臺灣博碩士論文加值系統

NF-κB 是一個重要的轉錄因子，幾乎存在於所有形式的動物細胞中，其活化與許多細胞內刺激反應有關，例如: 壓力、UV光和細菌或病毒的抗原，NF-κB 在免疫反應中為重要的調控者，若 NF-κB 調控失常會引起癌症、發炎反應、自體免疫疾病、敗血性休克、病毒感染或免疫發展不全。因此，主要的研究目標，是篩選能抑制 NF-κB 活化的化合物，希望能應用於治療發炎所引起的疾病；為了建構 NF-κB 抑制劑的三維定量結構模型，本研究利用 CATALYST 和 PHASE 兩套 3D-QSAR 軟體來分析五十三個高活性的 NF-κB 抑制劑並建立藥效基團假說。最佳 CATALYST 藥效基團模型是由五種特徵所組成，訓練組中 35 個抑制劑，其 correlation 為 0.9088，在測試組中 18 個抑制劑，其 correlation 為 0.83586，顯示出實際活性與預測活性有高度相關性；另外，利用 PHASE 所建立出的最佳藥效基團模型是由七種特徵所組成，訓練組之 R-squared 值為 0.9705，而在測試組之 R-squared 值為 0.8126。經由 PHASE 與 CATALYST 模型相互比較的結果，得知 PHASE 比 CATALYST 對抑制劑之活性預測更為準確。以 CoMSIA 分析 NF-κB 抑制劑之最佳結果的訓練組，利用 CATALYST 建立藥效基團，其最佳藥效基團模型是由六種特徵所組成，訓練組之 correlation 為 0.9032，測試組之 correlation 為 0.76，並且藥效基團可以與 NF-κB 結構表面的胺基酸相互對應。本研究中建立出三種不同之藥效基團模型，可作為藥物虛擬篩選中有價值的平台。最後，藉由此三種藥效基團模型，針對 Maybridge 資料庫中的化合物進行藥物虛擬篩選，篩選出七個化合物，再利用 DOCKING 軟體 (GOLD、GLIDE 和 SURFLEX) 分析此七種化合物，選出針對 NF-κB 最有抑制潛力的化合物，未來希望能進入 in vitro 的測試。

NF-κB (nuclear factor-kappa B) is an important transcription factor. It is found in almost all animal cell types and is involved in cellular responses to stimuli such as stress, ultraviolet irradiation, and bacterial or viral antigens. NF-κB plays a key role in regulating the immune response to infection. Consistent with this role, incorrect regulation of NF-κB has been linked to cancer, inflammatory and autoimmune diseases, septic shock, viral infection, and improper immune development. Therefore, we report selective inhibitor of NF-κB activation expected was able to therapeutic approach to inflammatory disease. In order to elucidate the 3D quantitative structure-activity relationship of NF-κB inhibitors was developed based on 35 potent inhibitors by CATALYST and PHASE software. The best scoring pharmacophore hypothesis, Hypo1, consisting of five features, had a best correlation coefficient of 0.9088. In the test set analysis, a correction coefficient of 0.83586 shows a good correlation between the experimental and estimated activities. In addition, a PHASE 3D-QSAR model derived with this pharmacophore yielded an R-squared of 0.9705 and an excellent external test set predictive R-squared of 0.8126 for 18 compounds. It is demonstrated that the performance of Phase is better than or equal to that of Catalyst HypoGen. The best CoMSIA model was identified from the stepwise analysis results and constructed a pharmacophore of the training set correspond to derived CoMSIA model were used by the Catalyst program. The best scoring pharmacophore hypothesis, Hypo1, consisting of six features, had a best correlation coefficient of 0.9032. In the test set analysis, a correction coefficient of 0.76. Furthermore, the pharmacphore features were correctly mapped onto structure surface. The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening. Finally, we continued to search Maybridge database by pharmacophore-based virtual screening, and found 7 compounds, at last select most potential lead compound by docking software, could be continue in vitro assay.

