone (PRG), several mono- and dihydroxylated PAH metabolites, and two brominated biphenyls. The predicted binding affinities of these estrogenic compounds were in very good agreement with the experimental values (average deviation of 0.61 +/- 0.4 kcal/mol). In conclusion, our LIE model provides a very good method for prediction of absolute ligand binding affinities, as well as binding orientation of ligands.
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