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题名: In Silico Prediction of Estrogen Receptor Subtype Binding Affinity and Selectivity Using Statistical Methods and Molecular Docking with 2-Arylnaphthalenes and 2-Arylquinolines
作者: Wang, Zhizhong1;  Li, Yan2;  Ai, Chunzhi3;  Wang, Yonghua1
关键词: receptor ;  selectivity ;  QSAR ;  docking
刊名: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
发表日期: 2010-09-01
DOI: 10.3390/ijms11093434
卷: 11, 期:9, 页:3434-3458
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology ;  Physical Sciences
类目[WOS]: Chemistry, Multidisciplinary
研究领域[WOS]: Chemistry
英文摘要: Over the years development of selective estrogen receptor (ER) ligands has been of great concern to researchers involved in the chemistry and pharmacology of anticancer drugs, resulting in numerous synthesized selective ER subtype inhibitors. In this work, a data set of 82 ER ligands with ER alpha and ER beta inhibitory activities was built, and quantitative structure-activity relationship (QSAR) methods based on the two linear (multiple linear regression, MLR, partial least squares regression, PLSR) and a nonlinear statistical method (Bayesian regularized neural network, BRNN) were applied to investigate the potential relationship of molecular structural features related to the activity and selectivity of these ligands. For ER alpha and ER beta, the performances of the MLR and PLSR models are superior to the BRNN model, giving more reasonable statistical properties (ER alpha: for MLR, R-tr(2) = 0.72, Q(te)(2) = 0.63; for PLSR, R-tr(2) = 0.92, Q(te)(2) = 0.84. ER beta: for MLR, R-tr(2) = 0.75, Q(te)(2) = 0.75; for PLSR, R-tr(2) = 0.98, Q(te)(2) = 0.80). The MLR method is also more powerful than other two methods for generating the subtype selectivity models, resulting in R-tr(2) = 0.74 and Q(te)(2) = 0.80. In addition, the molecular docking method was also used to explore the possible binding modes of the ligands and a relationship between the 3D-binding modes and the 2D-molecular structural features of ligands was further explored. The results show that the binding affinity strength for both ER alpha and ER beta is more correlated with the atom fragment type, polarity, electronegativites and hydrophobicity. The substitutent in position 8 of the naphthalene or the quinoline plane and the space orientation of these two planes contribute the most to the subtype selectivity on the basis of similar hydrogen bond interactions between binding ligands and both ER subtypes. The QSAR models built together with the docking procedure should be of great advantage for screening and designing ER ligands with improved affinity and subtype selectivity property.
关键词[WOS]: ER-BETA LIGANDS ;  NEURAL-NETWORKS ;  BIOLOGICAL EVALUATION ;  DIVERSE SET ;  MODULATORS ;  DERIVATIVES ;  SERIES ;  DETERMINANTS ;  REGRESSION ;  SEARCH
语种: 英语
WOS记录号: WOS:000282223500027
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/142135
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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作者单位: 1.NW A&F Univ, Ctr Bioinformat, Yangling, Shaanxi, Peoples R China
2.Dalian Univ Technol, Sch Chem Engn, Dalian, Liaoning, Peoples R China
3.Chinese Acad Sci, Dalian Inst Chem Phys, Dalian, Liaoning, Peoples R China

Recommended Citation:
Wang, Zhizhong,Li, Yan,Ai, Chunzhi,et al. In Silico Prediction of Estrogen Receptor Subtype Binding Affinity and Selectivity Using Statistical Methods and Molecular Docking with 2-Arylnaphthalenes and 2-Arylquinolines[J]. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,2010,11(9):3434-3458.
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