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論文中文名稱:利用電腦輔助藥物設計開發新穎的藥物:以流感內切酶、炭疽致死因子與醛糖還原酶為例 [以論文名稱查詢館藏系統]
論文英文名稱:Computer-aided drug discovery of novel drugs: influenza endonuclease, anthrax lethal factor and aldose reductase [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:工程科技研究所
畢業學年度:103
畢業學期:第二學期
中文姓名:廖晃聖
英文姓名:Huang-Sheng Liao
研究生學號:97679026
學位類別:博士
語文別:英文
口試日期:2015/07/28
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:劉宣良;陳文逸;蔡偉博;侯劭毅;黃志宏
中文關鍵詞:分子動態模擬同源模型分子嵌合藥效基團模型電腦輔助藥物設計
英文關鍵詞:molecular dynamics simulationshomology modelingmolecular dockingpharmacophore modelingvirtual screening
論文中文摘要:傳統的開發藥物模式通常需耗費數以百萬美元計的經費,所花的時間也約十年以上,為了縮短其研發時間,近期發展出的電腦輔助藥物設計方法則成為有用的工具,常見的使用元件包含了分子動態模擬、同源模型、分子嵌合、藥效基團模型、活性-結構定量關係分析與虛擬篩選,可根據實驗所需而搭配不同的元件來使用。有鑒於此,本研究將使用多種電腦輔助藥物設計工具,進行新穎的藥物開發,以流感內切酶、炭疽致死因子、醛糖還原酶為例。針對各個疾病,我們分別依據已知的抑制劑和目標蛋白與抑制劑的複合體結構來分別建立以配體為基礎和結構為基礎的藥效基團模型,此模型描述抑制劑結合在目標蛋白中所必須的重要官能基特徵。各個驗證後的藥效基團模型將套用至虛擬篩選,並自化學分子資料庫中篩選出對目標蛋白具抑制活性的化合物。所篩選出來的化合物與目標蛋白的結合親和力與穩定度將分別利用分子嵌合與分子動態模擬進行評估。根據本實驗的篩選結果,各個目標蛋白都能篩選出比參考抑制劑具有更好結合親和力與穩定度的化合物。因此我們的研究顯示,藥效基團模型可以提供合理的設計去找出新穎的藥物。因此,本研究所篩選出的化合物將可作為開發新穎的流感內切酶、炭疽致死因子與醛糖還原酶藥物的新型骨幹結構。
論文英文摘要:The traditional pattern of drug development costs not only millions of dollars but also decades. In order to shrink the development time, recent development of computer-aided drug design (CADD) approach has become a useful tool. The common elements of CADD include molecular dynamics simulations, homology modeling, molecular docking, pharmacophore modeling, quantitative structure-activity relationship analysis and virtual screening. These elements can be combined in various ways in different drug design practices. In this study, we aim to use a variety of CADD tools to discover novel drugs: influenza endonuclease, anthrax lethal factor and aldose reductase. For each disease, the ligand- (LPBM) and structure-based pharmacophore models (SPBM) were constructed based on the known inhibitors and the crystal structure, respectively. These models describe the critical features necessary for the inhibitor binding to the target protein was generated from each target protein-inhibitor complex. Each well-validated SBPM and LBPM was then applied in virtual screening to identify potential compounds with inhibitory activity toward the target protein from chemical databases. The binding affinity and stability between the target protein and the selected compounds were evaluated using molecular docking and molecular dynamics (MD) simulations, respectively. Based on our search method, serveral potenial hits that exhibited higher binding affinity and stability in comparison to the reference inhibitor were finally identified for each target protein. Our study suggests that the pharmacophore modeling can provide guidance for the rational design to discover novel drug. Thus, the obtaine hits could be used as new scaffold in the development of novel influenza endonuclease, anthrax lethal factor and aldose reductase drugs.
