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論文中文名稱:以藥效基團、定量構效關係與虛擬篩選來設計新型非洲嗜睡症抑制劑 [以論文名稱查詢館藏系統]
論文英文名稱:Discovery of novel kDNA inhibitors by Pharmacophore, QSAR modeling 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:化學工程研究所
畢業學年度:100
出版年度:101
中文姓名:夏漢聲
英文姓名:Hand-Some Hsia
研究生學號:99738042
學位類別:碩士
語文別:英文
口試日期:2012-07-20
論文頁數:115
指導教授中文名:劉宣良
口試委員中文名:黃志宏;何意;蔡偉博
中文關鍵詞:非洲嗜睡症錐蟲藥效基團定量構效關係虛擬篩選分子嵌合分子動態模擬
英文關鍵詞:Human African trypanosomiasis3D QSAR pharmacophoreCoMFAvirtual screeningCDockerMD simulation
論文中文摘要:非洲嗜睡症是由一種微小的原生動物-錐蟲所引起,寄生在動物的血液中;錐蟲會經體表進入人體血液,當發育到一定程度時會沿著人的循環系統侵入腦脊髓,使人發生昏睡而發病。然而現階段有關此類疾病的治療藥物非常少,即使是有效的藥物,開發至今也都已經超過40年,而且都具有強大的副作用。為了深入了解真正的藥效機制與研發新型不具副作用的藥物,我們使用了電腦輔助藥物設計元件中的藥效基團、比較力場分子分析、虛擬篩選、分子嵌合與分子動態模擬來進行本研究。首先,我們分別以44個與34個錐蟲體內粒線體之特殊DNA序列抑制劑分別架設藥效基團以及定量構效關係模型。在藥效基團的模型方面,我們以Hypogen法來架設模型並使用線性迴歸法以及Guner–Henry (GH) 評分方式進行驗證。結果顯示,藥效基團模型(Hypo1)有最高的線性迴歸係數(R=0.93),並有高的GH評分值(0.82),證明此模型具有良好的藥物活性預測能力,故我們以此模型進行了NCI化學資料庫的虛擬篩選,篩選出的化合物在藉由分子嵌合與分子動態模擬實驗評估其結合力強弱,最後,我們依據藥物對藥效基團的吻合度、化合物對目標DNA的氫鍵結合力強度與和分子動態模擬分析結果選出了一個能作為未來藥物開發上可能的前導藥物。在定量構效關係方面,我們使用了3D-QSAR generation模組進行模型建立並以線性迴歸法及交叉驗證法作為其驗證方式。實驗結果發現,第一組比較力場分子分析模型最具可靠的迴歸值(R2=0.95),而經由交叉驗證法所得到的值(q2 = 0.57)都証明了此模型有很高的可信度,故我們根據此比較力場分子分析模型所提供的結構資訊作為依據分別設計了8種化合物分子,並藉由分子嵌合與預測活性計算出這些化合物都具備高度的結合活性。在本研究中,我們成功的利用多種電腦輔助藥物設計元件進行了錐蟲體內粒線體之特殊DNA序列之藥物開發實驗,而實驗中搜尋與開發出的化合物分子期望在將來可以進一步的進行體外生物實驗來測試實際的藥物活性。
論文英文摘要:Human African trypanosomiasis (HAT) or sleeping sickness is caused by subspecies of the parasitic hemoflagellate Trypanosoma brucei. The chemotherapy of HAT currently centers on only small numbers of drugs, most of these were discovered more than forty years, and are plagued by various side effects. In quest of possible ways to understand the structural requirement for anti-trypanosomal mechanism and design novel compounds, the Hypogen 3D-QSAR pharmacophore analysis, CoMFA, virtual screening, docking and molecular dynamics can be powerful tools, which had the ability to design novel chemical entities with enhanced inhibitory potencies against Trypanosome brucei. The best pharmacophore model Hypo1 shows the highest correlation coefficient (R=0.93), and also shows a high goodness of GH score (0.82). After that, Hypo1 is used as a 3D query for virtual screening to discover potential inhibitors from NCI database. We then used CDocker and MD simulation program to analyze virtual hits. Finally, only one compound was remained which will be possible to view as a persuasive Trypanosomal inhibitor. In CoMFA studies, a good values of R2=0.95 from training set and promising predictive power from cross validation (q2=0.57) were obtained. Based on the model suggested steric and electrostatic interactions, we designed 8 novel compounds and analyzed their interaction and binding poses by CDocker consensus scoring function. Molecular modeling and CoMFA analysis were performed to obtain useful information about the structural requirements for the HAT inhibitors which could be utilized in its future design.
