本研究中，我們使用了電腦模擬技術研究有潛力發展出藥物的蛋白質-小分子複合體，特別專注於研究競爭型的小分子抑制劑。熱力學積分及分子嵌合計算是本論文所使用的兩個主要計算方法，我們利用這兩個計算方法研究了三個蛋白質系統，介紹如下:
肝醣合成酶激酶-3(GSK-3) 我們藉由熱力學積分及分子嵌合去研究新型態的GSK-3β 抑制劑。首先從小分子資料庫中收集數百萬個化合物並進行嵌合計算篩選化合物，並選擇5個高評分的分子進行熱力學積分的計算，估計其結合強度及預測其結合位向，計算所得結合強度較強的分子將建議給實驗學家進行酵素實驗。在計算的5個分子中，預測了1個化合物的Kd值接近M，預測的結合位向結果可進一步運用於類似物抑制劑的開發上。
半乳糖苷酶(Galactosidase) 我們藉由嵌合計算去計算幾種已知抑制劑並協助設計產生更好的衍生物。首先，先預測了已知抑制劑的結合位向，藉由疊圖與氫鍵分析分析半乳糖苷酶在活性位置的作用情形。之後，進一步地設計了13個相關的衍生物分子，並推薦了1個分子給實驗學家做酵素實驗。
吲哚胺-吡咯2，3-雙加氧酶(IDO) 我們使用了共價嵌合計算去輔助設計具有共價鍵的抑制劑。總共彙整了3類抑制劑分子共38種化合物並使用共價嵌合計算來進行排序，這些化合物的排序有助於開發類似的共價抑制劑。
關鍵字: GSK-3β 蛋白質激酶、IDO、 半乳糖苷酶、分子嵌合、豐富指數、虛擬篩選、分子資料庫

In this study, we utilized computer simulation to investigate protein-ligand complexes that have potential to develop drugs. We focused on identifying new small molecule inhibitors with competitive mechanism. Thermodynamic integration (TI) MD simulation and molecular docking computation are two main computational methods for these investigations. The three examined protein systems are described below.
GSK-3β kinase We aimed to identify new type GSK-3β kinase inhibitors by using docking computation and TI MD simulation. First, docking computations was carried out to screen millions of compounds. Selected top-ranked compounds are subject to TI-MD simulation in order to refine their estimated binding affinity with GSK3 kinase and predict their binding modes. Compounds of strong affinity were suggested to further experimental validation. Among examined 5 compounds, the computations predicted 1 compound with strong affinity. The predicted binding modes should aid in further development of analog inhibitors.
Galactosidase We utilized docking computation to aid in design of analogous, better inhibitors of a few known inhibitors. The binding modes were first examined and their interactions with galactosidase were analyzed. Among 13 designed derivative compounds, the computation predicted 1 compounds enhancing the inhibition. One compound was subject to experimental assay and exhibited enhanced inhibition.
Indoleamine 2,3-dioxygenase (IDO) For this enzyme, we used covalent docking to aid in design of covalent inhibitors. Several classes of potential covalent inhibitors in total of 38 compounds were investigated. Ranking of these compounds should be useful for development of effective, analogous covalent inhibitors of the examined compounds.
