基于对接的植物激素3D-QSAR和分子动力学模拟

蒙延娟, 易忠胜, 艾芳婷, 张爱茜. 基于对接的植物激素3D-QSAR和分子动力学模拟[J]. 环境化学, 2014, 33(6): 880-890. doi: 10.7524/j.issn.0254-6108.2014.06.016
引用本文: 蒙延娟, 易忠胜, 艾芳婷, 张爱茜. 基于对接的植物激素3D-QSAR和分子动力学模拟[J]. 环境化学, 2014, 33(6): 880-890. doi: 10.7524/j.issn.0254-6108.2014.06.016
MENG Yanjuan, YI Zhongsheng, AI Fangting, ZHANG Aiqian. 3D QSAR study of phytoestrogens:A combined molecular dockingand molecular dynamics simulation[J]. Environmental Chemistry, 2014, 33(6): 880-890. doi: 10.7524/j.issn.0254-6108.2014.06.016
Citation: MENG Yanjuan, YI Zhongsheng, AI Fangting, ZHANG Aiqian. 3D QSAR study of phytoestrogens:A combined molecular dockingand molecular dynamics simulation[J]. Environmental Chemistry, 2014, 33(6): 880-890. doi: 10.7524/j.issn.0254-6108.2014.06.016

基于对接的植物激素3D-QSAR和分子动力学模拟

  • 基金项目:

    国家自然科学基金(21167006,21267008);广西自然科学基金(2013GXNSFAA019034);环境化学与生态毒理学国家重点实验室开放基金(KF2011-21)资助.

3D QSAR study of phytoestrogens:A combined molecular dockingand molecular dynamics simulation

