研究生: |
許文綺 Hsu, Wen-Chi |
---|---|
論文名稱: |
以熱力學積分分子動力學模擬計算吡唑噠嗪化合物與GSK-3β激酶的相對結合自由能 Relative Binding Free Energy Computation of GSK-3β Kinase-Ligand Complexes Using Thermodynamic Integration MD Simulation: Compounds with Pyrazolo-Pyridazin Fused Ring |
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: | 2015 |
畢業學年度: | 103 |
語文別: | 中文 |
論文頁數: | 75 |
中文關鍵詞: | 分子動力學模擬 、熱力學積分 、相對結合自由能 、肝醣合成激酶-3β |
英文關鍵詞: | Molecular Dynamics Simulation, Thermodynamic Integration, Relative Binding Free Energy, GSK-3β kinase |
論文種類: | 學術論文 |
相關次數: | 點閱:143 下載:0 |
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肝醣合成酶激酶 3 (glycogen synthase kinase 3, GSK-3),是一種絲胺酸/蘇胺酸蛋白質激酶,在阿茲海默症 (AD) 的發病機制上扮演著重要的角色。根據推測,當GSK-3β活性太高會導致tau 蛋白過度磷酸化因而產生AD症狀,如果能設計有效的抑制劑分子去抑制GSK-3β的活性,則可能達到減輕或治療AD疾病的效果。因此,我們使用熱力學積分ΔΔG的模擬方法去計算蛋白質與小分子間的相對結合自由能,藉此幫助我們去設計小分子抑制劑。首先,我們先計算已知實驗值的分子的結合親和力,所計算結果與實驗值有不錯的吻合。之後,我們利用相同計算程序去預測類似物分子的結合力,並且將這些分子推薦去做酵素實驗。我們總共計算了11個分子的相對自由能ΔΔG (kcal/mol)。有三個類似物分子 PP1、PP2和LL4 在酵素實驗上發現到有效的抑制能力。此外,我們對這三個類似物分別去探討不同的結合位向的關係。在不同結合位向計算上,我們發現計算LL4是以和ZRM相同的結合位向結合蛋白質;而PP1和PP2則是以不相同結合位向結合蛋白質。PP1、PP2和LL4的ΔΔG計算結果分別為5.1, 2.7和1.2 kcal/mol,實驗結果分別為3.7, 0.7和-0.4 kcal/mol,計算值和實驗值有一定的相關性,相關係數為0.83,在考慮不同位向計算上,docking結合TI方法的計算可以更準確預測到實驗結果。此外,在探討小分子與蛋白質的結合親和力關係上,我們建議在指向水的分子結構位置上加上親水性的官能基團,預測可能有效增進蛋白質與小分子間的結合親力。最後,我們希望本篇模擬結果提供的資訊,能在未來上更進一步幫助GSK-3β去設計有效的小分子抑制劑。
Glycogen synthase kinase 3 (GSK-3) is a serine/threonine kinase that plays an important role in the pathogenesis of Alzheimer's disease (AD). It was postulated that when GSK-3β kinase’s activity is too high, it over-phosphorylates tau protein and causes AD.Inhibition of GSK-3βkinase would provide therapeutic effect in alleviated and/or cure AD.In the present study, we used thermodynamic integration molecular dynamics (TI-MD) simulation to calculate protein-ligand binding free energy to aid in design of GSK-3β inhibitors. First, we computed the affinity of analogous GSK-3β kinase inhibitors of available experimental data and the computed results were in reasonably good agreement with the experimental value.Subsequently, we employed the same protocol in order to identify new analogous inhibitors which would be recommended for kinase experiment. We presented predicted results of relative binding free energy ΔΔG (kcal/mol) for 11 compounds. Three analogs PP1, PP2 and LL4 were found to be effective inhibitors in kinase assay experiment.In addition, the binding modes of these 3 compounds were investigated. It was found that the LL4 adopted binding mode similar to ZRM's but PP1 and PP2 is adopted different binding modes. The computed ΔΔG values of PP1,PP2 and LL4 were 5.1, 2.7, and 1.2 kcal/mol , correlating well with experiment data of 3.7, 0.7, and -0.4 kcal/mol, respectively. The correlation coefficient (R2) was 0.83. Analysis of interactions between ligands and GSK-3β kinase suggested that addition the hydrophilic group at the site of the functional group pointing towards water can enhance the binding affinity of lignad-protein binding. These results provide useful insight for further design of GSK-3β kinase inhibitors in the future.
1. Cohen, P., Protein kinases - the major drug targets of the twenty-first century? Nat Rev Drug Discov, 2002. 1(4): p. 309-315.
2. Mobley, D.L. and K.A. Dill, Binding of Small-Molecule Ligands to Proteins: “What You See” Is Not Always “What You Get”. Structure, 2009. 17(4): p. 489-498.