論文目次:CONTENTS
CHINESE ABSTRACT i
ENGLISH ABSTRACT ii
ACKNOWLEDGEMENT iii
CONTENTS iv
TABLE CONTENTS vii
FIGURE CONTENTS viii
Chapter 1 INTRODUCTION 1
1.1 Computational approaches for new drug discovery 1
1.2 Pharmacophore modeling 2
1.3 Virtual screening 3
1.4 Molecular docking 3
1.5 Scoring functions of docking 5
1.6 Molecular dynamics (MD) simulations………………………………………..6
1.7 Aims of study 8
Chapter 2 METHODS 10
2.1 Pharmacophoremodel generation and validation 10
2.1.1 Ligand-based 10
2.1.2 Structure-based 10
2.1.3 Pharmacophore model validation 11
2.2 Database virtual screening 11
2.3 Molecular docking 13
2.4 MD simulations 14
2.5 Lead compound optimization 15
Chapter 3 PHARMACOPHORE MODELING AND VIRTUAL SCREENING TO
DESIGN POTENTIAL INFLUENZA VIRUS ENDOUCLEASE INHIBITORS…...16
3.1 Introduction of Influenza Virus Endonuclease 16
3.1.1 Influenza endonuclease inhibitors 18
3.1.2 Aims of study 20
3.2 Methods 21
3.3 Results and discussion 22
3.3.1 Pharmacophore generation 22
3.3.2 Pharmacophore model validation 30
3.3.3 Virtual screening for new Influenza Virus Endonuclease inhibitors 33
3.3.4 Docking studying of Influenza Virus Endonuclease 34
3.3.5 Conclusions 36
Chapter 4 STRUCTURE-BASED PHARMACOPHORE MODELING AND VIRTUAL SCREENING TO IDENTIFY NOVEL INHIBITORS FOR ANTHRAX LETHAL FACTOR………………………………………………………...37
4.1 Introduction of anthrax lethal factor 37
4.1.1 Anthrax lethal factor inhibitors 39
4.1.2 Aims of study 39
4.2 Methods 39
4.2.1 Construction of a SBPM 39
4.2.2 Validation of pharmacophore models (GH score) 40
4.2.3 Database screening (NCI database) 40
4.2.4 Molecular docking 41
4.2.5 MD simulation 41
4.3 Results and discussion 42
4.3.1 Construction of a SBPM 42
4.3.2 Validation of pharmacophore models (GH score) 44
4.3.3 Database screening for new LF inhibitors……………………………..46
4.3.4 Molecular docking studies of LF 46
4.3.5 Validation of the binding stability of the Hits 47
4.3.6 Validation of novelty of the Hits 48
4.3.7 Conclusions 49
Chapter 5 STRUCTURE-BASED PHARMACOPHORE MODELING AND VIRTUAL SCREENING TO IDENTIFY NOVEL INHIBITORS FOR ANTHRAX LETHAL FACTOR………………………………………………………...50
5.1 Introduction of Aldose reductase 50
5.1.1 ALR2 inhibitors 51
5.1.2 Aims of study 52
5.2 Methods 53
5.2.1 Construction of a ligand-based pharmacophore model (LBPM) 53
5.2.2 Construction of a structure-based pharmacophore model (SBPM) 54
5.2.3 Validation of both pharmacophore models 54
5.2.4 Database screening 55
5.2.5 Molecular docking 55
5.2.6 MD simulation 56
5.3 Results and discussion 56
5.3.1 Construction of a LBPM 56
5.3.2 Construction of a SBPM 62
5.3.3 Validation of both pharmacophore models 64
5.3.4 Validation of LBPM 64
5.3.5 Validation of SBPM 68
5.3.6 Virtual screening for new ALR2 inhibitors 70
5.3.7 Docking study of ALR2 72
5.3.8 Validation of the binding stability of the Hits 73
5.3.9 Novelty study 74
5.3.10 Conclusions 76
Chapter 6 GENERAL CONCLUSIONS 77
REFERENCES 78
APPENDIX I MY PUBLICATION LIST 91







TABLE CONTENTS
Table 2.1 Cutoff values used to assign the prediction levels of various ADME descriptors………………………………………………………………………….12
Table 3.1 The HypoGen statistical parameters of the top 10
pharmacophore hypothese………………………………………………………….27
Table3.2 Experimentally measured and predicted activities for the training set compounds………………………………………………………………................28
Table 4.1 Pharmacophore model validation using GH score method......................................45 Table 5.1 The HypoGen statistical parameters of the top 10 pharmacophore hypotheses.......60
Table 5.2 Pharmacophore model validation using GH score method......................................69
Table 5.3 2D structures and pharmacophore mappings of Hits 1 and 2 for ALR2
inhibitors with their fit values retrieved from the NCI and ZNP database………...75



FIGURE CONTENTS
Figure 1.1 3D-pharmacophore model. Pharmacophore features are color-coded with green for hydrogen-bond acceptor (HBA), cyan for hydrophobic (HY), red for positive ionizable (PI) and orange for ring-aromatic (RA)…………………………………3
Figure 1.2 Schematic diagrams illustrating the docking of a ligand to receptor to produce a complex…………………………………………………………………………….4
Figure 1.3 Small molecule (ligand) docked into a binding site of a protein (receptor).............5
Figure 1.4 The general flow chart for classical molecular dynamics (MD) simulation……….7
Figure 1.5 Schematic representation of the designed computer-aided drug discovery strategy implemented in the identification of potential leads for for influenza endonuclease, anthrax lethal factor and aldose reductase…………………………………………..9
Figure 2.1 (a) The website of the SciFinder scholar search system at
http://www.cas.org/scifinder/scholar and (b) the PubChem Structure Search at http://pubchem.ncbi.nlm.nih.gov/search...................................................................15
Figure 3.1 Mechanism of influenza endonuclease...................................................................17
Figure 3.2 Diketobutanoate inhibitors of influenza endonuclease. Numbers in brackets denote IC50 values determined in the cap-dependent RNA polymerase assay with influenza A polymerase (6, 8) or from literature……………………………………………..19
Figure 3.3 Flutimide and related inhibitors of influenza endonuclease. Literature IC50 values
for the inhibition of influenza A RNA polymerase are shown in
brackets……….........................................................................................................19
