利用藥效基團模組及虛擬篩選技術設計人類二氫乳清酸脫氫酵素及血栓素受體蛋白抑制劑

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研究生：	林郁珊 Lin, Yu-Shan
論文名稱：	利用藥效基團模組及虛擬篩選技術設計人類二氫乳清酸脫氫酵素及血栓素受體蛋白抑制劑 Design hDHODH Inhibitors and TP Inhibitors Using Pharmacophore Modeling and Virtual Screening Techniques
指導教授：	唐傳義
口試委員:	唐傳義林俊淵陳光武
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2012
畢業學年度：	100
語文別：	英文
論文頁數：	52
中文關鍵詞：	藥效基團、電腦輔助藥物設計、二氫乳清酸脫氫酵素、血栓素受體蛋白、虛擬篩選
外文關鍵詞：	pharmacophore, computer-aided drug design, dihydroorotate dehydrogenase, thromboxane A2 receptor, virtual screening
相關次數：	點閱：3 下載：0
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在這篇論文中,我們的目的是針對選定的目標蛋白建立可辨識高活性化合物的藥效基團模型來進行電腦輔助藥物設計。選定的兩個蛋白在醫學上表徵為下：人類二氫乳清酸脫氫酵素是一個與癌症和自體免疫及發炎等疾病有高度相關的蛋白酶。血栓素受體蛋白藉著激活血栓素來促進血小板凝聚反應，但過量活化則會導致血栓塞和心血管疾病。基於這兩個與人類多種疾病相關蛋白的重要性，我們利用一群已知生物活性的化合物來建構藥效基團，並使用成本函數分析、費雪隨機分配檢定及命中率預估測試來驗證所建立模型的預估品質及信心強度。數據結果顯示我們建立的藥效基團模型具有優良的預測能力。由於人類二氫乳清酸脫氫酵素的結晶結構已從實驗上被取得，然而血栓素受體蛋白的結晶結構尚未被解出，我們便針對這兩個蛋白建立不同的工作流程。使用所建立出的模型，結合里賓斯基五規則及受體配體對接模擬進行化合物資料庫的虛擬篩選，找出可能成為候選藥物的化合物。經過篩選後，我們找出了一百五十五個針對人類二氫乳清酸脫氫酵素的候選抑制劑，以及五千六百二十一個針對血栓素受體蛋白的候選抑制劑，這些抑制劑未來可提供給其他研究團隊進行後續的藥物實驗。

In this research, our objective is to build the pharmacophore models for selected target proteins that can identify inhibitors with high biological activities and to execute computer-aided drug design. Human dihydroorotate dehydrogenase (hDHODH) is an enzyme which is strongly correlated with certain cancers and autoimmune and inflammatory diseases. Thromboxane A2 receptor (TP) promotes platelet aggregation when activated by thromboxane A2, but over activation of TP may lead to thrombosis and other cardiovascular diseases. Due to the importance of these two proteins related to many human diseases, we use them as targets to build the hypotheses based on a set of known inhibitors, and then use cost function analysis, Fischer’s randomization and goodness of hit test to validate the quality and the confidence of statistical significance of our models. The results show that our models have excellent prediction ability. According to the crystal structures have been solved or not, we can construct different workflows for hDHODH and TP. Consequently, the pharmacophore model, Lipinski’s Rule-of-Five and CDOCKER docking program were integrated into a workflow for the discovery of potential inhibitor candidates from database. Through these workflows, 155 candidates for hDHODH and 5,621 candidates for TP are retrieved for further study.

誌謝......................................................................i
中文摘要.................................................................ii
ABSTRACT................................................................iii
CONTENTS.................................................................iv
FIGURE CONTENTS..........................................................vi
TABLE CONTENTS..........................................................vii
CHAPTER I：Introduction
    1.1 Background....................................................1
    1.2 Computer-Aided Drug Design....................................1
    1.3 Quantitative Structure-Activity Relationship..................1
    1.4 Pharmacophore.................................................2
    1.5 Target Protein I: Human Dihydroorotate Dehydrogenase..........2
    1.6 Target Protein II: Thromboxane A2 Receptor....................5
CHAPTER II：Materials and Methods
    2.1 Workflow......................................................7
    2.2 Biological Data Collection....................................7
    2.3 Pharmacophore Model Generation................................8
    2.4 Cost Function Analysis........................................9
    2.5 Fischer’s Randomization......................................9
    2.6 Goodness of Hit Test.........................................10
    2.7 Integration of Screening and Docking.........................10
    2.8 Virtual Screening............................................11
    2.9 Docking......................................................11

CHAPTER III：Results and Discussion
    3.1 Results for Human Dihydroorotate Dehydrogenase
        3.1.1 Workflow for Human Dihydroorotate Dehydrogenase....12
        3.1.2 Biological Data....................................12
        3.1.3 Pharmacophore Generation Results...................12
        3.1.4 Cost Function Analysis Results.....................13
        3.1.5 Fischer’s Randomization Results...................14
        3.1.6 Goodness of Hit Test Results.......................14
        3.1.7 Screening Results..................................15
        3.1.8 Docking Results....................................15
    3.2 Results for Thromboxane A2 Receptor
        3.2.1 Workflow for Thromboxane A2 Receptor...............16
        3.2.2 Biological Data....................................17
        3.2.3 Pharmacophore Generation Results...................17
        3.2.4 Cost Function Analysis Results.....................18
        3.2.5 Fischer’s Randomization Results...................18
        3.2.6 Screening Results..................................19
CHAPTER IV：Conclusions and Future Work
    4.1 Conclusions..................................................20
    4.2 Future Work..................................................21
REFERENCES...............................................................22
APPENDIX.................................................................30
                                

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