Keywords : GSK-3β, IDO, thermodynamic integration, MD simulation, Galactosidase, virtual screening, docking

目錄
中文摘要 1
ABSTRACT 3
目錄 5
表目錄 8
圖目錄 11
Chapter 1 緒論 Introduction 14
1.1　前言 (Preface) 14
1.2 GSK-3 (Glycogen synthase kinase) 15
1.3 Galactosidase 16
1.4 IDO (Indoleamine 2,3-dioxygenase) 17
1.5 小分子與蛋白質結合自由能計算 (Free energy calculation) 18
1.6 研究目標 (Research Objective) 19
Chapter 2 理論與方法 Theory and Methods 20
2.1 分子嵌合計算 (Molecular docking) 20
2.2 基因演算法 21
2.3 評分函數 23
2.4 豐富指數(EF)、ROC 曲線及AUC 25
2.5 均方根差(Root Mean Square Deviation , RMSD) 28
2.6 BindingDB資料庫(The Binding Database) 29
2.7 ZINC資料庫(ZINC Database) 30
2.7.1 ZINC 15 的GSK-3β篩選資料庫的設定 31
2.7.2 GSK-3β化學骨架搜尋 33
2.7.3 利用化學骨架的藥物篩選 35
2.7.4 化學骨架篩選分子的彙整 36
2.8 分子動力學模擬(Molecular dynamic, MD) 36
2.9 熱力學循環 (Thermodynamics cycle) 38
2.10 模擬系統 40
Chapter 3 GSK-3的結果與討論 The results and discussion of GSK-3 42
3.1 相對結合自由能的校準計算 42
3.1.1化合物ZRK在GSK-3β中三個構型的相對自由能差 42
3.2 篩選分子的相對自由能差 44
3.2.1 BZ 結合位向的計算結果 48
3.2.2 BZ1 結合位向的計算結果 50
3.2.3 BZ2 結合位向的計算結果 53
3.2.4 BZ3 結合位向的計算結果 57
3.2.5 OZ 結合位向的計算結果 59
3.3 篩選分子的氫鍵分析 61
3.3.1 ZRK 氫鍵分析 62
3.3.2 BZ 氫鍵分析 63
3.3.3 BZ1 氫鍵分析 64
3.3.4 BZ2 氫鍵分析 65
3.3.5 BZ3 氫鍵分析 67
3.5.6 OZ 氫鍵分析 68
3.4 篩選分子的相對結合自由能的彙整 69
3.5 設計分子的相對結合自由能差 71
3.5.1 pd1 結合位向的計算結果 73
3.5.2 pd2 結合位向的計算結果 76
3.6 設計分子的氫鍵分析 78
3.6.1 pd1 氫鍵分析 78
3.6.2 pd2 氫鍵分析 79
Chapter 4 Galactosidase的結果與討論The results and discussion of alpha- Galactosidase 80
4.1 alpha- 半乳糖苷酶嵌合計算的再現 80
4.2 抑制劑小分子結合構型的預測 82
4.4 H6小分子更動苯環官能基的嵌合計算 86
Chapter 5 IDO的結果與討論 The results of discussion of IDO 89
5.1 GOLD共價嵌合計算的再現 89
5.2 IDO 配體結合構型之再現 89
5.3 IDO 共價篩選分子 91
5.4 IDO 共價嵌合篩選 92
5.5 IDO 非共價鍵的數據集(data set) 97
5.6 IDO data set 1 的篩選測試 98
5.7 IDO data set 2 的篩選測試 101
Chapter 6 結論 Conclusion 105
References 107
附錄 Appendix 109
S1 GSK-3β化學骨架搜尋分子彙整 109
S2 藥篩分子的Common atom 彙整 124
S3 設計分子的Common atom 彙整 124
S4 考慮PH值效應的再現性嵌合計算 125

 

References
(1) Sarkar, S.; Rubinsztein, D. C. FEBS J. 2008, 275, 4263.
(2) Hanger, D. P.; Hughes, K.; Woodgett, J. R.; Brion, J.-P.; Anderton, B. H. Neurosci. Lett. 1992, 147, 58.
(3) Martinez, A.; Castro, A.; Dorronsoro, I.; Alonso, M. Med. Res. Rev. 2002, 22, 373.
(4) Cantley, L. C. Science 2002, 296, 1655.
(5) Martin, L.; Latypova, X.; Wilson, C. M.; Magnaudeix, A.; Perrin, M. L.; Yardin, C.; Terro, F. Ageing Res. Rev. 2013, 12, 289.
(6) Kint, J. A. Science 1970, 167, 1268.
(7) Okada, S.; O'Brien, J. S. Science 1968, 160, 1002.
(8) Suzuki, K.; Suzuki, Y. Proc. Natl. Acad. Sci. U.S.A 1970, 66, 302.
(9) Garman, S. C. Acta Paediatr. 2007, 96, 6.
(10) Eng, C. M.; Guffon, N.; Wilcox, W. R.; Germain, D. P.; Lee, P.; Waldek, S.; Caplan, L.; Linthorst, G. E.; Desnick, R. J. N. Engl. J. Med. 2001, 345, 9.
(11) Schiffmann, R.; Kopp, J. B.; Austin, H. A.; Sabnis, S.; Moore, D. F.; Weibel, T.; Balow, J. E.; Brady, R. O. J. Am. Med. Assoc. 2001, 285, 2743.
