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論文中文名稱:以受質與結構為基礎的藥物設計來開發新穎的組織胺N-甲基轉移酶抑制劑 [以論文名稱查詢館藏系統]
論文英文名稱:Discovery of Novel Human HNMT Inhibitors through Ligand and Structure-Based Drug Design [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:工程學院
系所名稱:工程科技研究所
畢業學年度:100
出版年度:101
中文姓名:P.Elumalai
英文姓名:Pavadai Elumalai
研究生學號:96679050
學位類別:博士
語文別:英文
口試日期:2012-01-13
論文頁數:162
指導教授中文名:劉宣良
口試委員中文名:劉慧貞;陳文義;何碩一;黄西峰
口試委員英文名:Hwai-Shen Liu;Wen-Yih Chen;Shao-Yi Hou;Chih-Hung Huang
中文關鍵詞:組織胺N-甲基轉移酶藥效基團模型虛擬篩選分子嵌合以結構為基礎以受質為基礎虛擬篩選分子嵌合共同評分函數
英文關鍵詞:histamine N-methyltransferasepharmacophore modelstructure-basedligand-basedvirtual screeningmolecular dockingconsensus score
論文中文摘要:組織胺,為一種生物胺,在人體組織中參與許多生理反應,如調節過敏反應、胃酸分泌、支氣管相關的氣喘及在中樞神經系統扮演神經傳導物質的角色,而組織胺可被組織胺N-甲基轉移酶去活化。組織胺N-甲基轉移酶分佈在多種組織中,如支氣管、腎臟及中樞神經系統。最近的研究指出,組織胺N-甲基轉移酶可針對神經退化性疾病、記憶、學習障礙與注意力不集中過動症作為有潛力的治療目標。因此,為了能了解抑制組織胺N-甲基轉移酶的必須化學特徵與開發出新穎的抑制劑,我們將分別利用最有活性的抑制劑、結構具多樣性的抑制劑群與解析度最高的組織胺N-甲基轉移酶結晶結構使用軟體Discovery Studio 2.5中的Hip-Hop、HypoGen及Ludi 演算法分別建構出common feature、以受質為基礎(ligand-based)及以結構為基礎(structure-based)的的藥效基團模型。從這三種方法架設出的藥效基團模型將分別進行Goodness-of-hit (GH) score的驗證,而所有的模型其分數值都大於0.7,指出可運用在虛擬篩選上並挑選出具潛力的藥物。我們選擇了NCI,Chembridge與Maybridge資料庫並以驗證過的藥效基團模型作為搜尋藥物的依據。隨後,從虛擬篩選後選擇出的化合物群將會以人類的組織胺N-甲基轉移酶結晶結構來進行分子嵌合。最後,我們提出了20個具潛力的抑制劑分子,結果都顯示具有好的預測活性質、好的結合作用力、高的藥效基團fit value及具有高的共同評分函數分數。本實驗所搜尋到的新穎化學結構分子將可作為新型骨幹的HNMT抑制劑分子設計使用。
論文英文摘要:Histamine, a biogenic amine, plays many important pathophysiological roles in human tissues such as mediator in allergic responses, a regulator of gastric acid secretion, a messenger in bronchial asthma, and a neurotransmitter in the central nervous system. The histamine is inactivated by histamine-metabolizing major enzyme histamine N-methyltransferase (EC 2.1.1.8; HNMT) in various tissues for example bronchus, kidney and the central nervous system. Importantly, it has been shown that HNMT is the only pathway for termination of the neurotransmitter action of histamine in the brain. Recently, the inhibition of HNMT has been demonstrated to play therapeutic roles in the treatment of neurodegenerative disease, memory and learning deficits and attention-deficit hyperactivity disorder. Thus, to better understand the essential chemical features for HNMT inhibition and discover novel HNMT inhibitors, common feature, ligand-based and structure-based pharmacophore models were, in this study, developed based on the most active inhibitors, structurally divers inhibitors and the highest resolution crystal structure of HNMT using Hip-Hop, HypoGen and Ludi algorithms of Discovery Studio 2.5 software, respectively. The best models were selected and the goodness-of-hit validation scores of three models showed more than 0.7, indicating that the pharmacophore models are efficient to identify novel hits using virtual screening techniques. The pharmacophore models were then employed as 3D-search query for virtual screening to retrieve potential hits from NCI, Chembridge and Maybridge databases. Subsequently, the hit compounds were subjected to molecular docking studies with the crystal structure of human HNMT. Finally, total of 20 hits were suggested as potential leads, which exhibited good estimated activities, favorable binding interactions, high pharmacophore fit values and high consensus scores. The obtained novel chemotype from these studies could facilitate to discover new scaffold in the development of novel HNMT inhibitors.
