針對farnesyl轉移酶抑制劑的分子利用3D-QSAR技術建立藥效基團模型(Applying 3D-QSAR technique to construct the pharmacophore model of farnesyltransferase inhibitors)

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研究生：	陳冠如 Kuan-Ju Chen
論文名稱：	針對farnesyl轉移酶抑制劑的分子利用3D-QSAR技術建立藥效基團模型(Applying 3D-QSAR technique to construct the pharmacophore model of farnesyltransferase inhibitors) Applying 3D-QSAR technique to construct the pharmacophore model of farnesyltransferase inhibitors
指導教授：	林志侯 Thy-Hou Lin
口試委員:
學位類別：	碩士 Master
系所名稱：	生命科學暨醫學院 - 分子醫學研究所 Institute of Molecular Medicine
論文出版年：	2008
畢業學年度：	96
語文別：	英文
論文頁數：	90
中文關鍵詞：	比較分子場分析、比較分子相似因子分析、Catalyst 、三維空間定量結構活性關係、farnesyl轉移脢
外文關鍵詞：	CoMFA, CoMSIA, Catalyst, 3D QSAR, farnesyltransferase
相關次數：	點閱：3 下載：0
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摘要
以一系列總共68個farnesyl轉移脢抑制劑為基礎，經由比較分子場分析、比較分子相似因子分析、與Catalyst程式建立了數個三維空間定量結構活性關係，試圖找出合理且具有可信度之藥效活性基團的分布。farnesyl轉移脢被認為在與Ras有關的癌症生成中扮演重要角色。由一個farnesyl轉移脢的晶體結構當作模板，並利用嵌合程式GOLD來建立其餘的抑制劑三維結構。由比較分子場分析、比較分子相似因子分析的方法所建立的模型互相吻合且套回farnesyl轉移脢活性部位與蛋白質特性相互比較討論。Catalyst的部份則由比較分子相似因子分析的最佳的結果來選擇使用的結構特性，Catalyst產生的藥效基團模型也被套回farnesyl轉移脢的活性區，進而和比較分子場分析、比較分子相似因子分析所產生的模型比較，並討論了從比較分子相似因子分析來選擇Catalyst要使用的結構特性之可行性。另一方面，將比較分子相似因子分析與Catalyst結合，除了能夠提昇統計上的結果之外，也能對抑制劑與蛋白質之間的作用力提供更全面的資訊。經由此篇比較分子場分析、比較分子相似因子分析、與Catalyst的結果相信可以增加找到更有潛力的抗癌抑制劑的可能性。

Abstract
A set of 68 imidazole and cyanophenyl containing farnesyltransferase (FTase) inhibitors were subjected to three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using the comparative molecular field analysis (CoMFA) , comparative molecular similarity indices analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these inhibitors were generated theoretically, and the conformations used in the 3D-QSAR studies were defined by docking them into the known structure of FTase binding pocket through GOLD3.1. The models constructed by CoMFA and CoMSIA were found to be conformed to each other and were both fitted in with the property potential surface of FTase active site. These pharmacophore features were also compared with those obtained by the Catalyst program and superimposed on the receptor site of FTase. All of the pharmacophore features are in well agreement with structural characteristics and in accord with each other. In addition, we provided a consensus between CoMSIA and Catalyst help improved the statistical results. The final 3D-QSAR models and the information of the inhibitor–receptor interaction would be guide the design of new drug leads against FTase activity.

Table of Contents
摘要    i
Abstract    ii
謝誌    iii
Table of Contents    iv
List of Tables    vii
List of Figures    ix
Chapter 1  Introduction    1
Chapter 2  Materials    5
2.1 Inhibitors    5
2.2 Software and Modules    6
2.2.1 GOLD 3.1    6
2.2.2 CoMFA    6
2.2.3 CoMSIA    7
2.2.4 SYBYL PLS    8
2.2.4.1 Cross-Validation    8
2.2.4.2 No validation    10
2.2.5 Catalyst4.9    10
2.2.6 catScramble    14
Chapter 3  Methods    15
3.1 Prepare protein structure    15
3.2 Generation of ligand structures    16
3.3 Molecular docking    17
3.4 CoMFA    18
3.5 CoMSIA    19
3.6 Generation of Catalyst hypothesis    20
3.7 catScramble    21
3.8 Consensus CoMSIA and Catalyst model    22
Chapter 4  Results and discussion    24
4.1 Docked conformation and statistics of CoMFA and CoMSIA    24
4.2 Mapping of CoMFA and CoMSIA model    26
4.2.1 Mapping CoMFA contours and protein surface    26
4.2.2 Mapping CoMSIA contours and protein surface    27
4.2.2.1 Electrostatic and steric effect    27
4.2.2.2 Hydrophobic interaction    28
4.3 Comparing of the structures and CoMFA contours    29
4.4 Generation of Catalyst hypotheses    31
4.5 Mapping of Hypothesis Generated by Catalyst    33
4.6 Statistic results of CoMFA, CoMSIA, and Catalyst    35
4.7 Consensus of CoMSIA and Catalyst    37
Chapter 5 Conclusion    38
Reference    87

