兰州大学机构库 >化学化工学院
二噁英毒性预测和毒理的理论计算研究
Alternative TitlePrediction of Dioxin Toxicity and Theoretical Calculation of Toxicology
杨红
Subtype硕士
Thesis Advisor张晓昀
2020-06-01
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name理学硕士
Degree Discipline分析化学
Keyword结构活性关系 二维定量构效 二噁英 三维定量构效 分子对接
Abstract随着当今世界经济的快速发展,环境污染问题日益突出。持久有机污染物具有高毒性、持久性和生物蓄积性等特征,越来越引起人们的重视。二噁英作为一类持久有机污染物拥有大量的同系物,其中一部分毒性巨大。由于二噁英毒性实验费时费力造成毒性数据的严重匮乏,因此二噁英毒性数据的缺失以及毒性机制的不明确给人类的生产和生活都带来了巨大的影响。本工作的目的是通过使用化学信息学的理论计算方法来高效、准确研究和预测二噁英的毒性,推测其毒性机理,弥补实验化学的不足。 第一章 持久有机污染物二噁英和化学信息学综述。 主要综述了二噁英的相关背景、前人在二噁英研究中取得的成果介绍了化学信息学的相关知识以及本研究中所使用的化学信息学方法。 第二章 基于结构活性关系(SAR)和二维定量构效关系(2D-QSAR)评估并预测二噁英的毒性。 在这一章中,我们的主要目的是利用结构活性关系和二维定量构效关系的方法来研究影响二噁英毒性大小的因素,并预测二噁英分子的毒性数据。首先使用定性结构活性关系分析了60种二噁英分子的结构和其毒性数值之间的关系,初步得出影响二噁英毒性的因素主要与取代基的种类、取代的位置有关。为了验证定性分析的猜测,利用二维定量构效关系建立了线性和非线性2D-QSAR模型。通过对模型进行验证,线性模型和非线性模型都具有良好的拟合度、鲁棒性、可靠性以及预测能力。分别使用这两种模型对162种缺乏毒性数据的二噁英分子进行了毒性数据预测,并使用Roy提出的方法对预测结果的准确性进行了评判,结果表明两个模型的预测值具有很高的可信度。此外二维定量构效关系还揭示了5个影响毒性的结构因素,这5个结构因素揭示的毒性机制印证了定性结构活性关系分析得到的推测。该工作补充了持久有机污染物二噁英的毒性数据库,为进一步研究二噁英的毒理提供了初步的依据。 第三章 基于三维定量构效关系(3D-QSAR)和分子对接研究二噁英毒性以及其与AhR的结合模式。 在这章中,我们的主要目的是更准确地研究二噁英的三维结构与毒性的定量关系,并确定二噁英分子与受体蛋白质的结合模式。首先利用CoMFA和CoMSIA对两种不同骨架的二噁英分子分别建立3D-QSAR模型。通过对模型等势图的分析,发现了影响二噁英毒性的几个场,确定了这些场对应的影响二噁英分子毒性的结构特征。为了更加直观地看到小分子配体和蛋白质受体之间的结合模式,我们利用分子对接的方法研究了几个典型二噁英分子与蛋白质之间的作用。分子对接的结果表明了不同分子与受体之间不同的结合模式,从而解释了小分子配体毒性差异的原因。该工作得到的结论也进一步验证了前一个工作中得到的结论,共同揭示了影响二噁英分子毒性的结构因素,为进一步研究二噁英的毒理提供了理论依据。 第四章 总结与展望。 在本章中,我们将论文中全部的工作进行了系统地总结,并列出了本论文得到的所有重要结论。
Other AbstractWith the rapid development of economy, environmental pollution has become prominent. Persistent organic pollutants have the characteristics of high toxicity, persistence and bioaccumulation, which have attracted more and more attention. As a kind of persistent organic pollutants, dioxins have a large number of homologues, some of which are extremely toxic. However, the toxicity experiments of dioxins are time-consuming and laborious, which results in a serious lack of toxicity data. The lack of toxicity data and the unclear toxicity mechanism have a greatly impact on human production and daily life. This paper aims to explore and predict the toxicity of dioxin efficiently and accurately using the theoretical calculation methods of chemoinformatics, speculate on its toxicology, which makes up for the shortcomings of experimental chemistry. Chapter 1 Review of persistent organic pollutants dioxins and chemoinformatics. In chapter 1, we summarized the related background of dioxin and the conclusion by previous researchers in the study of dioxinwe introduced the related knowledge of chemoinformatics and the chemoinformatics methods used in this paper. Chapter 2 In silico toxicity evaluation of dioxins using structure-activity relationship (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR). In chapter 2, our main purpose is to use qualitative structural-activity relationship analysis and two-dimensional quantitative structure-activity methods to explore the factors that affect the toxicity of dioxins and predict the toxicity data of dioxins. Firstly, the qualitative structure-activity relationship was used to analyze the relationship between the structures of 60 dioxins and corresponding pEC50 values. It was concluded that the factors affecting the toxicity of dioxins were mainly related to the types of substituents and the positions of substitutions. In order to verify the above analysis, two-dimensional quantitative structure-activity relationships were used to establish linear and non-linear 2D-QSAR models. Both the linear model and the non-linear model have good fitness ability, reliability, robustness and predicative ability after verification. These two established models were used to predict pEC50 values of 162 dioxin lacking toxicity data, and the accuracy of predictions was evaluated using the method proposed by Roy. The results show that the predicted values obtained from the two models have high reliability. In addition, the two-dimensional quantitative structure-activity relationship also revealed five structural factors affecting toxicity. The toxicity mechanism revealed by these five structural factors confirms the speculative conclusions obtained from the qualitative structure-activity relationship analysis. This work supplemented the toxicity database of dioxins and provided a preliminary basis for further research on the toxicity mechanism of dioxins. Chapter 3 Research on dioxin toxicity and binding mode with AhR based on three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking. In chapter 3, our main purpose is to explore the quantitative relationship between the three-dimensional structure of dioxins and toxicity data accurately, and determine the binding mode of dioxin molecules to receptor (AhR). Firstly, CoMFA and CoMSIA were used to establish 3D-QSAR models of two kinds of dioxins with different skeletons. Through the analysis of the contour maps, several fields affecting dioxins toxicity were found, and the corresponding structural factors that affect molecular toxicity were determined. In order to observe the binding mode between ligands and protein receptors intuitively, molecular docking was used to study the interaction between several typical dioxins molecules and AhR. The results indicated different binding modes between different molecules and receptor, which explains the difference in toxicity of small ligands. The conclusions obtained from this work further validated the conclusions obtained in the previous work, and together revealed the structural factors affecting the molecular toxicity of dioxins, providing a basis for further research on the toxicology of dioxins. Chapter 4 Main conclusions and prospects. In Chapter 4, all the work of this paper was summarized and all the important conclusions from this paper were listed.
Pages97
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/464475
Collection化学化工学院
Affiliation化学化工学院
First Author AffilicationCollege of Chemistry and Chemical Engineering
Recommended Citation
GB/T 7714
杨红. 二噁英毒性预测和毒理的理论计算研究[D]. 兰州. 兰州大学,2020.
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