臺灣博碩士論文加值系統

本論文主題為針對三個目標蛋白質建立電腦輔助藥物設計系統。此三個蛋白質分別為嚴重急性呼吸道症候群冠狀病毒之主蛋白酶 (SARS-CoV Main Protease)，過氧化酶增殖劑啟動受體- (Peroxisome Proliferator-Activated Receptor-) 以及二肽基肽酶-IV (Dipeptidyl Peptidase-IV，DPP-IV)。電腦輔助藥物設計方法主要可分為兩大類，分別為以蛋白質結構為主以及以配體為主之藥物設計方法(structure-based and ligand-based drug design)。在本論文中，將闡述如何分別應用這兩種技術於上述三種蛋白質先導藥物(Lead compound)之開發設計，並應用結構生物學解析先導藥物與目標蛋白質之交互作用關係。針對嚴重急性呼吸道症候群冠狀病毒，我們利用其主蛋白酶結構進行大規模的虛擬藥物篩選。進一步以對接技術(docking)模擬活性化合物與主蛋白酶的結合方式以定出其負責活性之核心結構並進行類似物篩選。此外，我們亦利用結構生物學技術探討活性化合物與主蛋白酶的結合情況以及其抑制機制。過氧化酶增殖劑啟動受體-為著名抗糖尿病藥物thiazolidinedione的受體。本論文利用以受體形狀為主的虛擬藥物篩選(Shape-based Virtual Screening)配合對接模擬以及類似物篩選找出兩個針對過氧化酶增殖劑啟動受體-具有高度專一性的化合物。生物活性測試顯示此二化合物為不完全活化劑(partial agonist)。進一步利用結構生物學以及對接技術探討此二化合物為不完全活化劑以及專一性的原因，並進一步定出其活性決定區域。二肽基肽酶-IV為治療第二型糖尿病的藥物標的。它可以有效的降低糖尿病患者對葡萄糖的耐受度。在本論文中，我們建立了ㄧ個二肽基肽酶-IV抑制劑的藥效基團模型(Pharmacophore)。此模型保留了二肽基肽酶-IV抑制劑所需具備的重要化學特性並且可與二肽基肽酶-IV蛋白質活性位置形成很好的互補關係。另外並進一步探討其在活性預測、以及資料庫篩選等方面的能力。

In this thesis, we setup the computer-aided drug design protocols for three target proteins, severe acute respiratory syndrome-coronavirus (SARS-CoV) main protease, peroxisome proliferators activated receptor-, and dipeptidyl peptidase-IV. For SARS-CoV main protease, the docking based virtual screening technique was performed. Novel non-peptide inhibitors with inhibition activity of submicro-molar range were discovered through this protocol. The structural biology study further clarify the inhibition mechanism of these compounds to SARS-CoV Mpro, which provide valuable information for next generation drug design against SARS-CoV Mpro. For PPAR-, the shape-based pharmacophore combined with docking study methods were employed. Two specific partial agonists to PPAR- were identified. Structural biology together with docking study explains the biological characteristics of these compounds, including partial agonist mechanism and selectivity, which provide useful information for further lead compounds modification. For DPP-IV, a protocol for pharmacophore generation, selection and validation was setup. The pharmacophore was characterized by the activity prediction power, and large database search power, which could be a practical tool for novel DPP-IV inhibitors discover.

