研究生: |
謝育劭 HSIEH Yu-Shao |
---|---|
論文名稱: |
肝醣合成酶激酶-3β抑制劑搜尋分子嵌合之高速虛擬篩選計算研究 Virtual Screening of GSK-3β Kinase Inhibitors Using Docking Computation |
指導教授: |
孫英傑
Sun, Ying-Chieh |
學位類別: |
碩士 Master |
系所名稱: |
化學系 Department of Chemistry |
論文出版年: | 2013 |
畢業學年度: | 101 |
語文別: | 中文 |
論文頁數: | 53 |
中文關鍵詞: | GSK-3β蛋白激酶 、阿茲海默症 、分子嵌合 、豐富指數 、虛擬篩選 、分子資料庫 |
英文關鍵詞: | GSK-3β, Alzheimer’s disease, Docking, Enrichment factor, Virtual screening, Database |
論文種類: | 學術論文 |
相關次數: | 點閱:170 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
Glycogen synthase kinase-3β(GSK-3β)為一蛋白質激酶,在生物體中扮演著重要的角色,其作用機制失調與許多疾病諸如糖尿病、癌症以及阿茲海默症等有相關。在本篇研究中,我們利用分子嵌合計算來研究尋找新穎的GSK-3β抑制劑。首先,我們從蛋白質資料庫可以得到許多GSK-3β的結晶構型,從中挑選26個胺基酸序列完全相同的結晶構型,研究其再現性。第二,我們再選擇其中10個再現性表現最好的構型做交叉分子嵌合,藉此研究不同結晶構型之間,最能有效正確預測結合模式的蛋白質結構。第三,當選定蛋白質結構後,測試不同的活性中心氫鍵限制條件,我們從208有已知Ki值的GSK-3β抑制劑中,挑選出25個非類似結構的GSK-3β活性分子,再透過Directory of Useful Decoys-Expanded (DUD-E)產生decoys的方式,產生了955個decoys。建立出一套GSK-3β專用的標準測試的分子資料庫,在GSK-3β標準測試資料庫中測試不同氫鍵限制設定對於Enrichment factor (EF)的影響。我們得到最好的氫鍵限制設定可以提升Enrichment factor達7.84,篩選效率明顯高於其他設定。我們藉由以上較佳的條件選擇和設定,進行高速虛擬篩選,並且在ZINC資料庫、烏克蘭ENAMINE資料庫、ITRI工研院藥庫,總共挑選了270萬個化合物,這些化合物皆符合rule of five規則。從虛擬篩選的結果分析推薦具有潛力的化合物給實驗學家進行測試,並且在實驗測試的結果中發現數個具有抑制效果的分子。結果顯示本研究的虛擬篩選策略有助於實驗學家更有效率找到GSK-3β抑制劑並進一步做藥物設計。
GSK-3β is a kinase protein that plays significant role in many biochemical functions, whereas dysregulated GSK-3β is involved in human diseases such as diabetes, cancer, Alzheimer’s disease. This study used docking computation to aid in discovery of GSK-3β inhibitors.First, we carried out docking computations for 26 GSK-3β-inhibitor complexes with crystal structures available in the protein data bank in order to examine their reproducibility. Second, we choose 10 complexes to do cross docking and investigate how ligand conformations can be regained when protein structures from different complexes were used. Third, we employed standard decoy compounds generation tool provide by Directory of Useful Decoys-Expanded (DUD-E) for benchmark. The active compounds were collected from the Binding Database. Among the existing 208 active compounds with Ki values available, we selected 25 chemical structurally dissimilar compounds. We constructed a group of 980 compounds consisting 25 active and 955 decoy compounds, and see if 25 active compounds can be screened out in docking computation. Effects of different hinge hydrogen bond constraints settings were examined as well. We can enhance the Enrichment factor up to 7.84 with best hinge hydrogen bond constraints setting.We selected the best settings and carried out virtual screening for 2.7 million compounds that are in compliance with rule of five (RO5) from ZINC database, Ukraine ENAMINE database and ITRI database. Analysis of the results from the virtual screening of compounds, we recommended some potential inhibitor compounds to the experimental scientists. Several compounds showed good inhibition effect in the laboratory tests. The results of this study showed that virtual screening protocols used in the present study can help to find inhibitors of GSK-3β more efficiently, and can be subject to further inhibitor design.
1. Charifson, P.S., Practical application of computer-aided drug design / edited by Paul S. Charifson. New York : Marcel Dekker, c1997.
2. Taylor, R.D., P.J. Jewsbury, and J.W. Essex, A review of protein-small molecule docking methods. Journal of computer-aided molecular design, 2002. 16(3): p. 151-166.
3. Klebe, G., Virtual ligand screening: strategies, perspectives and limitations. Drug discovery today, 2006. 11(13-14): p. 580.
4. Frame, S. and P. Cohen, GSK3 takes centre stage more than 20 years after its discovery. Biochemical Journal, 2001. 359(Pt 1): p. 1.