論文目次:CONTENTS
ABSTRACT...………………………………………………………………………….. ………….i
ACKNOWLEDGEMENTS...……………………………………………………………………..v
CONTENTS..................................................................................................................................vii
TABLE CONTENTS…………………………………………………………………………......xi
FIGURE CONTENTS…………………………………………………………………………..xiii


Chapter 1 GENERAL INTRODUCTION.......................................................................................1
1.1 Motivation………………………………………………………………………………..1
1.2 The investigating system-Histamine N-methyltransferase…………………………….....2
1.3 The specific aims of thesis……………….…………………………………………........4
1.4 The organization of thesis………………………………………………………….…….4
Chapter 2 REVIEW OF LITERATURE..........................................................................................6
2.1 Histamine………………………………………………………………………….…......6
2.1.1 Synthesis, storage and release of histamin……………………………….……….7
2.1.2 Metabolism of histamine.………………………………………….……………...8
2.1.3 Functions of histamine.…………………………………………………………...8
2.2 Histamine N-methyltransferase (HNMT)………………………………………………10
2.2.1 Function of HNMT.……………………………………………………………...11
2.2.1.1 Distributions and function of HNMT in CNS…………………………....12
2.2.1.2 Distributions and function of HNMT in peripheral tissues………...…....13
2.2.2 Structure of HNMT……………………………………………………………..15
2.2.3 HNMT polymorphism... ……………………………………………………….21
2.3 HNMT inhibitors…………………….............................................................................23
2.4 Pharmacological significances of HNMT inhibition……..…………………………….26
Chapter 3 METHODS……………………………………………………………………………28
3.1 Pharmacophore modeling……………………………………………………………....28
3.1.1 Ligand-based pharmacophore modeling………………………………...………30
3.1.2 Structure-based pharmacophore modeling……………………………………...32
3.2 Virtual Screening.............................................................................................................33
3.2.1 Ligand-based virtual screening………………………………………….............34
3.2.2 Structure-based virtual screening………………………………………………..35
3.3 Molecular docking……………………………………………………………………...36
3.3.1 LigandFit docking program……………………………………………………..38
3.4 Scoring functions of docking…………………………………………………………...40
3.4.1 Types of scoring functions………………………………………………………40
3.4.2 Ligand Scoring…………………………………………………………………..42
3.4.2.1 Jain scoring function……………………………………………………..43
3.4.2.2 LigScore1 scoring function………………………………………………43
3.4.2.3 LigScore2 scoring function………………………………………………45
3.4.2.4 Ludi scoring function…………………………………………….............46
3.4.2.5 Piecewise Linear Potential (PLP)……………………………….……….46
3.4.2.5.1 PLP1………………………………………………………............47
3.4.2.5.2 PLP2……………………………………………………………....48
3.4.2.6 Potential of Mean Force (PMF) …………………………………………50
3.5 Consensus scoring………………………………………………………………………51
Chapter 4 PHARMACOPHORE MODELING, VIRTUAL SCREENING AND DOCKING STUDIES TO IDENTIFY NOVEL HNMT INHIBITORS……………………..….52
4.1 Abstract…………………………………………………………………………………52
4.2 Introduction……………………………………………………………………………..53
4.3 Materials and methods………………………………………………………………….57
4.3.1 Pharmacophore modeling……………………………………………………….57
4.3.1.1 Data preparation………………………………………………………….57
4.3.1.2 Generation of pharmacophore hypotheses using HypoGen………...........65
4.3.1.3 Pharmacophore model validations……………………………………….66
4.3.2 Virtual screening………………………………………………………………...68
4.3.3 Molecular docking and Scoring.………………………………………………...69
4.4 Results and discussion…………………………………………………………….........71
4.4.1 Generation of 3D-pharmacophore model………………………………….........71
4.4.2 Evaluations of Hypo1 pharmacophore model…………………………………...78
4.4.3 Virtual screening………………………………………………………………...85
4.4.4 Docking studies and consensus scoring…………………………………………87
4.5 Conclusions……………………………………………………………………………..94
4.6 References........................................................................................................................94
Chapter 5 LIGAND AND STRUCTURE-BASED PHARMACOPHORE MODELING FOR THE DISCOVERY OF POTENTIAL HUMAN HNMT INHIBITORS.................101
5.1 Abstract………………………………………………………………………………..101
5.2 Introduction……………………………………………………………………………103
5.3 Materials and methods………………………………………………………………...105
5.3.1 Generation of ligand-based pharmacophore model…………………..……..…105
5.3.2 Generation of structure-based pharmacophore model…………………….…...107
5.3.3 Validation of pharmacophore models……………………………………….....108
5.3.4 Database screening…………………………………………………………......109
5.3.5 Molecular docking…………………………………………………………......110
5.4 Results and discussion…………………………………………………………….…...111
5.4.1 Generation of common-feature pharmacophore model………………………..111
5.4.2 Generation of structure-based pharmacophore model…………………….…...114
5.4.3 Validation of ligand-based and structure-based pharmacophore models……....116
5.4.4 Database screening……………………………………………………………..118
5.4.5 Molecular docking and consensus scoring…………………………………….121
5.5 Conclusions…………………………………………………………………………....129
5.6 References……………………………………………………………………………..129
Chapter 6 CONCLUSIONS AND FUTURE DIRECTIONS......................................................137
6.1 Conclusions…………………………………………………………………………....137
6.2 Future directions……………………………………………………………………....138
REFERENCES ………………………………………………………………………………....140

CURRICULUM VITAE ……..………………………………………………………………....159
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