中文摘要 Ⅰ

Abstract Ⅱ

誌謝 Ⅲ

目錄 Ⅳ

圖目錄 Ⅶ

表目錄 XI


第一章 序論 1

1-1 發炎反應 1

1-2 NF-κB 調控發炎基因表現之機轉 1

1-3 NF-κB (p50/p65) 結構 6

1-4 電腦輔助藥物設計 12

1-5 CATALYST簡介 16

1-6 CATALYST產生構形 19

1-7 CATALYST/HypoGen 理論 21

1-8 CATALYST分數判斷 25

1-9 CATALYST/CatScramble 統計驗證 27

1-10 PHASE 簡介 28

1-11 研究動機與目的 33

第二章 材料方法與策略 34

2-1 實驗材料 34

2-2 實驗方法與策略 35

第三章 結果與討論 37

3-1 CATALYAT 和 PHASE 3D-QSAR 模型之間的相關性 37

3-1-1 訓練組的結果 39

3-1-2 CATALYST/CatScramble 結果 44

3-1-3 測試組的結果 46

3-1-4 討論 49

3-2 CoMFA、CoMSIA 結果與 CATALYST 模型之間的相關性 53

3-2-1 訓練組的結果 55

3-2-2 CATALYAT/CatScramble 結果 60

3-2-3 測試組的結果 62

3-2-4 討論 65

3-3 不同主架構抑制劑的活性預測 70

3-4 CoMFA、CoMSIA 和 CATALYST 結果與討論 73

第四章 篩選 NF-κB 抑制劑 76

4-1 資料庫篩選與活性預測 76

4-2 篩選結果討論 79

第五章 結論與未來展望 83

5-1 結論 83

5-2 未來展望 83

附錄 85

參考文獻 87

作者簡歷 92


圖目錄

Fig 1-1. The Rel/NF-κB/IκB family of proteins 3

Fig 1-2. A schematic representation of signaling cascades for LPS, IL and TNF-α stimulation and activation of NF-κB (p50/p65) 4

Fig 1-3. Potential mechanisms by which NF-κB activation can cause development of cancer 5

Fig 1-4. The Rel/NF-κB family of proteins 8

Fig 1-5. The structure of the heterodimer bound on the IgκB DNA 9

Fig 1-6. DNA contacts made by the heterodimer 10

Fig 1-7. P50 of NF-κB structure bound to κB site DNA 11

Fig 1-8. Timeline in a drug discovery project 14

Fig 1-9. The role of Computer aided drug design in drug development 15

Fig 1-10. The classifications of Computer-aided drug design 15

Fig 1-11. The eleven type of pharmacophore features 18

Fig 1-12. Summary description of CATALYST HypoGen methodology 18

Fig 1-13. Conformation generate by CHARMm Force Field 20

Fig 1-14. The three stages building to pharmacophore model 23

Fig 1-15. Calculating fit of pharmacophore feature and training set compound 23

Fig 1-16. Regression information used to estimate activity and fit value of training set 24

Fig 1-17. Three cost of the hypothesis model 26

Fig 1-18. Cross-Validation used to actual activity and molecular structure of training set 27

Fig 1-19. Summary description of Phase methodology 31

Fig 1-20. Illustration of the treebased partitioning technique used to identify common pharmacophores based on intersite distances 31

Fig 1-21. The mapping of a molecule to a volume bit pattern that provides the independent variables for a PHASE 3D-QSAR model 32

Fig 2-1. The strategy of molecular modeling used to NF-ĸB inhibitors 36

Fig 3-1. 2D chemical structures of the 35 training set molecules used to from HypoGen pharmacophore 38

Fig 3-2. The regression of actual versus predicted activities by the Hypo1 hypothesis for the training set inhibitors onto a linear relationship 41

