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研究生:林郁珊
研究生(外文):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
論文名稱(外文):Design hDHODH Inhibitors and TP Inhibitors Using Pharmacophore Modeling and Virtual Screening Techniques
指導教授:唐傳義
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
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:pharmacophorecomputer-aided drug designdihydroorotate dehydrogenasethromboxane A2 receptorvirtual screening
相關次數:
  • 被引用被引用:0
  • 點閱點閱:126
  • 評分評分:
  • 下載下載:7
  • 收藏至我的研究室書目清單書目收藏:0
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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8. 以藥效基團及結構資訊虛擬搜尋新型鉀離子通道Kv1.3抑制劑
9. 以分子嵌合與共通評分函數預測蛋白質和配體的親合力及透過配體為基礎的藥效基團尋找新型藥物架構:於乙醯膽鹼酵素抑制劑之應用
10. 利用分子嵌合、藥效基團、虛擬篩選開發新型流感內切酶抑制劑
11. 虛擬篩選人類角鯊烯合成酵素之抑制分子
12. 新穎有效的對付登革病毒甲基轉移酵素之抑制分子
13. 針對惡性瘧原蟲DHODH的抑制劑建立藥效基團模型與三維定量構效關係分析
14. 產生基於配體的藥效基團模型並進行虛擬藥物篩選具有強效抗癌活性的新穎微管蛋白抑制劑之研究
15. 利用三維定量構效關係與虛擬篩選方式挑選出具COX-2專一性的抑制劑
 
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1. 運用藥效基團集虛擬篩選以探索表皮生長因子受體抑制劑之新化學結構
2. 利用中草藥化合物篩選酪胺酸酶抑制劑建立藥效基團模型
3. 以藥效基團及結構資訊虛擬搜尋新型鉀離子通道Kv1.3抑制劑
4. 發展三維定量構效關係組合模型架構用於搜尋與優化標靶蛋白質抑制劑基於Pharmacophore, CoMFA 和 CoMSIA 電腦技術
5. 針對酪胺酸酶抑制劑利用分子對接和Catalyst軟體進行3D-QSAR藥效基團研究
6. 利用電腦虛擬高速篩選與藥效基團評分來尋找抑制酪氨酸去磷酸酶的第二型糖尿病先導藥物
7. 對C型肝炎病毒NS3蛋白酶抑制劑產生化學作用力為主的藥效基團之研究
8. 利用PharmacophoreEnsemble/SupportVectorMachineApproach預測人類多重藥物傳送蛋白P-Glycoprotein的抑制活性
9. 針對惡性瘧原蟲DHODH的抑制劑建立藥效基團模型與三維定量構效關係分析
10. 產生基於配體的藥效基團模型並進行虛擬藥物篩選具有強效抗癌活性的新穎微管蛋白抑制劑之研究
11. 針對farnesyl轉移酶抑制劑的分子利用3D-QSAR技術建立藥效基團模型(Applying3D-QSARtechniquetoconstructthepharmacophoremodeloffarnesyltransferaseinhibitors)
12. Design of a Real-Time Bus Tracer for OCP-Based Systems
13. 利用分子嵌合、藥效基團、虛擬篩選開發新型流感內切酶抑制劑
14. 利用Pharmacophore Ensemble/Support Vector Machine方法預測ABCG2抑制劑結合親和力
15. GASP、DISCOtech、Galahad、PharmaGist和HipHop藥效基團模型軟體的比較與分析