論文目次:ABSTRACT i
ACKNOWLEDGEMENTS v
CONTENTS vi
TABLE CONTENTS x
FIGURE CONTENTS xi
Chapter 1 GENERAL INTRODUCTION 1
Chapter 2 LITERATURE REVIEW 3
2.1 Human African Trypanosomiasis 3
2.1.1 Treatment of Human African Trypanosomiasis 5
2.1.2 Drugs Used in Hemolymphatic Stage 5
2.1.3 Drugs Used in the Neurological Stage 6
2.1.4 Drugs Entered into Clinical Trials 8
2.1.5 Combination Chemotherapy 9
2.2 The Code of Life 10
2.3 DNA Groove Binders 12
2.3.1 DNA Minor Groove Targeted Therapeutic Agents 13
2.3.2 DNA Binding Antibiotics 14
2.3.3 Synthetic DNA Binding Compounds 16
Chapter 3 MOLECULAR MODELING 23
3.1 Molecular docker 23
3.1.1 Docking programs 24
3.1.1.1 CDOCKER algorithm 24
3.2 Scoring function for docking 27
3.2.1 The types of Scoring functions 28
3.2.2 Scoring functions 29
3.2.2.1 Jain scoring function 30
3.2.2.2 LigScore1 scoring function 31
3.2.2.3 LigScore2 scoring function 32
3.2.2.4 Ludi scoring function 33
3.2.2.5 Piecewise Linear Potential (PLP) 34
3.2.2.6 PLP1 34
3.2.2.7 PLP2 36
3.2.2.8 Potential of Mean Force (PMF) 37
3.3 Pharmacophore design model 38
3.4 Virtual screening 40
3.5 CoMFA 40
3.5.1 Steps of a CoMFA 43
3.6 Molecular dynamics (MD) 45
Chapter 4 Discovery of novel Trypanosoma brucei inhibitors by pharmacophore modeling, molecular docking and virtual screening 47
4.1 Abstract 47
4.2 Introduction 48
4.3 Materials and methods 51
4.3.1 Collection of dataset 51
4.3.2 Pharmacophore model generation 54
4.3.3 Validation of the pharamacophore model 55
4.3.4 Database screening 56
4.3.5 Molecular docking 56
4.3.6 Consensus score 57
4.3.7 Molecular dynamics 57
4.4 Result and Discussion 58
4.4.1 Pharmacophore studies 58
4.4.2 Pharmacophore validation 61
4.4.2.1 Test set prediction 61
4.4.2.2 GH score validation 61
4.4.3 Molecular docking results 62
4.4.3.1 Scoring calculation 64
4.4.4 Database screening 65
4.4.5 Interactions analysis of hit compounds 66
4.4.6 MD simulations 69
4.5 Conclusions 71
4.6 Reference 72
Chapter 5 Pharmacophore Guided 3D-QSAR CoMFA Analysis of diamidine as mitochondrial kinetoplast DNA Inhibitors 77
5.1 Abstract 77
5.2 Introduction 78
5.3 Methods 80
5.3.1 Data Set 80
5.3.2 Structure based pharmacophore modeling studies 82
5.3.3 Alignment Rule 83
5.3.4 CoMFA Models Generation 84
5.3.5 Partial Least Square Analysis (PLS) 84
5.3.6 Molecular modeling 85
5.3.7 Scoring functions 86
5.4 Results 87
5.4.1 Structure based pharmacophore modeling studies 87
5.4.2 Pharmacophore Evaluation 88
5.4.3 Molecular docking results 89
5.4.4 Scoring calculations 90
5.4.5 Alignment of Molecules in the Training and Test Sets 91
5.4.6 CoMFA Results 92
5.4.7 CoMFA Contour Maps Analysis 92
5.4.8 Designing of New Analogues 94
5.5 Conclusions 97
5.6 References 98
Chapter 6 GENERAL CONCLUSIONS 101
Chapter 7 GENERAL REFERENCES 103
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