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論文中文名稱: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 [以論文名稱查詢館藏系統]
論文英文名稱:Pharmacophore- and structure-based virtual screening of potential Kv1.3 inhibitors with new scaffolds [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:100
中文姓名:古文昕
英文姓名:Wen-Xin Ku
研究生學號:98688002
學位類別:碩士
語文別:英文
口試日期:2011-07-13
論文頁數:149
指導教授中文名:劉宣良
口試委員中文名:劉懷勝;黃志宏
中文關鍵詞:鉀離子通道Kv1.3藥效基團藥物篩選分子嵌合共通評分函數
英文關鍵詞:Potassium channelKv1.3homology modelpharmacophorevirtual screeningconsensus scoring function
論文中文摘要:T細胞調節的異常會導致多發性硬化症、第一型糖尿病及多項自體免疫疾病,目前治療的方向為抑制記憶T細胞過度分化來達到治療的目的。除了鈣活化型鉀離子通道3.1 (KCa3.1) 之外,電位控制型鉀離子通道1.3 (Kv1.3)在最近也被廣泛討論並能作為治療自體免疫性疾病之目標。為了能夠搜尋出新型並不帶有肝臟毒性的可能藥物,利用藥效基團為基礎的虛擬藥物篩選的方式可以從現有的化合物資料庫中快速的搜尋出可能的藥物分子。本研究共收集106個Kv1.3的抑制劑來建構藥效基團模型。我們使用了線性迴歸法與交叉驗證法來進行藥效基團模型的驗證。結果顯示,藥效基團模型(Hypo3-3)有最高的測試組回歸係數(R2 = 0.763),其藥效基團模型包含了兩個氫鍵接受者基團、一個疏水性基團及一個芳香環基團。並透過95%信心指數的交叉驗證法下更進一步的驗證此模型的藥物活性預測能力。通過驗證後的藥效基團模型我們將會進行NCI化學資料庫的虛擬篩選,依據藥物對藥效基團模型的吻合度(fit value)前10名且具不同骨幹結構(scaffold)的化合物將作為未來藥物開發上可能的前導藥物。另一方面,我們也以結構為基礎的虛擬藥物篩選方式來篩選可抑制Kv1.3的化合物。我們首先利用最高解析度的Kv1.2 結晶結構以同源模擬的方式模擬建構出Kv1.3的結構,並以5-(4-phenoxybutoxy)psoralen (PAP-1), 5-(4-phenylbutoxy)psoralen (Psora-4)及7-substituted khellinones等對Kv1.3具有選擇性的抑制劑透過分子嵌合來建立具高度預測活性的共通評分函數。實驗中會將已知資料庫的化合物分子進行分子嵌合實驗,能嵌合上Kv1.3的化合物分子會再以共同評分函數進行藥物活性的預測,決定出具潛力的Kv1.3抑制劑。藉由以藥效基團及結構為基礎的虛擬藥物篩選方法所篩選出來具潛力的化合物,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:In addition to KCa3.1, Kv1.3 has been regarded as a promising target for the selective inhibition of terminally differentiated effector memory T (TEM) cell in T cell-mediated autoimmune diseases, such as multiple sclerosis and type 1 diabetes. In this study, pharmacophore-based virtual screening was used to search for new lead compounds with new scaffolds that can serve as potential drugs without causing liver toxicity. A total of 106 Kv1.3 inhibitors, which were collected from published literatures, were used to build pharmacophore model. The best pharmacophore model (Hypo3-3), containing of two hydrogen bond acceptor, one hydrophobic, and one aromatic ring, has the highest R2 value (0.763) for the test set. The cross validation method with 95% confidence level was further used to validate Hypo3-3 and proved that this model was reliable in identifying structurally diverse compounds for Kv1.3 inhibition. This model was then employed as a filter to search for lead compounds with new scaffolds from the NCI chemical database. Top 10 hit compounds, selected based on their fit values, were found to share different conformations. On the other hand, the potent and selective Kv1.3 inhibitors can be retrieved by structure-based virtual screening. In the beginning, the Kv1.3 structure was build by homology modeling using the highest resolution structure of Kv1.2 as the template. Then, PAP-1, Psora-4 and 7-substituted khelliones inhibitors with selectivity for Kv1.3 were collected to construct a consensus scoring function with highly predicative ability by molecular docking. After docking ligand from commercially available database, this consensus scoring function was used to discover the novel selective Kv1.3 inhibitors. The resulting hit compounds from both ligand- and structure-based virtual screening can be applied in further in vitro biological evaluation and optimization.