(12) Ringe, D.; Petsko, G. A. J. Biol. 2009, 8.
(13) Naik, S.; Zhang, N.; Gao, P.; Fisher, M. T. Curr. Top. Med. Chem. 2012, 12, 2504.
(14) Shin, M. H.; Lim, H. S. Mol. BioSyst. 2017, 13, 638.
(15) Munn, D. H.; Zhou, M.; Attwood, J. T.; Bondarev, I.; Conway, S. J.; Marshall, B.; Brown, C.; Mellor, A. L. Science 1998, 281, 1191.
(16) Prendergast, G. C. Oncogene 2008, 27, 3889.
(17) Austin, C. J. D.; Rendina, L. M. Drug Discov. Today 2015, 20, 609.
(18) Ciorba, M. A. Curr. Opin. Gastroenterol. 2013, 29, 146.
(19) Mobley, D. L.; Dill, K. A. Structure 2009, 17, 489.
(20) Yin, J.; Henriksen, N. M.; Slochower, D. R.; Shirts, M. R.; Chiu, M. W.; Mobley, D. L.; Gilson, M. K. J. Comput.-Aided Mol. Des. 2016, 31, 1.
(21) Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. D. Proteins 2003, 52, 609.
(22) Rose, P. W.; Beran, B.; Bi, C.; Bluhm, W. F.; Dimitropoulos, D.; Goodsell, D. S.; Prlić, A.; Quesada, M.; Quinn, G. B.; Westbrook, J. D.; Young, J.; Yukich, B.; Zardecki, C.; Berman, H. M.; Bourne, P. E. Nucleic Acids Res. 2011, 39, D392.
(23) Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Couch, G. S.; Greenblatt, D. M.; Meng, E. C.; Ferrin, T. E. J. Comput. Chem. 2004, 25, 1605.
(24) Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. J. Mol. Biol. 1997, 267, 727.
(25) Jones, G.; Willett, P.; Glen, R. C. J. Mol. Biol. 1995, 245, 43.
(26) Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. J. Med. Chem. 2004, 47, 1750.
(27) Hanley, J. A.; McNeil, B. J. Radiology 1982, 143, 29.
(28) Triballeau, N.; Acher, F.; Brabet, I.; Pin, J. P.; Bertrand, H. O. J. Med. Chem. 2005, 48, 2534.
(29) Gilson, M. K.; Liu, T.; Baitaluk, M.; Nicola, G.; Hwang, L.; Chong, J. Nucleic Acids Res. 2016, 44, D1045.
(30) Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.; Coleman, R. G. J. Chem. Inf. Model. 2012, 52, 1757.
(31) Sterling, T.; Irwin, J. J. J. Chem. Inf. Model. 2015, 55, 2324.
(32) Case, D. A.; Babin, V.; Berryman, J. T.; Betz, R. M.; Cai, Q.; Cerutti, D. S.; Cheatham, T. E.; Darden, T. A.; Duke, R. E.; Gohlke, H.; Goetz, A. W.; Gusarov, S.; Homeyer, N.; Janowski, P.; Kaus, J.; Kolossvary, I.; Kovalenko, A.; Lee, T. S.; LeGrand, S.; Luchko, T.; Luo, R.; Jadej, B.; Merz, K. M.; Paesani, F.; Roe, D. R.; Roitberg, A.; Sagui, C.; Salomon-Ferrer, R.; Seabra, G.; Simmerling, C. L.; Smith, W.; Swails, J.; Walker, R. C.; Wang, J.; Wolf, R. M.; Wu, X.; Kollman, P. A. AMBER14; University of California: San Francisco, 2014.
(33) Jakalian, A.; Bush, B. L.; Jack, D. B.; Bayly, C. I. J. Comput. Chem. 2000, 21, 132.
(34) Ouyang, X.; Zhou, S.; Ge, Z.; Li, R.; Kwoh, C. K. Nucleic Acids Res. 2013, 41, W329.
(35) Ouyang, X.; Zhou, S.; Su, C. T. T.; Ge, Z.; Li, R.; Kwoh, C. K. J. Comput. Chem. 2013, 34, 326.
(36) Kirton, S. B.; Murray, C. W.; Verdonk, M. L.; Taylor, R. D. Proteins 2005, 58, 836.