Fig 3-3. Top-scoring HypoGen pharmacophore Hypo1 42

Fig 3-4. 2D chemical structures of the 18 testing set molecules 46

Fig 3-5. The regression of actual versus predicted activities by the Hypo1 hypothesis for the test set inhibitors onto a linear relationship 47

Fig 3-6. Curves for test set activity predictions by PHASE 3D-QSAR model 47

Fig 3-7. (A) Mapping of compound 1 (top) and compound 2 (below) onto Hypo1 from CATALYST, (B) PHASE 51

Fig 3-8. (A) Mapping of compound 1~11 onto Pharm_A from PHASE. (B) Sample view of the excluded volumes created for the Pharm_A hypothesis using creates NF-κB receptor 52

Fig 3-9. 2D chemical structures of the 32 training set molecules used to from HypoGen pharmacophore 54

Fig 3-10. Top-scoring HypoGen pharmacophore Hypo1 57

Fig 3-11. The regression of actual versus predicted activities by the Hypo1 hypothesis for the training set inhibitors onto a linear relationship 59

Fig 3-12. 2D chemical structures of the 19 testing set molecules 62

Fig 3-13. The regression of actual versus predicted activities by the Hypo1 hypothesis for the test set inhibitors onto a linear relationship 64

Fig 3-14. Mapping of compound 1, compound 2 and compound 8 onto Hypo1 67

Fig 3-15. Mapping of test compound 35, compound 36 and compound 47 onto Hypo1 68

Fig 3-16. NO.1 inhibitor of docking conformation aligned to pharmacophore model onto protein structure surface 69

Fig 3-17. NO.1 inhibitor of docking conformation aligned to pharmacophore model into protein structure 69

Fig 3-18. 2D chemical structures of the Indan derivative and MG-132 molecules 71

Fig 3-19. Mapping of NO.8 inhibitor docking conformation and diverse inhibitor onto hypothesis 71

Fig 3-20. NO.13 inhibitor bound to DNA-binding region of NF-κB structure used to GOLD by Ligplot 4.22 program 72

Fig 3-21. NF-ĸB inhibitors aligned to core structure by Sybyl7.3 75

Fig 3-22. Pharmacophore model compared with CoMFA and CoMSIA models 75

Fig 4-1. Rule of five for drug development 77

Fig 4-2. Virtual screening of NF-ĸB inhibitors by CATALYST and PHASE pharmacophore from Maybridge database 78

Fig 4-3. SBL004 compound bound to DNA-binding region of NF-κB structure used to GOLD by Ligplot 4.22 program 80

Fig 4-4. SBL003 compound bound to DNA-binding region of NF-κB structure used to SURFLEX by PyMoL 81

Fig 4-5. SBL004 compound bound to DNA-binding region of NF-κB surface used to GOLD by PyMoL 82

Fig A. Toll-like receptor signaling pathway 85

Fig B. 2D chemical structures of the 7 molecules by virtual screening from Maybridge database used to CATALYST and PHASE pharmacophore 86

表目錄

Table 1-1. Description of the QSAR results table columns 32

Table 3-1. Information of statistical significance of the training set in cost values for top 10 hypotheses 40

Table 3-2. PLS statistics of PHASE 3D-QSAR models and prediction of test set 42

Table 3-3. Comparison QSAR of PHASE and CATALYST of train set predictions 43

Table 3-4. Results from cross-validation using CatScramble in CATALYST 45

Table 3-5. Comparison QSAR of PHASE and CATALYST of test set predictions 48

Table 3-6. Summary of categorical NF-κB QSAR test set predictions 51

Table 3-7. Information of statistical significance of the training set in cost values for top 10 hypotheses 56

Table 3-8. Experimental biological data and estimated IC50 values of the training set molecules based on the pharmacophore model Hypo1 58

Table 3-9. Results from cross-validation using CatScramble in CATALYST 61

Table 3-10. Experimental biological data and estimated IC50 values of the test set molecules based on the pharmacophore model Hypo1 63

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