論文目次:ABSTRACT i
ACKNOWLEDGEMENT v
CONTENTS vii
TABLE CONTENTS x
FIGURE CONTENTS xiii
Chapter 1 GENERAL INTRODUCTION 1
Chapter 2 LITERATURE REVIEW 3
2.1 Introduction of Autoimmune disease 3
2.1.1 Terminally differentiated effector memory T (TEM) cells 3
2.1.2 TEM cells inhibition 6
2.2 Potassium channel 6
2.3 Channel Classification and Functions 7
2.3.1 Six Transmembrane One-Pore Channels 9
2.3.1.1 Pore and Selectivity Filter 10
2.3.1.2 Voltage Sensor and Channel Activation 10
2.3.1.3 Inactivation 10
2.3.2 Two Transmembrane One Pore Channels 11
2.3.3 Four Transmembrane Two-Pore Channels 11
2.4 Crystal structure of Kv1.2 11
2.5 Potassium Channel inhibitors 12
2.5.1 Peptides 15
2.5.1.1 Classifiy of Peptides 16
2.5.1.2 Docking site of Peptides 17
2.5.2 Small Molecules 18
2.5.2.1 The major binding site of Kv Channels 20
2.5.2.2 The docing site of Kv1.3 21
Chapter 3 MOLECULAR MODELING 24
3.1 Overview 24
3.2 Pharmacophore design model 25
3.3 Homology modeling 27
3.4 Molecular Dynamics 30
3.4.1 Force Fields 31
3.4.1.1 Overview Several Classical Force Fields 31
3.4.1.2 The Parameters in the Force Field 33
3.4.1.3 Functional Form of the CHARMm Force Field 39
3.4.2 Minimization 40
3.4.3 Equilibration 43
3.4.4 Molecular Dynamics 44
3.5 Docking 46
3.5.1 Docking programs 47
3.5.1.1 LibDock 47
3.6 Scoring function for docking 48
3.6.1 The types of Scoring functions 49
3.6.2 Scoring functions 50
Chapter 4 3D pharmacophore based virtual screening of potential Kv1.3 inhibitors with different structural scaffolds for the treatment of autoimmune diseases 52
4.1 Abstract 52
4.2 Introduction 53
4.3 Methods 56
4.3.1 Data set 56
4.3.2 Conformation analysis 66
4.3.3 Generation of pharmacophore models 66
4.3.4 Validation of pharmacophore models 68
4.3.5 Virtual screening 68
4.4 Results and discussion 69
4.4.1 Pharmacophore generation 69
4.4.2 The best pharmacophore model selection 75
4.4.3 Fisher’s randomization test 82
4.4.4 Virtual screening 83
4.5 Conclusions 89
4.6 Reference 90
Chapter 5 Discovery of Potential Inhibitors to Kv1.3 via Homology modeling and Structure-Based Virtual screening 95
5.1 Abstract 95
5.2 Introduction 96
5.3 Methods 100
5.3.1 Homology modeling and MD simulation 100
5.3.2 Data set for molecular docing 101
5.3.3 Molecular docking experiments 104
5.3.4 Scoring function 105
5.3.5 Consensus scoring function validation and decoys definition 106
5.3.6 Structure-based Virtual screening 108
5.4 Results and discussion 108
5.4.1 Homology modeling 108
5.4.2 Molecular Dynamic and Docking experiment 110
5.4.3 The performace of consensus scoring function 112
5.4.4 The validation of the consensus scoring function in virtual screening 117
5.4.5 Structure-based virtual screening 119
5.5 Conclusions 125
5.6 Reference 126
Chapter 6 GENERAL CONCLUSIONS 134
Chapter 7 GENERAL REFERENCES 136
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