3. Hardy, J., A Hundred Years of Alzheimer's Disease Research. Neuron, 2006. 52(1): p. 3-13.
4. Yamaguchi, H., K. Ishiguro, T. Uchida, A. Takashima, C.A. Lemere, and K. Imahori, Preferential labeling of Alzheimer neurofibrillary tangles with antisera for tau protein kinase (TPK) I/glycogen synthase kinase-3β and cyclin-dependent kinase 5, a component of TPK II. Acta Neuropathologica, 1996. 92(3): p. 232-241.
5. Imahori, K. and T. Uchida, Physiology and pathology of tau protein kinases in relation to Alzheimer's disease. The Journal of Biochemistry, 1997. 121(2): p. 179-188.
6. Wagner, U., M. Utton, J.-M. Gallo, and C. Miller, Cellular phosphorylation of tau by GSK-3 beta influences tau binding to microtubules and microtubule organisation. Journal of Cell Science, 1996. 109(6): p. 1537-1543.
7. Avila, J., F. Wandosell, and F. Hernández, Role of glycogen synthase kinase-3 in Alzheimer's disease pathogenesis and glycogen synthase kinase-3 inhibitors. Expert Review of Neurotherapeutics, 2010. 10: p. 703-710.
8. Cho, J.H. and G.V. Johnson, Primed phosphorylation of tau at Thr231 by glycogen synthase kinase 3β (GSK3β) plays a critical role in regulating tau's ability to bind and stabilize microtubules. Journal of neurochemistry, 2004. 88(2): p. 349-358.
9. Welsh, G.I. and C. Proud, Glycogen synthase kinase-3 is rapidly inactivated in response to insulin and phosphorylates eukaryotic initiation factor eIF-2B. Biochem. J, 1993. 294: p. 625-629.
10. Amar, S., R. Belmaker, and G. Agam, The possible involvement of glycogen synthase kinase-3 (GSK-3) in diabetes, cancer and central nervous system diseases. Curr Pharm Des, 2011. 17(22): p. 2264-2277.
11. Steinbrecher, T. and A. Labahn, Towards Accurate Free Energy Calculations in Ligand Protein-Binding Studies. Curr. Med. Chem., 2010. 17(8): p. 767-785.
12. Deng, Y.Q. and B. Roux, Computations of Standard Binding Free Energies with Molecular Dynamics Simulations. J. Phys. Chem. B, 2009. 113(8): p. 2234-2246.
13. Jorgensen, W.L. and L.L. Thomas, Perspective on free-energy perturbation calculations for chemical equilibria. J. Chem. Theory Comput., 2008. 4(6): p. 869-876.
14. Christ, C.D., A.E. Mark, and W.F. van Gunsteren, Feature Article Basic Ingredients of Free Energy Calculations: A Review. J. Comput. Chem., 2010. 31(8): p. 1569-1582.
15. Nicholls, A., D.L. Mobley, J.P. Guthrie, J.D. Chodera, C.I. Bayly, M.D. Cooper, and V.S. Pande, Predicting small-molecule solvation free energies: an informal blind test for computational chemistry. Journal of medicinal chemistry, 2008. 51(4): p. 769-779.
16. Wong, C.F. and J. Andrew McCammon, Computer simulation and the design of new biological molecules. Israel Journal of Chemistry, 1986. 27(2): p. 211-215.
17. Espinoza-Fonseca, L.M., Thermodynamic aspects of coupled binding and folding of an intrinsically disordered protein: a computational alanine scanning study. Biochemistry, 2009. 48(48): p. 11332-11334.
18. Verdonk, M.L., J.C. Cole, M.J. Hartshorn, C.W. Murray, and R.D. Taylor, Improved protein-ligand docking using GOLD. Proteins-Struct. Funct. Gene., 2003. 52(4): p. 609-623.
19. Nissink, J.W.M., C. Murray, M. Hartshorn, M.L. Verdonk, J.C. Cole, and R. Taylor, A new test set for validating predictions of protein-ligand interaction. Proteins-Struct. Funct. Gene., 2002. 49(4): p. 457-471.
20. Srinivasan, J., M.W. Trevathan, P. Beroza, and D.A. Case, Application of a pairwise generalized Born model to proteins and nucleic acids: inclusion of salt effects. Theoretical Chemistry Accounts, 1999. 101(6): p. 426-434.
21. Kollman, P.A., I. Massova, C. Reyes, B. Kuhn, S. Huo, L. Chong, M. Lee, T. Lee, Y. Duan, and W. Wang, Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Accounts of chemical research, 2000. 33(12): p. 889-897.
22. Kirkwood, J.G., Statistical mechanics of fluid mixtures. The Journal of Chemical Physics, 1935. 3(5): p. 300-313.
23. Genheden, S., I. Nilsson, and U. Ryde, Binding affinities of factor Xa inhibitors estimated by thermodynamic integration and MM/GBSA. Journal of chemical information and modeling, 2011. 51(4): p. 947-958.
