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論文中文名稱:利用分子嵌合、藥效基團、虛擬篩選開發新型流感內切酶抑制劑 [以論文名稱查詢館藏系統]
論文英文名稱:The discovery of novel influenza endonuclease inhibitors by molecular docking, pharmacophore 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:99
出版年度:100
中文姓名:李品佑
英文姓名:Pin-Yu Li
研究生學號:98738029
學位類別:碩士
語文別:英文
口試日期:2011-07-13
論文頁數:127
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;劉懷勝
口試委員英文名:Chih-Hung Huang;Hwai-Shen Liu
中文關鍵詞:流感內切酶分子嵌合共通評分函數接受者操作特徵藥效基團藥物篩選
英文關鍵詞:influenza endonucleasemolecular dockingconsensus scoring functionreceiver-operating characteristic (ROC)pharmacophorevirtual screening
論文中文摘要:流感A型病毒因為具有快速繁殖、突變頻繁、並且偶發地跨物種傳遞是造成人類以及動物傷亡的一個主要因素。最近對全世界造成緊急危害的高度致病豬流感顯示出對於新型有效治療方法的迫切需求。流感內切酶是一個對抗流感病毒感染的治療標靶。在本研究中,為了設計新穎具有更強生物活性的抑制劑來對抗流感,我們使用了49個現有的流感內切酶抑制劑,藉由分子嵌合方法來探討抑制劑與流感內切酶活性的關係。再者,結合了11種不同的內建評分函數,藉由偏最小平方迴歸法,我們建造一個新穎的共通評分函數。此評分函數有著實驗組迴歸係數(R2)為0.827,驗證組迴歸係數為0.855的高度相關性,能夠成功地預測一群活性差異甚大的抑制劑之pIC50 (-log IC50)值。除此之外,經由接受者操作特徵(ROC)曲線的驗證,此評分函數可應用於電腦虛擬藥物篩選及設計。經由我們的作用力能量分析,顯示出胺基酸Lys137和Lys134對於抑制劑活性是相當重要的,可以被使用來做抑制劑修飾以得到更高抑制能力的抑制劑。我們更進一步地以配體為基礎的方法來建立化學藥效基團,並應用於流感內切酶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,將可定義出具有新型骨架的流感內切酶抑制劑。接下來,分子嵌合及共通評分函數將用以預測這些藥物的活性。本研究的結果將可應用於日後設計新型並更具活性的抑制劑來達到臨床上的應用。
論文英文摘要:Influenza A virus, a major cause of human and animal loss, reproduces rapidly, mutates frequently and occasionally crosses species barriers. The recent emergence around the world of pig influenza related to highly pathogenic forms of the human virus has emphasized the urgent need for new effective therapies. Influenza endonuclease is an attractive target of antiviral therapy for influenza infections. In this study, molecular docking was used to dock 49 influenza endonuclease inhibitors into the active site of the influenza endonuclease with the purpose of designing a novel antiviral agent having enhanced biological activities against the enzyme. A novel consensus scoring function was constructed by combining 11 different scoring methods after partial least squares regression. This consensus scoring function was able to successfully estimate the pIC50 (-log IC50) value of a wide range of ligands with the correlation coefficients (R2) of 0.827 and 0.855 for the training and test sets, respectively. This function was further validated by the receiver-operating characteristic (ROC) curve. The results showed that the consensus scoring function developed here is applicable in virtual screening and for future in silico drug design. Our interaction energy analysis also suggested that Lys137 and Lys134 are significant for the inhibitory activity and can be used to modify the inhibitors for higher inhibiting capabilities. Furthermore, a ligand-based approach was performed to establish a pharmacophore model for virtual screening of novel influenza endonuclease inhibitors. Docking and consensus scoring were conducted to predict the estimated activity of these compounds. The results of this study can be applied to the design of new and more potent influenza endonuclease inhibitors for clinical purposes.
論文目次:ABSTRACT i
ACKNOWLEDGEMENT v
CONTENTS vi
TABLE CONTENTS x
FIGURE CONTENTS xi
Chapter 1 GENERAL INTRODUCTION 1
Chapter 2 LITERATURE REVIEW 3
2.1 Influenza virus 3
2.1.1 The structure of influenza virus 4
2.1.2 Influenza virus replication cycle and sites of action of antiviral agents 5
2.1.3 The development of anti-influenza drugs 7
2.2 Influenza RNA polymerase 9
2.2.1 Introduction of Polymerase Acidic protein (PA) 9
2.2.2 Introduction of Polymerase Basic protein 1 (PB1) 13
2.2.3 Introduction of Polymerase Basic protein 2 (PB2) 13
2.3 Influenza endonuclease 15
2.3.1 Influenza endonuclease inhibitors 16
Chapter 3 MOLECULAR MODELING 20
3.1 Molecular docking 20
3.1.1 Docking programs 22
3.1.1.1 CDOCKER algorithm 22
3.1.1.2 DOCK algorithm 25
3.1.1.3 FlexX algorithm 26
3.1.1.4 GOLD algorithm 27
3.1.1.5 LibDock 28
3.2 Scoring function for docking 29
3.2.1 The types of Scoring functions 29
3.2.2 Scoring functions 31
3.2.2.1 Jain scoring function 32
3.2.2.2 LigScore1 scoring function 33
3.2.2.3 LigScore2 scoring function 34
3.2.2.4 Ludi scoring function 35
3.2.2.5 Piecewise Linear Potential (PLP) 36
3.2.2.6 PLP1 36
3.2.2.7 PLP2 37
3.2.2.8 Potential of Mean Force (PMF) 39
3.3 Pharmacophore modeling 40
3.4 Virtual screening 41
Chapter 4 Molecular docking to predict the inhibitory activity of influenza endonuclease inhibitors 43
4.1 Abstract 43
4.2 Introduction 44
4.3 Methods 47
4.3.1 Dataset for molecular docking 47
4.3.2 Molecular docking experiments 50
4.3.3 Scoring functions 51
4.3.4 Consensus scoring function validation and decoys definition 52
4.4 Results and discussion 53
4.4.1 The performance of consensus scoring function 53
4.4.2 Interactions analysis of influenza endonuclease binding site and its inhibitors 63
4.4.3 The validation of our consensus scoring function in virtual screening 70
4.5 Conclusions 72
4.6 References 73
Chapter 5 The discovery of novel influenza endonuclease inhibitors by pharmacophore modelling, molecular docking, and virtual screening 80
5.1 Abstract 80
5.2 Introduction 81
5.3 Methods 86
5.3.1 2D fingerprint similarity searches 86
5.3.2 Ligand-based pharmacophore model generation (HipHop) and validation 86
5.3.3 Pharmacophore with excluded volumes (HipHopRefine) and validation 89
5.3.4 Structure-based molecular docking 90
5.3.5 Virtual screening 90
5.4 Results and discussion 91
5.4.1 2D fingerprint similarity search analysis 91
5.4.2 Ligand-based common feature pharmacophore (HipHop model) generation and validation 92
5.4.3 Pharmacophore with excluded volumes (HipHopRefine model) and validation 96
5.4.4 Virtual screening 99
5.4.5 Structure-based molecular docking studies of influenza endonuclease 99
5.5 Conclusions 109
5.6 References 110
Chapter 6 GENERAL CONCLUSIONS 115
Chapter 7 GENERAL REFERENCES 117
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