發展三維定量構效關係組合模型架構用於搜尋與優化標靶蛋白質抑制劑基於Pharmacophore, CoMFA 和 CoMSIA 電腦技術

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研究生：	石貴中 Shih, Kuei-Chung
論文名稱：	發展三維定量構效關係組合模型架構用於搜尋與優化標靶蛋白質抑制劑基於Pharmacophore, CoMFA 和 CoMSIA 電腦技術 Develop 3D-QSAR Combination Modeling Approach for Screening and Optimizing Target Protein Inhibitors Based on Pharmacophore, CoMFA, and CoMSIA in Silico
指導教授：	唐傳義 Tang, Chuan-Yi
口試委員:
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2011
畢業學年度：	99
語文別：	英文
論文頁數：	168
中文關鍵詞：	定量構效關係、pharmacophore 、CoMFA 、CoMSIA 、組合模型架構
外文關鍵詞：	QSAR, pharmacophore, CoMFA, CoMSIA, combination modeling approach
相關次數：	點閱：3 下載：0
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在現今的合理化藥物設計裡，定量構效關係﹝QSAR﹞是一種重要的技術，這樣的技術主要是用來建立一計算模型，而這個模型能夠反應出蛋白質和抑制劑間統計相關性。在目前主要的兩項三維定量構效關係﹝3D-QSAR﹞技術分別為Comparative Molecular Field Analysis (CoMFA)/ Comparative Molecular Similarity Index Analysis (CoMSIA)和Pharmacophore。Pharmacophore最具代表性的功能就是利用三維搜尋去辨識標靶蛋白質相關的抑制劑。但是，理論型的pharmacophore最多只能產生出五種特徵球，這個限制導致理論型pharmacophore無法完整的描述出抑制劑所需要的特徵。另外pharmacophore特徵球並不能在三維空間上針對蛋白質結合區的空間大小進行限制。另外一項技術，CoMFA/CoMSIA模型雖然不能夠進行三維搜尋去辨識相關的抑制劑，但是他們的特點是可以簡單的用來修改分子結構使其最佳化和能夠描述出分子量的限制範圍。另外，CoMFA/CoMSIA模型是使用特徵輪廓來描述抑制劑的化學特徵。而特徵輪廓的數量並不會受到限制，因此CoMFA/CoMSIA模型能夠利用特徵輪廓完整的描述出抑制劑所需要的特徵。透過這樣的特性，CoMFA/CoMSIA模型能夠提供較佳的預測化合物生物活性的預測能力。根據上述的兩項不同技術間的描述，在本篇論文內我們將這兩項不同的技術整合再一起。我們提出了一3D-QSAR組合模型架構用來解決這兩項3D-QSAR技術彼此的缺點。我們的組合架構模型能夠提供一個有用的工具，用來開發新的先導化合物並且透過虛擬篩選來預測其生物活性。

Quantitative Structure Activity Relationships (QSAR) is an important technique in the rational drug design, which was used to build computational models to find a statistically significant correlation between the receptor and inhibitors. There are two mainstream of 3D-QSAR technologies, namely Comparative Molecular Field Analysis (CoMFA)/ Comparative Molecular Similarity Index Analysis (CoMSIA) and Pharmacophore. Most significant function of pharamcophore model is to use 3D screen to recognize the related target protein inhibitors. However, the number of pharmacophore features was restricted as five chemical features at maximum, and which could not describe the 3D space limitation of the binding site. This restriction induces incompletely describing the chemical features of inhibitors. Contrastingly, the other two models, CoMFA and CoMSIA were not suitable to search 3D databases, but can easily be used to modify the molecule structure optimization and describe the limit range of molecule weights. Additional, CoMFA and CoMSIA models use contours to describe the chemical features of inhibitor. The number of contours was not restricted, that could reflect the chemical features of inhibitor. Therefore, CoMFA and CoMSIA models could provide better predication ability to predict the bioactivity. According to above characters, we prefer to combine these two different technologies. We propose a 3D-QSAR combination modeling approach to solve two 3D-QSAR technical shortcomings of each other. Our combination approach could provide a valuable tool in the design of new leads with desired biological activity by virtual screening.

Abstract    i
摘要    iii
致謝辭    v
Table of Contents    vii
List of Figures    xi
List of Tables    xiii
Chapter 1 Introduction    1
Chapter 2 3D-QSAR Modelling Methods    7
1 Designing a 3D-QSAR Combination Modeling Approach    7
2 Generating Theoretical pharmacophore Models    7
3 Generating CoMFA and CoMSIA Models    8
4 Contour Maps of CoMFA and CoMSIA Models    9
Chapter 3 Quality Validation Methods of The 3D-QSAR Model    11
1 Cost function analysis    11
2 Fischer’s cross-validation analysis    12
3 PLS Analysis for CoMFA and CoMSIA Models    13
4 Goodness of Hit Test Score    14
Chapter 4 Materials    17
1 Introduction of B-Raf Kinase    17
1.1 Biological Data of B-Raf Kinase    18
2 Introduction of Neuraminidase    21
2.1 Biological Data of Neuraminidase    22
3 Introduction of Heat Shock Protein 90    25
3.1 Biological Data of Heat Shock Protein 90    28
4 Introduction of FMS-like Tyrosine Kinase 3    30
4.1 Biological Data of FMS-like Tyrosine Kinase 3    31
Chapter 5 Results and Discussion    35
1 Results of B-Raf Combination Modeling Approach    35
1.1 Pharmacophore Generation Results of B-Raf Kinase    35
1.2 The Fischer’s Validation of B-Raf pharmacophore hypotheses    37
1.3 CoMFA and CoMSIA Generation Results of B-Raf Kinase    38
1.4 GH Validation of B-Raf Combination Approach    41
1.5 Contour Map Discussion of B-Raf CoMFA and CoMSIA Models    43
2 Results of Neuramindase Combination Modeling Approach    50
2.1 Pharmacophore Generation Results of Neuraminidase    50
2.2 CoMSIA Generation Results of Neuraminidase    53
2.3 Contour Map Discussion of Neuraminidase CoMFA and CoMSIA Models    55
3 Results of HSP90 Combination Modeling Approach    61
3.1 Pharmacophore Generation Results of HSP90    61
3.2 The Fischer’s Validation of HSP90 pharmacophore hypotheses    64
3.3 CoMFA and CoMSIA Generation Results of HSP90 Kinase    65
3.4 GH Validation of HSP90 Combination Approach    68
3.5 Feature analysis of the HSP90 pharmacophore hypothesis    70
3.6 Contour Map Discussion of HSP90 CoMFA and CoMSIA Models    71
4 Results of FLT-3 Kinase Combination Modeling Approach    77
4.1 Pharmacophore Generation Results of FLT-3 Kinase    77
4.2 The Fischer’s Validation of FLT-3 pharmacophore hypotheses    81
4.3 CoMFA and CoMSIA Generation Results of FLT-3 Kinase    82
4.4 Contour Map Discussion of FLT-3 CoMFA and CoMSIA Models    85
Chapter 6 Conclusions    91
References    93
Appendix    103

                                

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