CONTENT………………………………………………………………….............................I
ABBREVIATIONS VII
TABLE CONTENT IX
FIGURE CONTENT XI
CHINESE ABSTRACT XV
ENGLISH ABSTRACT XVI
CHAPTER 1. INTRODUCTION 1
1.1 PROBLEM STATEMENT 1
1.2 GENERAL STRATEGY 2
1.3 SPECIFIC AIM 3
1.4 SUMMARY OF THIS THESIS CONTENT 3
CHAPTER 2 COMPUTER-AIDED DRUG DESIGN 7
2.1 STRUCTURE-BASED DRUG DESIGN 8
2.1.1 Overview and design cycles 8
2.1.2 Protein structure 10
2.1.3 Compound database preparation 11
2.1.4 Computational approach to predict the potential binding ligand—direct docking and de novo docking 11
2.1.4.1 Direct Docking 11
2.1.4.1.1 The search algorithm 14
2.1.4.1.2 Scoring function 16
2.1.4.1.3 Protein flexibility 18
2.1.4.1.4 Post analysis 20
2.1.4.2 De novo docking 22
2.2 LIGAND-BASED DRUG DESIGN 23
2.2.1 Active analogues approach 25
2.2.2 Pharmacophore model design 25
2.2.3 3D-Quantitative structure-activity relationship 28
2.2.4 Pseudoreceptor model 29
CHAPTER 3 SOLVING PROTEIN STRUCTURE BY X-RAY CRYSTALLOGRAPHY 30
3.1 PROTEIN CRYSTALLIZATION 31
3.1.1 Theory 31
3.1.2 Crystallization screening 32
3.1.3 Methods 33
3.2 DATA COLLECTION AND PROCESSING 36
3.2.1 Data collection. 36
3.2.2 Data processing 45
3.3 SOLUTION OF THE PHASE PROBLEM 47
3.4 MODEL BUILDING AND REFINEMENT 51
3.5 THE LIMITATION OF PROTEIN CRYSTAL STRUCTURE IN STRUCTURE-BASED DRUG DESIGN 54
CHAPTER 4 STRUCTURE-BASED DRUG DESIGN OF ANTI-SARS DRUGS 57
4.1 SUMMARY OF THIS CHAPTER 57
4.2 INTRODUCTION 57
4.2.1 Severe acute respiratory syndrome (SARS) 57
4.2.2 Severe acute respiratory syndrome coronavirus (SARS-CoV) 58
4.2.3 SARS-CoV Main Protease 62
4.2.4 Structure of SARS-CoV Main Protease 63
4.2.5 Inhibitors of SARS-CoV Main Protease 67
4.3 MATERIAL AND METHODS 57
4.3.1 Database Preparation 70
4.3.2 Protein Preparation 70
4.3.3 Docking 71
4.3.3.1 Introduction to GOLD algorithm 71
4.3.3.2 Introduction to GOLD GoldScore fitness function 75
4.3.3.3 Parameters setup for GOLD running 77
4.3.4 SARS-CoV Mpro purification and inhibition assay 77
4.3.5 Protein crystallization 78
4.3.6 X-ray diffraction data collection 79
4.3.7 X-ray diffraction data processing 79
4.3.8 Structure determination of SARS-CoV Mpro by molecular replacement methods using the program MOLREP 83
4.3.8.1 Introduction to MOLREP 83
4.3.8.2 MOLREP parameters setup for SARS-CoV Mpro 84
4.3.9 Structure refinement of SARS-CoV Mpro by the program Refmac5 84
4.3.9.1 Introduction to Refmac5 84
4.3.9.2 Refmac5 parameters setup 85
4.3.10 Map display and model building 85
4.4 RESULT AND DISCUSSION 86
4.4.1 Identification of Novel SARS-CoV Mpro Inhibitors by Structure-Based Virtual Screening 86
4.4.2 Identification of Core Structure and Analogue Search 88
4.4.2.1 Docking Study of Compound 1 88
4.4.2.2 Docking Study of Compound 2 89
4.4.3 Crystals of SARS-CoV Mpro 94
4.4.4 Data processing result 95
4.4.4.1 Space group determination 95
4.4.4.2 Data scaling result 96
4.4.5 Molecular replacement result for SARS-CoV Mpro, SARS-CoV Mpro/3, SARS-CoV Mpro/15 96
4.4.6 Model refinement results for SARS-CoV Mpro, SARS-CoV Mpro/3, SARS-CoV Mpro/15 99
4.4.7 Structure validation for SARS-CoV Mpro, SARS-CoV Mpro/3, SARS-CoV Mpro/15 100
4.4.8 Overall structure of SARS-CoV M pro 111
4.4.9 The structure of SARS-CoV Mpro in complex with compound 3 111
4.4.10 The structure of SARS-CoV Mpro in complex with compound 15 115
