Discovery of novel natural compound inhibitors targeting estrogen receptor α by an integrated virtual screening strategy
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Estrogen receptor (ER) is a nuclear hormone receptor and plays an important role in mediating the cellular effects of estrogen. ER can be classified into two receptors: estrogen receptor alpha (ERα) and beta (ERβ), and the former is expressed in 50~80% of breast tumors and has been extensively investigated in breast cancer for decades. Excessive exposure to estrogen can obviously stimulate the growth of breast cancers primarily mediated by ERα, and thus anti-estrogen therapies by small molecules are of concern to clinicians and pharmaceutical industry in the treatment of ERα-positive breast cancers. Although a series of estrogen receptor modulators have been developed, these drugs can lead to resistance and side effects. Therefore, the development of small molecule inhibitors with high target specificity has been intensified. In this pursuit, an integrated computer-aided virtual screening technique, including molecular docking and pharmacophore model screening, was used to screen traditional Chinese medicine (TCM) databases. The compounds with high docking scores and fit values were subjected to ADME (adsorption, distribution, metabolism, excretion) and toxicity prediction, and ten hits were identified as potential inhibitors targeting ERα. Molecular docking was used to investigate the binding modes between ERα and three most potent hits, and molecular dynamic simulations were chosen to explore the stability of these complexes. The rank of the predicted binding free energies evaluated by MM/GBSA is consistent with the docking score. These novel scaffolds discovered in the present study can be used as critical starting point in the drug discovery process for treating ERα-positive breast cancer.
KeywordsEstrogen receptor (ER) α Traditional Chinese medicine (TCM) Molecular docking Pharmacophore model Molecular dynamics simulation Virtual screening
This study is supported by the National Science Foundation of China (81803430) and Qing Lan Project and Postgraduate Research and Practice Innovation Program of Jiangsu Province (SJCX18_1051).
- 24.Schrödinger (2009). version 9.0; Schrödinger, LLC: New YorkGoogle Scholar
- 30.Discovery Studio 2.5 (2009). Accelrys Inc., San DiegoGoogle Scholar
- 35.Frisch M, Trucks GW, Schlegel H, Scuseria GE, Robb MA, Cheeseman JR, Montgomery J, Vreven T, Kudin KN, Burant JC (2004) Gaussian 03, revision c. 02, vol 4. Gaussian. Inc., Wallingford, CTGoogle Scholar