                                

Reference

1. Christoph, W. M.; Michael, A. M.; Lothar, B. Targeting the Ras signaling pathway: a rational, mechanism-based treatment for hematologic malignancies? Blood. 2000, 96, 1655-1669.
2. Reid, T. S.; Beese, L. S. Crystal structures of the anticancer clinical candidates R115777 (Tipifarnib) and BMS-214662 complexed with protein farnesyltransferase suggest a mechanism of FTI selectivity. Biochemistry. 2004, 43, 6877-6884.
3. Brunner, T. B.; Hahn, S. M.; Gupta, A. K.; Muschel, R. J.; McKenna, W. G.; Bernhard, E. J. Farnesyltransferase inhibitors: an overview of the results of preclinical and clinical investigations. Cancer Res. 2003, 63, 5656-68.
4. Baker, V. R. Geological fluvial geomorphology. Geological Society of America Bulletin. 1988, 100, 1157.
5. Roberts, P.J.; Der, C. J. Targeting the Raf-MEK-ERK mitogen-activated protein kinase cascade for the treatment of cancer. Oncogene. 2007, 26, 3291-3310.
6. Chen, W. J.; Andres, D. A.; Goldstein, J. L.; Brown. M. S. Cloning and expression of a cDNA encoding the alpha subunit of rat p21ras protein farnesyltransferase. Proc. Natl. Acad. Sci. U S A. 1991, 88, 11368-11372.
7. Chen, W. J.; Andres, D. A.; Goldstein, J. L.; Russell, D. W.; Brown, M. S. cDNA cloning and expression of the peptide-binding beta subunit of rat p21ras farnesyltransferase, the counterpart of yeast DPR1/RAM1. Cell. 1991, 66, 327-334.
8. Dunten, P.; Kammlott, U.; Crowther, R.; Weber, D.; Palermo, R.; Birkroft, J. Protein farnesyltransferase: structure and implications for substrate binding. Biochemistry. 1998, 37, 7907-7912.
9. Leftheris, K.; Kline, T.; Natarajan, S.; DeVirgilio, M. K.; Cho, Y. H.; Pluscec, J.; Ricca, C.; Robinson, S. Peptide based P21RAS farnesyl transferase inhibitors: systematic modification of the tetrapeptide CA1A2X motif. Bioorg. Med. Chem. Lett. 1994, 4, 887-892.
10. Caliendo, G.; Fiorino, F.; Grieco, P.; Perissutti, E.; De Luca, S.; Giuliano, A.; Santelli, G.; Califano, D.; Severino, B.; Santagada, V. Synthesis and biological activity of pseudopeptides inhibitors of Ras farnesyl transferase containing unconventional amino acids. Farmaco. 1999, 54, 785-790.
11. Santagada, V.; Caliendo, G.;Severino, B.; Perissutti, E.; Ceccarelli, F.; Giusti, L.; Mazzoni, M. R.; Salvadori, S.;Temussi, P. A. Probing the shape of a hydrophobic pocket in the active site of delta-opioid antagonists. J. Pept. Sci. 2001, 7, 374-385.
12. Lee, H. Y.; Sohn, S. H.; Kwon, B. M. Development of tripeptidyl farnesyltransferase inhibitors. Bioorg. Med. Chem. Lett. 2002, 12, 1599-1602.
13. Hunt, J. T.; Lee, V. G.; Leftheris, K.; Seizinger, B.; Carboni, J.; Mabus, J.; Ricca, C.; Yan, N.; Manne, V. Potent, cell active, non-thiol tetrapeptide inhibitors of farnesyltransferase. J. Med. Chem. 1996, 39, 353-358.
14. Dinsmore, C. J.; Williams, T. M.; O'Neill, T. J.; Liu, D.; Rands, E.; Culberson, J. C.; Lobell, R. B.; Koblan, K. S.; Kohl, N. E.; Gibbs, J. B.; Oliff, A. I.; Graham, S. L.; Hartman, G. D. Imidazole-containing diarylether and diarylsulfone inhibitors of farnesyl-protein transferase. Bioorg. Med. Chem. Lett. 1999, 9, 3301-3306.
15. Bergman, J. M.; Abrams, M. T.; Davide, J. P.; Greenberg, I. B.; Robinson, R. G.; Buser, C. A.; Huber, H. E.; Koblan, K. S.; Kohl, N. E.; Lobell, R. B.; Graham, S. L.; Hartman, G. D.; Williams, T. M.; Dinsmore, C. J. Aryloxy substituted N-arylpiperazinones as dual inhibitors of farnesyltransferase and geranylgeranyltransferase-I. Bioorg. Med. Chem. Lett. 2001, 11, 1411-1415.
16. Tong, Y.; Lin, N. H.; Wang, L.; Hasvold, L.; Wang, W.; Leonard, N.; Li, T.; Li, Q.; Cohen, J.; Gu, W. Z.; Zhang, H.; Stoll, V.; Bauch, J.; Marsh, K.; Rosenberg, S. H.; Sham, H. L., Discovery of potent imidazole and cyanophenyl containing farnesyltransferase inhibitors with improved oral bioavailability. Bioorg. Med. Chem. Lett. 2003, 13,1571-1574.
17. Wang, L.; Gary, T. W.; Wang, X.; Tong, Y.; Gerry, S.; David P.; Nicholas, M.; Li, Q.; Jerry, C.; Gu, W. Z.; Zhang, H.; Bauch, J.; Jakob, C. G.; Charles, W. H.; Vincent, S.; Kennan, M.; Saul, H. R.; Sham, H. L.; Lin, N. H. Design, synthesis, and biological activity of 4-[(4-cyano-2-arylbenzyloxy)-(3-methyl-3H-imidazol-4-yl)methyl]benzonitril es as potent and selective farnesyltransferase inhibitors. J. Med. Chem. 2004, 47, 612-626.
18. Cramer, R. D., III; Patterson, D. E.; Bunce. J. D. Recent advances in comparative molecular field analysis (CoMFA). Prog. Clin. Biol. Res. 1989, 291, 161-165.

19. Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem. 1994, 37, 4130-4146.
20. SYBYL 8.0; The Tripos Associates, 1699 S. Hanley Rd., St. Louis, MO.
21. Catalyst, version 4.9 (software package); Accelrys, Inc. (previously known as Molecular Simulations, Inc.): San Diego, CA, 2003. http://accelrys.com/. [cited 2008 July.22]
22. Kholodovych, V. GENETIC OPTIMIZATION FOR LIGAND DOCKING G.O.L.D. TUTORIAL. http://www2.umdnj.edu/~kholodvl/tut/gold_tutorial.pdf. [cited 2008 August. 17]
23. GOLD User Guide & Tutorials. 2006: The Cambirdge Crystallographic Data Centre
24. Cramer, R. D., III; Patterson, D. E.; Bunce, J. D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Amer. Chem. Soc. 1988, 110, 5959-5967.
25. Wold, S.; C.A.; Dunn, W. J., III; Edlund, U.; Esbensen, K.; Geladi, P.; Hellberg, S.; Johansson, E.; Lindberg, W.; Sjostrom, M. Multivariate Data Analysis in Chemistry. CHEMOMETRICS: Mathematics and Statistics in Chemistry, 1984.
26. Stahle, L.; Wold, S. Multivariate data analysis and experimental design in biomedical research. Prog. Med. Chem. 1988, 25, 291-338.
27. Klebe, G.; Abraham, U.; Mietzner, T. Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity. J. Med. Chem. 1994, 37, 4130-4146.
28. Klebe, G.; Abraham, U. Comparative molecular similarity index analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries. J. Comput. Aided. Mol. Des. 1999, 13, 1-10.
29. Durdagi, S.; Kapou, A.; Kourouli, T.; Andreou, T.; Nikas, S. P.; Nahmias, V. R.; Papahatjis, D. P.; Papadopoulos, M. G.; Mavromoustakos, T. The application of 3D-QSAR studies for novel cannabinoid ligands substituted at the C1' position of the alkyl side chain on the structural requirements for binding to cannabinoid receptors CB1 and CB2. J. Med. Chem. 2007, 50, 2875-2885.
30. SYBYL, The Tripos Boodshelf 7.2. 2006: Tripos Inc.
31. Catalyst Tutorials. http://www.scripps.edu/rc/softwaredocs/msi/catalyst45/tutorials/Catalyst4.5TOC.fm.html. [cited 2008, July, 15]
32. Wang, J.; P. C.; Kollman, P. A. How well does a restrained electrostatic potential (resp) model perform in calculating conformational energies of organic and biological molecules. J. Comp. Chem. 2000, 21, 1049-1074.
33. Jakalian, A.; Bush, B. L.; Jack, D. B.; Bayly, C. I. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method. J. Comput. Chem. 2000, 21, 132-146.
34. Jakalian, A.; Jack, D. B.; Bayly, C. I. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J. Comput. Chem. 2002, 23, 1623-1641.
35. Ghose, A. K.; Crippen, G. M. Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. J. Chem. Inf. Comput. Sci. 1987, 27, 21-35.

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