Figure 3.4 Chemical structures of the 41 training set compounds used in pharmacophore….24
Figure 3.5 The best hypothesis model (Hypo1) of the influenza virus endonuclease inhibitors
generated by the HypoGen module in DS V2.5.5 software. (A) Hypo1 model features; (B) 3D spatial arrangement and geometric parameters of Hypo1 and distance between pharmacophore features in angstrom; (C) Hypo1 mapping of the most active compound (compound 1; IC50 = 0.19 µM) and (D) one of the lowest active compounds (compound 41; IC50 = 800 µM). Colorcoding of the pharmacophore features: hydrogen bond accepter in green, hydrophobic in cyan...26
Figure 3.6 Chemical structures of the 11 test set compounds used in pharmacophore model validation…………………………………………………………………..............31
Figure 3.7 Plot of the correlation between the experimental and predicted activities based on Hypo1 model for the (A) training set and (B) test set compounds………………….32
Figure 3.8 The difference in total cost of hypotheses between the initial spreadsheet and 19 andom spreadsheets after Cat-Scramble run…………………………………….......32
Figure 3.9 The comformation of Hypo1 pharmacophore model (A) and docked conformation of the most active compound (Compound 1, IC50 = 0.19 µM) in the active site (B). The green dotted lines represent hydrogen bonds…………………………………...34
Figure 3.10 10 new lead compounds screened from the NCI database using the generated pharmacophore model with high fit values and their respective mapping to the features of Hypo 1…………………………………………………………………...35
Figure 4.1 Pathophysiology of Anthrax.……………………………………………………..37
Figure 4.2 Furin activation of the anthrax toxin……………………………………………..38
Figure 4.3 Full and zoomed view of 3D crystal structure of anthrax lethal factor (PDBcode 1PWU). The zoomed view shows the important active site residues (stick form in element color) along with the generated pharmacophoric features (small spheres). Green color represents HBA, magenta color represents HBD and cyan color represents HY features... ……………………………………………………..43
Figure 4.4 (A) Structure-based pharmacophore model of lethal factor (LF) inhibitor including two HBA (green spheres), one HBD feature (magenta sphere), one HY feature (cyan spheres) and 18 exclusion spheres (gray spheres). (B) Pharmacophoric features are shown with inter-feature distance constraints……………………….43
Figure 4.5 Binding orientations of (A) Hit 1 (green color), (B) Hit 2 (blue color), (C) Hit 3 (red color). Active site residues are shown in stick form and hydrogen bond interactions are indicated with green dotted lines………………………………...47
Figure 4.6 Root-mean-squared deviation profiles of hits 1 2 3 are represented in green, blue and red, respectively………………………………………………….48
Figure 4.7 Chemical structures of identified hits for LF inhibition and their overlays on the pharmacophore model…………………………………………………………….48
Figure 5.1 The polyol pathway of glucose metabolism. AR catalyzes the NADPH-dependent reduction of glucose to sorbitol. Sorbitol dehydrogenase oxidizes sorbitol to fructose in an NAD+-linked reaction..…………………………............................50
Figure 5.2 So far, a variety of structurally different compounds have been reported to act as ALR2 inhibitors……………………...…………………………...........................52
Figure 5.3 Chemical structures of the 79 training set compounds used in pharmacophore model generation.……………………...…………………………........................57
Figure 5.4 (A) The best HypoGen Pharmacophore model of LB_Hypo1. (B) 3D spatial arrangement and geometric parameters of LB_Hypo1 and distance between pharmacophore features (Å). (C) LB_Hypo1 mapping of the most active compound 1. (D) LB_Hypo1 mapping of the lowest active compounds 79. color-coding of the pharmacophore features: HBA in green, HBD in magenta, HY in cyan..……………………...…………………………........................................61
Figure 5.5 (A) Pharmacophoric features retrieved through structure- based strategy: one HBA (green spheres), one NI feature (blue sphere), three HY feature (cyan spheres). (B) Pharmacophoric features are shown with inter-feature distance constraints...……………………...………………………….................................62
Figure 5.6 The zoomed view shows the important active site residues (stick form in element color) along with the generated pharmacophoric features. Green color represents HBA, blue color represents NI and cyan color represents HY features………......63
Figure 5.7 Chemical structures of the 33 test set compounds used in pharmacophore model validation....……………………...………………………….................................65
Figure 5.8 Plot of the correlation between the experimental activity and the estimated activity by LB_Hypo1 for the training set molecules (in black) and the test set molecules (in red)…………………………………………………………………………..66
Figure 5.9 Results of Fischer’s randomization test for 95% confidence level……………… 67
Figure 5.10 Schematic representation of virtual screening protocol implemented in the
identification of ALR2 inhibitors………………………………………………..71
Figure 5.11 Binding orientations of (A) Hit 1 (green color) was obtained by LBPM virtual screening, (B) Hit 2 (blue color) was obtained by SBPM virtual screening. Active site residues are shown in stick form and hydrogen bond interactions are indicated with green dotted lines………………………………………………..72
Figure 5.12 Root-mean-squared deviation (RMSD) profiles of Hit 1 and Hit 2 are represented in green and blue, respectively…………………………………………………..73
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