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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神經異常症藥物:以阿茲海默症、運甲腺素蛋白類澱粉沉積症與憂鬱症為例 [以論文名稱查詢館藏系統]
論文英文名稱:Computer-aided drug discovery of novel drugs for neuropsychiatric disorders: Alzheimer’s disease, transthyretin amyloidosis and depression [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:102
出版年度:103
中文姓名:周政利
英文姓名:Zhen-Li Zhou
研究生學號:98679027
學位類別:博士
語文別:中文
口試日期:2014-07-29
論文頁數:85
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:白敦文;陳文逸;黃志宏;蔡偉博
口試委員英文名:Tun-Wen Pai;Wen-Yi Chen;Chih-Hung Huang;Wei-Bor Tsai
中文關鍵詞:神經異常症阿茲海默症運甲腺素蛋白類澱粉沉積症憂鬱症電腦輔助藥物設計
英文關鍵詞:neuropsychiatric disordersAlzheimer’s disease (AD)transthyretin (TTR) amyloidosisdepressioncomputer-aided drug design (CADD)
論文中文摘要:神經異常症為大腦內的神經細胞漸進性的喪失或神經傳導物質不足,導致多種精神病症狀的產生。常見的神經異常症包含了阿茲海默症、運甲腺素蛋白類澱粉沉積症與憂鬱症。為了開發新型的阿茲海默症、運甲腺素蛋白類澱粉沉積症與憂鬱症藥物,本研究將結合多種電腦輔助藥物設計元件並設計出一個有效的篩選方法自化學分子資料庫中篩選出新穎的前導化合物。針對各個疾病,我們分別依據各個目標蛋白與抑制劑的複合體結構來建立以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,本研究所篩選出的化合物將可作為開發新穎的神經異常症藥物的新型骨幹結構。
論文英文摘要:Neuropsychiatric disorders are characterized by progressive loss of neuronal cells or lack of neurotransmitters in the brain, resulting in various psychotic symptoms. The common neuropsychiatric disorders include Alzheimer’s disease (AD), transthyretin (TTR) amyloidosis and depression. To discover new drugs against AD, TTR amyloidosis and depression, a useful guide combining several computer-aided drug design (CADD) approaches was designed in this study to identify novel lead compounds from chemical databases. For each disease, a structure-based pharmacophore model (SBPM) that describes the critical features necessary for the inhibitor binding to the target protein was generated from each target protein-inhibitor complex. Each well-validated SBPM 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. Thus, the obtaine hits could be used as new scaffold in the development of novel neuropsychiatric disorders drugs.
論文目次:CHINESE ABSTRACT......................................i
ENGLISH ABSTRACT.....................................ii
ACKNOWLEDGEMENTS....................................iii
CONTENTS.............................................iv
TABLE CONTENTS......................................vii
FIGURE CONTENTS....................................viii
Chapter 1 INTRODUCTION................................1
1.1 Neuropsychiatric disorders........................1
1.2 Pathogenic factors in neuropsychiatric disorder...2
1.3 Target diseases...................................3
1.3.1 Alzheimer’s disease (AD)........................3
1.3.1.1 Neuropathological hallmarks of AD.............3
1.3.1.1.1 Senile plaques (SPs)........................4
1.3.1.1.2 Neurofibrillary tangles (NFTs)...............6
1.3.1.2 Therapeutic strategy: inhibit the amyloid-β
(Aβ) aggregration.....................................6
1.3.2 Transthyretin (TTR) amyloidosis.................7
1.3.2.1 The precise function of TTR and the role of
its variants..........................................8
1.3.2.2 Therapeutic strategy: stabilize the native
state of TTR..........................................9
1.3.3 Depression.....................................10
1.3.3.1 Connection between serotonin deficiency and depression...........................................11
1.3.3.2 Therapeutic strategy: inhibit the human
serotonin transporter (SERT).........................12
1.4 Computational approaches for new drug discovery..13
1.4.1 Homology modeling..............................13
1.4.2 Pharmacophore modeling.........................14
1.4.3 Virtual screening..............................16
1.4.4 Molecular docking..............................16
1.4.5 Scoring functions of docking...................18
1.4.6 Molecular dynamics (MD) simulations............19
1.5 Aims of this study...............................20
Chapter 2 METHODS....................................22
2.1 Homology model generation........................22
2.2 Pharmacophoremodel generation and validation.....22
2.2.1 Ligand-based...................................22
2.2.2 Structure-based................................23
2.2.3 Pharmacophore model validation.................23
2.3 Database virtual screening.......................24
2.4 Molecular docking................................25
2.5 MD simulations...................................27
2.6 Lead compound optimization.......................28
Chapter 3 RESULTS AND DISCUSSION.....................29
3.1 Identification of novel non-peptide inhibitors
against Aβ aggregation...............................29
3.1.1 Determination of the precise PGKLVYA binding conformations on the Aβ(17-42) protofibril...........29
3.1.2 Critical interactions in maintaining the
stability of the Aβ-PGKLVYA complex..................32
3.1.3 Generation and validation of the structure-based pharmacophore model (SBPM)...........................33
3.1.4 Virtual screening for new non-peptide AD
drugs................................................35
3.1.5 Validation of the binding stability and affinity
of the Hits..........................................36
3.1.6 Validation of the synthetic accessibility and
novelty of the Hits..................................39
3.2 Identification of novel TTR amyloidosis
inhibitors...........................................39
3.2.1 Generation of the ligand-based pharmacophore
model (LBPM).........................................39
3.2.2 Generation of the SBPM.........................41
3.2.3 Validation of pharmacophore models.............42
3.2.4 Virtual screening for new TTR amyloidosis
inhibitors...........................................43
3.2.5 Molecular docking..............................44
4.2.6 Validation of the binding stability of the
Hits.................................................45
3.2.7 Validation of the synthetic accessibility and
novelty of the Hits..................................48
3.3 Identification of novel selective serotonin
reuptake inhibitors (SSRIs)..........................48
3.3.1 Construction of SERT homology model............48
3.3.2 Analysis of inhibitor binding poses............50
3.3.3 Generation of the the SBPM.....................53
3.3.4 Validation of the SBPM.........................54
3.3.5 Virtual screening..............................55
3.3.6 Validation of the binding stability of the
Hits.................................................57
3.3.7 Validation of the novelty of the the Hits......60
Chapter 4 CONCLUSIONS................................61
REFERENCES...........................................63
APPENDIX I MY PUBLICATION LIST.......................81
APPENDIX II COPYRIGHT PERMISSION OF PUBLISHED PAPER..83
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