5. Ougolkov, A.V. and D.D. Billadeau, Targeting GSK-3: a promising approach for cancer therapy? 2006.
6. Nikoulina, S.E., et al., Potential role of glycogen synthase kinase-3 in skeletal muscle insulin resistance of type 2 diabetes. Diabetes, 2000. 49(2): p. 263-271.
7. Lovestone, S., et al., Alzheimer's disease-like phosphorylation of the microtubule-associated protein tau by glycogen synthase kinase-3 in transfected mammalian cells. Current biology: CB, 1994. 4(12): p. 1077.
8. Frame, S., P. Cohen, and R.M. Biondi, A common phosphate binding site explains the unique substrate specificity of GSK3 and its inactivation by phosphorylation. Molecular cell, 2001. 7(6): p. 1321-1327.
9. Balaraman, Y., et al., Glycogen synthase kinase 3β and Alzheimer’s disease: pathophysiological and therapeutic significance. Cellular and Molecular Life Sciences CMLS, 2006. 63(11): p. 1226-1235.
10. Zhang, H.-C., et al., Novel bis (indolyl) maleimide pyridinophanes that are potent, selective inhibitors of glycogen synthase kinase-3. Bioorganic & medicinal chemistry letters, 2007. 17(10): p. 2863-2868.
11. Kang, J., et al., The precursor of Alzheimer's disease amyloid A4 protein resembles a cell-surface receptor. 1987: p. 733-736.
12. Wood, J.G., et al., Neurofibrillary tangles of Alzheimer disease share antigenic determinants with the axonal microtubule-associated protein tau (tau). Proceedings of the National Academy of Sciences, 1986. 83(11): p. 4040-4043.
13. Takashima, A., et al., Activation of tau protein kinase I/glycogen synthase kinase-3beta by amyloid beta peptide (25-35) enhances phosphorylation of tau in hippocampal neurons. Neuroscience research, 1998. 31(4): p. 317.
14. Van Wauwe, J. and B. Haefner, Glycogen synthase kinase-3 as drug target: from wallflower to center of attention. Drug News Perspect, 2003. 16(9): p. 557-565.
15. Makino, S. and I.D. Kuntz, Automated flexible ligand docking method and its application for database search. Journal of Computational Chemistry, 1997. 18(14): p. 1812-1825.
16. Morris, G.M., et al., Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of computational chemistry, 1998. 19(14): p. 1639-1662.
17. Friesner, R.A., et al., Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of medicinal chemistry, 2004. 47(7): p. 1739-1749.
18. Jones, G., et al., Development and validation of a genetic algorithm for flexible docking. Journal of molecular biology, 1997. 267(3): p. 727-748.
19. Huang, N., B.K. Shoichet, and J.J. Irwin, Benchmarking sets for molecular docking. Journal of medicinal chemistry, 2006. 49(23): p. 6789-6801.
20. Mysinger, M.M., et al., Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. Journal of Medicinal Chemistry, 2012. 55(14): p. 6582-6594.
21. Schlessinger, A. and B. Rost, Protein flexibility and rigidity predicted from sequence. Proteins: Structure, Function, and Bioinformatics, 2005. 61(1): p. 115-126.
22. Perola, E., Minimizing false positives in kinase virtual screens. Proteins: Structure, Function, and Bioinformatics, 2006. 64(2): p. 422-435.
23. Jones, G., P. Willett, and R.C. Glen, Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. Journal of molecular biology, 1995. 245(1): p. 43-53.
24. Verdonk, M.L., et al., Improved protein–ligand docking using GOLD. Proteins: Structure, Function, and Bioinformatics, 2003. 52(4): p. 609-623.
25. Eldridge, M.D., et al., Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. Journal of computer-aided molecular design, 1997. 11(5): p. 425-445.
26. Cheng, T., et al., Comparative assessment of scoring functions on a diverse test set. Journal of chemical information and modeling, 2009. 49(4): p. 1079-1093.
27. Kim, H.-J., et al., Novel GSK-3B inhibitors from sequential virtual screening. Bioorganic & medicinal chemistry, 2008. 16(2): p. 636-643.
28. Huang, S.-Y. and X. Zou, Advances and challenges in protein-ligand docking. International journal of molecular sciences, 2010. 11(8): p. 3016-3034.
29. Cavasotto, C.N. and R.A. Abagyan, Protein flexibility in ligand docking and virtual screening to protein kinases. Journal of molecular biology, 2004. 337(1): p. 209-226.
30. Lipinski, C.A., et al., Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews, 1997. 23(1): p. 3-25.
31. Mensch, J., et al., In vivo, in vitro and in silico methods for small molecule transfer across the BBB. Journal of pharmaceutical sciences, 2009. 98(12): p. 4429-4468.
32. Whitley, K.M., Analysis of SciFinder Scholar and Web of Science citation searches. Journal of the American Society for Information Science and Technology, 2002. 53(14): p. 1210-1215.
33. Jordis, U., Reaxys. Chemistry in Britain, 2009. 6(10): p. 71.