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論文中文名稱:利用藥效基團模型、虛擬篩選、分子嵌合、分子動態模擬與三維定量構效關係來開發新型金黃色葡萄球菌二氫葉酸還原酶抑制劑 [以論文名稱查詢館藏系統]
論文英文名稱:Discovery of potential inhibitors against Staphylococcus aureus dihydrofolate reductase by pharmacophore modeling, virtual screening, molecular docking, molecular dynamics simulation and 3D-QSAR modeling [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:103
畢業學期:第一學期
中文姓名:李劍峻
英文姓名:Juan-June Lee
研究生學號:100688007
學位類別:碩士
口試日期:2014/12/02
指導教授中文名:劉宣良
口試委員中文名:蔡偉博;黃志宏
中文關鍵詞:金黃色葡萄球菌二氫葉酸還原酶以配體為基礎的藥效基團模型以結構為基礎的藥效基團模型虛擬篩選分子嵌合分子動態模擬三維定量構效關係比較分子場分析
英文關鍵詞:Staphylococcus aureus; Dihydrofolate reductase; ligand-based pharmacophore modeling; structure-based pharmacophore modeling; virtual screening; molecular docking; molecular dynamics simulation; 3D-QSAR; CoMFA
論文中文摘要:二氫葉酸還原酶是金黃色葡萄球菌中去氧核醣核酸和核醣核酸合成時所必須的酵素。抑制二氫葉酸還原酶已被證實為治療金黃色葡萄球菌感染的有效方法,然而,金黃色葡萄球菌已對大多數的二氫葉酸還原酶抑制劑產生抗藥性。為了開發具有潛力的金黃色葡萄球菌二氫葉酸還原酶抑制劑,本研究分別以已知的二氫葉酸還原酶抑制劑以及二氫葉酸還原酶與已知藥物Q11的複合體共結晶結構,來架設以配體和結構為基礎的藥效基團模型。經過驗證後,所產生最佳的以配體為基礎的藥效基團模型包括了三個疏水性和一個氫鍵接受者基團;而最佳的以結構為基礎的藥效基團模型,包括了三個疏水性和兩個氫鍵提供者基團。上述兩個通過驗證的藥效基團模型將作為三維虛擬篩選依據,並於美國國家癌症研究所(National Cancer Institute)和Maybridge資料庫中鑑別出具潛力的二氫葉酸還原酶抑制劑。從檢索中得到的化合物會再經由ADMET與分子嵌合實驗進行二次篩選。所篩選出的化合物會以10奈秒的分子動態模擬來驗證其結合穩定性。最後,挑選出五個比最高抑制活性的二氫葉酸還原酶抑制劑Trimethoprim具有更好的結合穩定度的化合物作為具潛力的抑制劑。我們的研究顯示,在虛擬篩選中同時使用以配體和結構為基礎的藥效基團模型,能增加鑑別出具有活性且不同結構二氫葉酸還原酶抑制劑的機會。在論文的第二部份,以三維定量構效關係的比較分子場來探討一系列十二個炔聯抗葉酸化合物來建立比較分子場模型,以確定這些分子活性所需的子結構。最佳的比較分子場模型得到交叉驗證法值q2為0.843、迴歸值r2為0.993以及預測相關係數r2pred為0.863。根據比較分子場模型的場域分析圖,與炔聯抗葉酸化合物活性相關之關鍵結構因素如下: R3及Ar取代位置分別適合空間障礙大和負電性的取代基;而R1取代位置則是適合空間障礙小的取代基。從本研究所獲得的結果顯示出電腦模擬方法可以有效地鑑別與設計出更具潛力和新穎的二氫葉酸還原酶抑制劑,用以治療金黃色葡萄球菌的感染。
論文英文摘要:Dihydrofolate reductase (DHFR) is an enzyme required for the synthesis of DNA/RNA in Staphylococcus aureus. Inhibition of DHFR has been proven to be a potent strategy for the treatment of S. aureus infection; however, S. aureus has generated drug resistance to most DHFR inhibitors. To discover potential S. aureus DHFR inhibitors, the ligand- and structure-based pharmacophore models (denoted as LBPM and SBPM, respectively) were constructed based on the known DHFR inhibitors and the co-crystal structure of DHFR-Q11 complex, respectively. After validation, the best LBPM consists of three hydrophobics (HYs) and one hydrogen bond acceptor (HBA), while the best SBPM consists of three HYs and two hydrogen bond donors (HBDs). Both well validated models were subsequently used as 3D-queries in virtual screening to identify potential S. aureus DHFR inhibitors from National Cancer Institute (NCI) and Maybridge databases. The retrieved compounds were then filtered by ADMET and molecular docking and their binding stabilities were validated by 10-ns molecular dynamics (MD) simulations. Finally, five compounds were identified as potential S. aureus DHFR inhibitors based on their higher binding stabilities in comparison to the most active DHFR inhibitor “Trimethoprim”. Our study suggests that using both LBPM and SBPM simultaneously in virtual screening is able to increase the chance of identifying more active and structurally diverse DHFR inhibitors. In the second part of this thesis, a series of 12 propargyl-linked antifolates were used to construct a 3D-quantitative structure activity relationship (QSAR) CoMFA model to determine the substructures required for the activities of these molecules. The best CoMFA model yielded the cross-validation coefficient q2 = 0.843, the correlation coefficient r2 = 0.993 and the predictive correlation coefficient r2pred = 0.863. According to CoMFA contour map, some key structural factors responsible for propargyl-linked antifolates activity were revealed as follows: R3 and Ar are suitable for substituents with favor bulky groups and electronegative, respectively; while R1 is suitable for substituent with disfavor bulky group. The results gained from this study indicate that computational approach may be used effectively to discover potent and novel DHFR inhibitors for the treatment of S. aureus infection.
