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論文中文名稱: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 [以論文名稱查詢館藏系統]
論文英文名稱:Molecular docking combined with a consensus scoring function to predict protein-ligand affinity and ligand-based pharmacophore search for new drug scaffolds: an application for acetylcholinesterase inhibition [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:生物科技研究所
畢業學年度:99
出版年度:99
中文姓名:呂欣樺
英文姓名:Shin-Hua lu
研究生學號:97688006
學位類別:碩士
語文別:英文
口試日期:2009-07-30
論文頁數:126
指導教授中文名:劉宣良
口試委員中文名:黃志宏;蔡偉博;林忻怡
中文關鍵詞:乙醯膽鹼酵素抑制劑阿茲海默症分子嵌合共通評分函數芳香環間作用力藥效基團虛擬搜尋
英文關鍵詞:Acetylcholinesterase inhibitorsAlzheimer’s diseasemolecular dockingconsensus scoring functionπ-π interactionpharmacophorevirtual screening
論文中文摘要:阿茲海默症是一種於老年人中最常發生之神經退化性疾病。罹患疾病時病人會出現認知上的缺失且伴隨慢性神經退化症狀。包括逐漸失去記憶、認知功能退化和降低腦容量,症狀開始後大概八至十年病人會死亡。這類疾病常伴隨著中樞神經系統膽鹼神經傳導物質失去作用,因此,大部分治療阿茲海默症的藥物為乙醯膽鹼酵素抑制劑,它能增加突觸間神經傳導物質乙醯膽鹼的含量。本篇研究將利用分子嵌合實驗和評分函數方法來評估88個配體的親和性,此88個配體將被分成一個包含68個配體的訓練組和一個包含20個配體的測試組,我們的結果將被用來進一步描述抑制劑和乙醯膽鹼酵素結合區域的特性。我們的結果顯示訓練組變異量(R2)為0.8439,測試組變異量為0.9573,並利用單個抽出交叉確認法驗證預測結果是否在可接受範圍內,而此所得到的結果(Q2)為0.6291,顯示本實驗所開發出來的共通評分函數適合應用於探討乙醯膽鹼酵素抑制過程中之分子結構特色和其生物活性的預測。由以上得到的結果可以明確推測配體和受體之間的作用力,其中包含了在主要活性位置、凹陷區域和乙醯結合部分形成重要的氫鍵作用力、苯環間作用力和疏水作用力。我們更進一步地以抑制劑為基礎,建立化學藥效基團模型,並應用於乙醯膽鹼酵素抑制劑的虛擬藥物篩選。透過這些藥效模型及電腦虛擬藥物篩選技術,可搜尋出幾個具有新型藥物骨架的乙醯膽鹼酵素抑制劑。本研究的結果將可應用於日後設計新型並更具活性之乙醯膽鹼酵素抑制劑以達到臨床應用之目的。
論文英文摘要:Alzheimer’s disease is the most common cause of dementia characterized by progressive cognitive impairment in the elderly. It is a chronic, slowly progressive neurodegenerative disorder. The gradual loss of memory, decline in other cognitive functions, and decrease in functional capacity result in death approximately 8-10 years after the onset of the symptoms. It is accompanied by dysfunctions in the cholinergic neurotransmission of the central nervous system. Hence, most of the drugs approved for AD treatment are acetylcholinesterase inhibitors (AChEIs), which can enhance cholinergic neurotransmission by increasing acetylcholine availability in the synaptic cleft. In this study, molecular docking experiments combined with a consensus scoring function were conducted to predict the binding affinities of a total of 88 AChEIs, in which 68 and 20 compounds were used in the training and test sets, respectively, and to characterize the structural features of the catalytic gorge of acetylcholinesterase (AChE) toward binding. Our results yielded correlation coefficients R2 = 0.8439 and 0.9573 for the training and test sets, respectively, after partial least squares regression and leave-one-out cross-validation coefficient Q2 = 0.6291, indicating that the consensus scoring function developed here is applicable to bioactivity prediction and structural characterization for AChE inhibition. The identification of the protein-ligand interactions produces a list of those residues within the dual binding site of AChE, which make the most important hydrogen bond,
論文目次:ABSTRACT i
ACKNOWLEDGEMENTS v
CONTENTS vi
TABLE CONTENTS viii
FIGURE CONTENTS ix
Chapter 1 GENERAL INTRODUCTION 1
Chapter 2 LITERATURE REVIEW 3
2.1 Alzheimer’s disease (AD) 3
2.1.1 The amyloid cascade hypothesis 3
2.1.2 The tau protein hypothesis 6
2.1.3 The cholinergic hypothesis 8
2.2 Acetylcholinesterase (AChE) 10
2.2.1 Crystal structure Acetylcholinesterase 11
2.2.2 Amyloid – cholinesterase interactions 13
2.2.3 AChE inhibitors 16
Chapter 3 MOLECULAR MODELING 20
3.1 Overview 20
3.2 Docking 21
3.2.1 Docking programs 22
3.2.1.1 CDOCKER algorithm 22
3.2.1.2 DOCK algorithm 24
3.2.1.3 FlexX algorithm 24
3.2.1.4 GOLD algorithm 25
3.2.1.5 LibDock 25
3.3 Pharmacophore design model 27
3.4 Virtual screening 28
Chapter 4 Molecular docking combined with a consensus scoring function to predict protein-ligand affinity: an application for acetylcholinesterase inhibition 29
4.1 Abstract 29
4.2 Introduction 29
4.3 Methods 32
4.3.1 Data set and biological activity 32
4.3.2 Molecular docking experiments 41
4.3.3 Scoring functions 42
4.4 Result and Discussion 43
4.4.1 The performance of the consensus scoring function 43
4.4.2 Interactions between AChEIs and the huAChE binding sites 52
4.5 Conclusions 59
4.6 Abbreviations 59
4.7 References 60
Chapter 5 The discovery of potential acetylcholinesterase inhibitors: a combination of pharmacophore modeling, virtual screening, and molecular docking studies 68
5.1 Abstract 68
5.2 Introduction 69
5.3 Methods 72
5.3.1 Data preparation 72
5.3.2 Pharmacophore model generation 82
5.3.3 Validation of the pharamacophore model 82
5.3.4 Virtual screening 83
5.3.5 Molecular docking 84
5.4 Results 85
5.4.1 Construction of pharmacophore model 85
5.4.2 Model validation 88
5.4.3 Database screening 92
5.4.4 Molecular docking studies of AChE 93
5.5 Discussion 97
5.6 Conclusions 102
5.7 References 103
Chapter 6 GENERAL CONCLUSIONS 111
Chapter 7 GENERAL REFERENCES 113
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論文全文使用權限:同意授權於2016-01-25起公開