  • Fund Project:
  • 摘要: 采用分子对接和分子动力学(MD)模拟方法研究植物雌激素类化合物与雌激素受体的相互作用机制,对接结果表明,雌激素受体活性位点的疏水和氢键作用是影响植物雌激素化合物活性的主要原因,植物雌激素类化合物主要与氨基酸残基GLU353、ARG394、HIS524和LEU525之间形成氢键.然后以对接后的分子构象进行分子结构叠合,结合比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)方法建立了3D-QSAR模型.CoMFA模型的交叉验证相关系数(Q2)和非交叉验证相关系数(R2)值分别为0.676和0.994,标准估计误差SEE和F统计量分别为0.143和342.115;CoMSIA模型的Q2=0.565,R2=0.972,SEE=0.286和F=111.480.结果表明,CoMFA和CoMSIA模型具有良好的稳定性和预测能力,可为植物雌激素的雌激素效应研究提供有力的支持.采用MD模拟研究了小分子和受体蛋白的动力学情况,为对接结果的合理性提供了验证.
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    [2] Setchell K. Phytoestrogens: The biochemistry, physiology, and implications for human health of soy isoflavones[J]. Am J Clin Nutr, 1998, 68(6): 1333S-1346S
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    [4] Liu Z H, Kanjo Y, Mizutani S. A review of phytoestrogens: Their occurrence and fate in the environment[J]. Water Res, 2010, 44(2): 567-577
    [5] Zhang Y M, Yang X S, Sun C, et al. Quantitative structure-activity relationship of compounds binding to estrogen receptor β based on heuristic method[J]. Science China Chemistry, 2011, 54(1): 237-243
    [6] Shi L M, Fang H, Tong W D, et al. QSAR models using a large diverse set of estrogens[J]. J Chem Inf Comput Sci, 2001, 41(1): 186-195
    [7] Yu S J, Keenan S M, Tong W D, et al. Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative structure-activity relationship (3D-QSAR) models: Predicting the estrogenic activity of xenoestrogens[J]. Chem Res Toxicol, 2002, 15(10): 1229-1234
    [8] Waller C L. A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds[J]. J Chem Inf Comput Sci, 2004, 44(2): 758-765
    [9] Wu Y, Wang Y, Zhang A Q, et al. Three-dimensional quantitative structure-activity relationships of flavonoids and estrogen receptors based on docking[J]. Chinese Science Bulletin, 2010, 55(15): 1488-1494
    [10] Yang X S, Wang X D, Ji L, et al. Combining docking and comparative molecular similarity indices analysis (COMSIA) to predict estrogen activity and probe molecular mechanisms of estrogen activity for estrogen compounds[J]. Chinese Science Bulletin, 2008, 53(23): 3626-3633
    [11] Branham W S, Dial S L, Moland C L, et al. Phytoestrogens and mycoestrogens bind to the rat uterine estrogen receptor[J]. J Nutr, 2002, 132(4): 658-664
    [12] Chiba H, Uehara M, Wu J, et al. Hesperidin, a citrus flavonoid, inhibits bone loss and decreases serum and hepatic lipids in ovariectomized mice[J]. J Nutr, 2003, 133(6): 1892-1897
    [13] Brzozowski A M, Pike A C, Dauter Z, et al. Molecular basis of agonism and antagonism in the oestrogen receptor[J]. Nature, 1997, 389(6652): 753-758
    [14] Schuttelkopf A W, van Aalten D M F. PRODRG: A tool for high-throughput crystallography of protein-ligand complexes[J]. Acta Crystallogr D Biol Crystallogr, 2004, 60(8): 1355-1363
    [15] Fuhrmans M, Sanders B P, Marrink S J, et al. Effects of bundling on the properties of the SPC water model[J]. Theor Chem Acc, 2010, 125(3): 335-344
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    [18] Berendsen H J C, Postma J P M, van Gunsteren W F, et al. Molecular dynamics with coupling to an external bath[J]. J Chem Phys, 1984, 81(8):3684-3690
    [19] Tong W D, Perkins R, Strelitz R, et al. Quantitative structure-activity relationships (QSARs) for estrogen binding to the estrogen receptor: Predictions across species[J]. Environ Health Perspect, 1997, 105(10): 1116-1124
    [20] Fang H, Tong W D, Shi L M, et al. Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens[J]. Chem Res Toxicol, 2001, 14(3): 280-294
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  • 收稿日期:  2013-10-09
蒙延娟, 易忠胜, 艾芳婷, 张爱茜. 基于对接的植物激素3D-QSAR和分子动力学模拟[J]. 环境化学, 2014, 33(6): 880-890. doi: 10.7524/j.issn.0254-6108.2014.06.016
引用本文: 蒙延娟, 易忠胜, 艾芳婷, 张爱茜. 基于对接的植物激素3D-QSAR和分子动力学模拟[J]. 环境化学, 2014, 33(6): 880-890. doi: 10.7524/j.issn.0254-6108.2014.06.016
MENG Yanjuan, YI Zhongsheng, AI Fangting, ZHANG Aiqian. 3D QSAR study of phytoestrogens:A combined molecular dockingand molecular dynamics simulation[J]. Environmental Chemistry, 2014, 33(6): 880-890. doi: 10.7524/j.issn.0254-6108.2014.06.016
Citation: MENG Yanjuan, YI Zhongsheng, AI Fangting, ZHANG Aiqian. 3D QSAR study of phytoestrogens:A combined molecular dockingand molecular dynamics simulation[J]. Environmental Chemistry, 2014, 33(6): 880-890. doi: 10.7524/j.issn.0254-6108.2014.06.016

基于对接的植物激素3D-QSAR和分子动力学模拟

  • 1.  桂林理工大学化学与生物工程学院, 桂林, 541004;
  • 2.  环境化学与生态毒理学国家重点实验室, 中国科学院生态环境研究中心, 北京, 100085
基金项目:

国家自然科学基金(21167006,21267008);广西自然科学基金(2013GXNSFAA019034);环境化学与生态毒理学国家重点实验室开放基金(KF2011-21)资助.

摘要: 采用分子对接和分子动力学(MD)模拟方法研究植物雌激素类化合物与雌激素受体的相互作用机制,对接结果表明,雌激素受体活性位点的疏水和氢键作用是影响植物雌激素化合物活性的主要原因,植物雌激素类化合物主要与氨基酸残基GLU353、ARG394、HIS524和LEU525之间形成氢键.然后以对接后的分子构象进行分子结构叠合,结合比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)方法建立了3D-QSAR模型.CoMFA模型的交叉验证相关系数(Q2)和非交叉验证相关系数(R2)值分别为0.676和0.994,标准估计误差SEE和F统计量分别为0.143和342.115;CoMSIA模型的Q2=0.565,R2=0.972,SEE=0.286和F=111.480.结果表明,CoMFA和CoMSIA模型具有良好的稳定性和预测能力,可为植物雌激素的雌激素效应研究提供有力的支持.采用MD模拟研究了小分子和受体蛋白的动力学情况,为对接结果的合理性提供了验证.

English Abstract

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