4.4.11 Comparison of 3 and 15 to Other Complex-Structures of SARS-CoV Mpro 116
4.5 CONCLUSION 117
CHAPTER 5 STRUCTURE-BASED DRUG DESIGN OF ANTI-DIABETES DRUGS—AGONIST OF PEROXISOME PROLIFERATOR-ACTIVATED RECEPTORS- (PPAR) 119
5.1 SUMMARY OF THIS CHAPTER 119
5.2 INTRODUCTION 120
5.2.1 Insulin resistance and Type 2 diabetes (T2D) 120
5.2.2 Peroxisome proliferator activated receptors 121
5.2.3 Peroxisome proliferator activated receptors-gamma and insulin resistance 126
5.2.4 Agonist for Peroxisome proliferator activated receptors-gamma 127
5.2.5 Second generation peroxisome proliferator activated receptors-gamma agonist 128
5.2.5.1 PPAR/ dual agonist 128
5.2.5.2 PPAR// pan agonist 130
5.2.5.3 PPAR partial agonist 132
5.2.6 Structure of Peroxisome proliferator activated receptors-gamma ligand binding domain 134
5.3 MATERIAL AND METHODS 137
5.3.1 Shape-based database searching 137
5.3.1.1 Catalyst shape module 137
5.3.1.2 Catalyst shape generation and database searching 138
5.3.2 Molecular Docking of the Hit Compound to the Binding Pocket of PPARγ 140
5.3.3 Analogue search 140
5.3.4 Structure determination 141
5.4 RESULT AND DISCUSSION 141
5.4.1 Identification of compound 1 as novel PPAR ligands by shape-based virtual screening. 141
5.4.2 Compound 1 core structure identification and analogue search 143
5.4.3 Data processing results for PPAR/2 and PPAR/3 crystals 147
5.4.3.1 Space group determination 147
5.4.3.2 Data scaling result 150
5.4.4 Structure determination of PPAR/2 and PPAR/3 150
5.4.4.1 Molecular replacement result for PPAR/2 and PPAR/3 150
5.4.4.2 Model refinement result for PPAR/2 and PPAR/3 153
5.4.5 Structure validation for PPAR/2 and PPAR/3 154
5.4.6 Overall structures of PPARγ-ligand complex 162
5.4.7 PPARγ-2 complex structure 163
5.4.8. Comparison of compounds 2 and 3 complex structures. 164
5.4.9 Comparison of PPARγ full agonists binding modes 165
5.4.10 Selectivity 167
5.5 CONCLUSION 169
CHAPTER 6 PHARMACOPHORE MODEL FOR DIPEPTIDYL PEPTIDASE IV INHIBITORS 170
6.1 SUMMARY OF THIS CHAPTER 170
6.2 INTRODUCTION 170
6.2.1 Dipeptidyl peptidase IV as a drug target for type-II diabetes 170
6.2.2 Structures of dipeptidyl peptidase IV 172
6.2.2.1 The overall structure 172
6.2.2.2 The active sites 174
6.2.3 Inhibitors of dipeptidyl peptidase IV 177
6.2.3.1 Proline-like Inhibitors of Dipeptidyl Peptidase IV 177
6.2.3.2 Non-Proline-like inhibitors of dipeptidyl peptidase IV 180
6.2.4 Computer-aided drug design to dipeptidyl peptidase IV 183
6.3 MATERIAL AND METHODS 185
6.3.1 DPP-IV inhibition assay 185
6.3.2 Pharmacophore generation 185
6.3.2.1 Introduction to Catalyst-Hypogen module 185
6.3.2.1.1 The conformation generation process 186
6.3.2.1.2 Hypotheses generation--the Hypogen module 190
6.3.2.1.3 Evaluation of the hypotheses 196
6.3.2.2 DPP-IV inhibitors preparation for Catalyst 197
6.3.2.3 Parameters set up for Hypogen module 198
6.3.2.4 Database preparation and searching 198
6.4 RESULT AND DISCUSSION 199
6.4.1 Pharmacophore generation for DPP-IV inhibitors 199
6.4.2 Activity prediction and mapping of Hypo1 to training-set compounds 204
6.4.3 Validation of Hypo1 206
6.4.4 Mapping of Hypo1 onto the protein active site and inhibitors 211
6.5 CONCLUSION 215
CHAPTER 7 CONCLUSION 216
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