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論文中文名稱:利用藥效基團、三維定量構效關係、分子動態模擬及虛擬篩選來開發阿茲海默症的潛在藥物 [以論文名稱查詢館藏系統]
論文英文名稱:Discovery of potential drugs for Alzheimer’s disease by pharmacophore modeling, 3D-QSAR modeling, molecular dynamics simulations and virtual screening [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:生化與生醫工程研究所
畢業學年度:101
出版年度:102
中文姓名:黃孝文
英文姓名:Siao-Wun Huang
研究生學號:100688028
學位類別:碩士
語文別:中文
口試日期:2013-07-31
論文頁數:121
指導教授中文名:劉宣良
指導教授英文名:Hsuan-Liang Liu
口試委員中文名:黃志宏;何意;蔡偉博
中文關鍵詞:阿茲海默症tau蛋白乙型分泌酵素三維定量構效關係藥效基團分子動態模擬虛擬篩選
英文關鍵詞:tau proteinbeta-secretase (BACE1)3D-QSARpharmacophoremolecular dynamics simulationsvirtual screening
論文中文摘要:阿茲海默症為一種最常見的慢性神經退化性疾病,其臨床症狀為大腦中的認知功能區域產生缺失與皮質層發生萎縮,並且在患者腦部發現神經元纖維纏繞和?澱粉斑塊堆積。至今,尚缺乏真正能有效治療阿茲海默症的藥物。而過去的研究指出tau蛋白與乙型分泌酵素對於阿茲海默症皆扮演重要的角色。為了開發更具潛力的阿茲海默症藥物,我們採用多種電腦輔助藥物設計模組包含了藥效基團、三維定量構效關係、分子嵌合、分子動態模擬及虛擬篩選來進行本次實驗。針對tau蛋白,我們利用目前最有效的胜肽抑制劑所嵌合後具有代表性的構形來架設以結構為基礎的藥效基團模型,接著我們以此模型用作三維的方式進行了中草藥化學資料庫的虛擬篩選,篩選出的化合物再藉由分子動態模擬的實驗評估其結合穩定性,最後,我們找出了三個能作為未來藥物開發上可能的前導藥物,其具有與最有效的胜肽抑制劑相似的結合親和力。至於乙型分泌酵素方面,我們用9種不同的乙型分泌酵素-乙型分泌酵素抑制劑複合體的結晶結構來架設以多種結構為基礎的藥效基團模型,經過Guner-Henry評分方法驗證後,我們運用此模型以虛擬篩選模組進行中草藥化學資料庫的篩選並用於疊合結構多樣性的乙型分泌酵素抑制劑,最後再經由三維定量構效關係的分析及分子嵌合的方法來篩選出兩個具有潛力的前導藥物。而本實驗所挑選出來的化合物可以應用於日後設計新型並更具有抑制活性的阿茲海默症藥物來達到臨床上的應用。
論文英文摘要:Alzheimer's disease (AD) is the most common progressive chronic neurodegenerative disorder characterized by loss of neurones particularly in those regions associated with cognitive functions and cortical atrophy. Neuropathological hallmarks include neurofibrillary tangles (NFTs) and amyloid-beta plaques. To date, no truly effective therapy drugs has been developed for AD. Previous studies show that tau protein and beta-secretase (BACE1) are two predominant targets for anti-AD drugs. In this study, we applied many computational approaches including pharmacophore modeling, 3D-QSAR modeling, molecular docking, molecular dynamics (MD) simulations and virtual screening to discover more potential anti-AD drugs. For tau protein, we constructed structure-based pharmacophore model that was developed using the representative docked conformations from the most effective peptide inhibitors. This model was subsequently used as a 3D-query in virtual screening to identify potential hits from Traditional Chinese Medicine (TCM) database. The binding stabilities of these hits were further validated using molecular dynamics simulations. Finally, only three compounds were identified as potential leads, which exhibited similar binding affinities in comparison to the most effective peptide inhibitor for tau protein. As to BACE1, we constructed multicomplex-based pharmacophore model by a collection of 9 crystal structures of BACE1-inhibitor complex. This model was validated by Guner-Henry (GH) scoring methods, applied to screen the TCM database and and to align the structurally diverse BACE1 inhibitors. Then, 3D-QSAR analysis and molecular docking were conducted to retrieve two potential lead compounds. In summary, the results of this study can be applied to the design of new and more potent anti-AD drugs for clinical purposes.
論文目次:摘要 i
ABSTRACT iii
ACKNOWLEDGEMENTS v
CONTENTS vi
Chapter 1 GENERAL INTRODUCTION 1
Chapter 2 LITERATURE REVIEW 3
2.1 Alzheimer’s Disease 3
2.1.1 Pathophysiology of Alzheimer's Disease 4
2.1.2 Aggregation of amyloid fibrils 6
2.2 Tau protein 11
2.2.1 Tauopathies 13
2.2.2 Hyperphosphorylation and aggregation of tau protein 15
2.2.3 Treatment strategy for tau protein 17
2.3. Amyloid ? 18
2.3.1 Amyloid Precursor Protein (APP) 19
2.3.2 ?-secretase (BACE-1) 21
Chapter 3 MOLECULAR MODELING 22
3.1 Molecular docker 22
3.1.1 Docking programs 23
3.1.2 CDOCKER algorithm 23
3.2 Scoring function for docking 26
3.2.1 The types of Scoring functions 27
3.2.2 Scoring functions 28
3.2.2.1 Jain scoring function 29
3.2.2.2 LigScore1 scoring function 30
3.2.2.3 LigScore2scoring function 31
3.2.2.4 Ludi scoring function 32
3.2.2.5 Piecewise Linear Potential (PLP) 33
3.2.2.6 PLP1 33
3.2.2.7 PLP2 35
3.2.2.8 Potential of Mean Force (PMF) 36
3.3 Force Field 37
3.3.1 The Parameters in the Force Field 39
3.3.2 Functional Form of CVFF Force Filed 45
3.4 Pharmacophore design model 47
3.5 Molecular Dynamics 48
3.5.1 Constraints During Dynamics Simulations 49
3.5.1.1 The NVE Ensemble 50
3.5.1.2 The NPT Ensemble 51
3.5.1.3 The NVT Ensemble 51
3.6 CoMFA 52
3.6.1 Steps of a CoMFA 54
Chapter 4 Identification of non-peptide inhibitors against tau protein aggregation from TCM database by pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation 56
4.1 Abstract 56
4.2 Introduction 57
4.3 Material and Methods 60
4.3.1 Preparation of the TLKIVW peptide inhibitor and the 306VQIVYK311oligomer 60
4.3.2 Identification of the possible TLKIVW binding sites on the 306VQIVYK311oligomer by rigid-body docking………………....................61
4.3.3 Identification of the potential binding modes by CDOCKER and consensus scoring 62
4.3.4 Evaluation of the binding stability by MD simulation 63
4.3.5 Generation and validation of the structure-based pharmacophore model 65
4.3.6 Database screening 66
4.4 Results and Discussion 68
4.4.1 TLKIVW binding sites and modes on 306VQIVYK311 oligomers 68
4.4.2 Generation of structure-based pharmacophore 71
4.4.3 Validation of the structure-based pharmacophore model 72
4.4.4 Virtual screening 74
4.4.5 MD simulations – Overall structural flexibility and stability 75
4.5 References 79
Chapter 5 GENERAL CONCLUSIONS 85
Chapter 6 GENERAL REFERENCES 87
Appendix 101
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