論文目次:CONTENTS
ABSTRACT i
ACKNOWLEDGEMENTS v
CONTENTS vi
TABLE CONTENTS x
FIGURE CONTENTS xi
Chapter 1 GENERAL INTRODUCTION 1
Chapter 2 LITERATURE REVIEW 3
2.1 Infection disease 3
2.2 Staphylococcus aureus (S. aureus) 4
2.2.1 Virulence factors 6
2.2.2 Treatment and antibiotic resistance 7
2.3 Dihydrofolate reductase (DHFR) 8
2.3.1 Function of folate biosynthsis pathway 9
2.3.2 Previous studies of DHFR inhibitors 11
Chapter 3 MOLECULAR MODELING 18
3.1 Pharmacophore modeling 19
3.1.1 Ligand-based pharmacophore 20
3.1.2 Structure-based pharmacophore 20
3.2 Virtual screening 21
3.3 Molecular docking 22
3.3.1 Docking programs 24
3.3.1.1 DOCK algorithm 24
3.3.1.2 FlexX algorithm 26
3.3.1.3 LibDock 26
3.4 Scoring function for docking 26
3.4.1 The types of scoring functions 27
3.4.2 Scoring functions 29
3.4.2.1 Piecewise Linear Potential (PLP) 29
3.4.2.2 PLP1 30
3.4.2.3 PLP2 31
3.4.2.4 Potential of Mean Force (PMF) 33
3.4.2.5 Jain scoring function 33
3.4.2.6 LigScore1 scoring function 34
3.4.2.7 LigScore2 scoring function 35
3.4.2.8 Ludi scoring function 36
3.5 Molecular dynamics simulations 37
3.5.1 Force Fields 38
3.5.1.1 CHARMm force field 39
3.5.1.2 The Parameters in the Force Field 39
3.5.1.3 Functional Form of the CHARMm Force Field 44
3.5.2 Minimization 46
3.5.3 Equilibration 49
3.5.4 Molecular Dynamics 50
3.6 Comparative molecular field analysis (CoMFA) 52
3.6.1 Step of CoMFA modeling 54
Chapter 4 Discovery of potential Staphylococcus aureus dihydrofolate reductase inhibitors by ligand- and structure-based pharmacophore modeling 56
4.1 Abstract 56
4.2 Introduction 57
4.3 Materials and Methods 60
4.3.1 The construction of the LBPM 60
4.3.2 The construction of the SBPM 63
4.3.3 Validations of both pharmacophore model 63
4.3.4 Virtual screening 64
4.3.5 Molecular docking 64
4.3.6 Molecular dynamics simulation 65
4.4 Results and discussion 66
4.4.1 The Construction of the LBPM 66
4.4.2 The construction of the SBPM 70
4.4.3 Validation of the LBPM 71
4.4.4 Validation of the SBPM (GH score) 75
4.4.5 Virtual screening 77
4.4.6 Molecular docking 78
4.4.7 MD simulations 81
4.5 Conclusions 82
Chapter 5 Receptor-based 3D-QSAR models and design of new propargyl-linked antifolates as Staphylococcus aureus DHFR inhibitors 84
5.1 Abstract 84
5.2 Introduction 85
5.3 Materials and Methods 86
5.3.1 Data set preparation 86
5.3.2 Molecular docking and alignment 88
5.3.3 Generated 3D-QSAR model 88
5.3.4 Partial least square (PLS) analysis 88
5.4 Results and discussion 89
5.4.1 Receptor-based alignment 89
5.4.2 CoMFA results 90
5.4.3 CoMFA contour maps analysis 92
5.4.4 Structural requirement and design new molecules 95
5.5 Conclusions 97
Chapter 6 GENERAL CONCLUSIONS 98
Chapter 7 REFERENCES 99
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