24. Gil L, A., P.A. Valiente, P.G. Pascutti, and T. Pons, Computational Perspectives into Plasmepsins Structure—Function Relationship: Implications to Inhibitors Design. Journal of tropical medicine, 2011. 2011.
25. Case, D.A., V. Babin, J.T. Berryman, R.M. Betz, Q. Cai, D.S. Cerutti, T.E. Cheatham, T.A. Darden, R.E. Duke, H. Gohlke, A.W. Goetz, S. Gusarov, N. Homeyer, P. Janowski, J. Kaus, I. Kolossvary, A. Kovalenko, T.S. Lee, S. LeGrand, T. Luchko, R. Luo, B. Jadej, K.M. Merz, F. Paesani, D.R. Roe, A. Roitberg, C. Sagui, R. Salomon-Ferrer, G. Seabra, C.L. Simmerling, W. Smith, J. Swails, R.C. Walker, J. Wang, R.M. Wolf, X. Wu, and P.A. Kollman, AMBER14. 2014, San Francisco: University of California.
26. Case, D.A., T. Darden, T. Cheatham, C.L. Simmerling, J. Wang, R.E. Duke, R. Luo, R. Walker, W. Zhang, and K. Merz, Amber 11, 2010, University of California.
27. Gentile, G., G. Bernasconi, A. Pozzan, G. Merlo, P. Marzorati, P. Bamborough, B. Bax, A. Bridges, C. Brough, and P. Carter, Identification of 2-(4-pyridyl) thienopyridinones as GSK-3β inhibitors. Bioorganic & medicinal chemistry letters, 2011. 21(16): p. 4823-4827.
28. Steinbrecher, T., D.L. Mobley, and D.A. Case, Nonlinear scaling schemes for Lennard-Jones interactions in free energy calculations. The Journal of chemical physics, 2007. 127(21): p. 214108.
29. Steinbrecher, T., I. Joung, and D.A. Case, Soft‐core potentials in thermodynamic integration: Comparing one‐and two‐step transformations. Journal of computational chemistry, 2011. 32(15): p. 3253-3263.
30. Kollman, P., Free energy calculations: applications to chemical and biochemical phenomena. Chemical reviews, 1993. 93(7): p. 2395-2417.
31. Lazareno, S. and N. Birdsall, Estimation of competitive antagonist affinity from functional inhibition curves using the Gaddum, Schild and Cheng‐Prusoíf equations. British journal of pharmacology, 1993. 109(4): p. 1110-1119.
32. Kaus, J.W., L.T. Pierce, R.C. Walker, and J.A. McCammon, Improving the Efficiency of Free Energy Calculations in the Amber Molecular Dynamics Package. J. Chem. Theory Comput., 2013. 9(9): p. 4131-4139.
33. Pearlman, D.A., D.A. Case, J.W. Caldwell, W.S. Ross, T.E. Cheatham, S. DeBolt, D. Ferguson, G. Seibel, and P. Kollman, AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Computer Physics Communications, 1995. 91(1): p. 1-41.
34. Pettersen, E.F., T.D. Goddard, C.C. Huang, G.S. Couch, D.M. Greenblatt, E.C. Meng, and T.E. Ferrin, UCSF Chimera—a visualization system for exploratory research and analysis. Journal of computational chemistry, 2004. 25(13): p. 1605-1612.
35. Cornell, W.D., P. Cieplak, C.I. Bayly, I.R. Gould, K.M. Merz, D.M. Ferguson, D.C. Spellmeyer, T. Fox, J.W. Caldwell, and P.A. Kollman, A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. Journal of the American Chemical Society, 1995. 117(19): p. 5179-5197.
36. Jakalian, A., D.B. Jack, and C.I. Bayly, Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: II. Parameterization and validation. Journal of computational chemistry, 2002. 23(16): p. 1623-1641.
37. Frisch, A. and M.J. Frisch, Gaussian 03 Pocket Reference. 2003: Gaussian, Incorporated.
38. Wang, J., R.M. Wolf, J.W. Caldwell, P.A. Kollman, and D.A. Case, Development and testing of a general amber force field. Journal of computational chemistry, 2004. 25(9): p. 1157-1174.
39. Jorgensen, W.L., J. Chandrasekhar, J.D. Madura, R.W. Impey, and M.L. Klein, Comparison of simple potential functions for simulating liquid water. The Journal of chemical physics, 1983. 79(2): p. 926-935.
40. Lee, H.C., W.C. Hsu, A.L. Liu, C.J. Hsu, and Y.C. Sun, Using Thermodynamic Integration MD Simulation to Compute Relative Protein-Ligand Binding Free Energy of a GSK3ß Kinase Inhibitor and its Analogs. J. Mol. Graph. Model., 2014. 51: p. 37-49.