癌症一直為十大死因之一。因此與癌症有關的標靶藥物研發是目前國際間藥物化學家研究的主要方向之一。由於在實驗上合成藥物需要大量的人力、物力。因此若可以使用電腦來輔助藥物設計、藥物探索，不僅可以縮短研發的時程，更可以大量減少有機合成研發的成本。蛋白質激酶目前被認為與許多癌症有關。在本篇論文中，我們使用電腦輔助的方法來探討化學分子與蛋白質激酶間的作用力型態以及如何提升篩選具有生物活性的小分子。我們使用兩種理論方法，分別是熱力學積分及分子嵌合。

　　在熱力學積分中，我們所針對的蛋白質標的為ERK2。我們以原本已知的活性分子開始。預測了一系列新的類似物，這些類似物是以苯環上的極性或非極性官能基做為區別。在我們的結果中，羥基(OH) 取代後發現其與蛋白質間的結合能力約比之前研究中的抑制劑分子好2 kcal/mol，而其他官能基的取代約增進1 kcal/mol。我們的預測結果可為實驗學家提供有機合成、蛋白質與細胞實驗的理論依據。

　　除了分子動力學之外，我們也以分子嵌合計算探討FGFR1蛋白質幾個效應對豐富指數(enrichment factor)的影響。豐富指數為評價虛擬篩選優劣的指標之一。在豐富指數的研究中，我們採用DUD資料庫中活性及非活性的分子及設定不同的蛋白質條件。在設定條件時發現擺動514號胺基酸(賴氨酸)能夠大幅度增加正確挑選出活性分子的數量。並且設定絞鍊片段上564號胺基酸(丙胺酸)的氫鍵限制以進一步探究篩選效率。從本論文的研究中，我們尋找到最佳的條件，也希望這些最佳化條件可以進一步成為此類蛋白激酶大量虛擬篩選中的基礎。進而幫助先導化合物的找尋。

Cancer has been one of the top ten leading causes of death for several decades. The
target drugs research for cancer therapy is now a popular field among international
medicinal chemists. Because of the significant amount of money and human resources
spent in the drug development process, computer-aided drug design method is an
attractive tool to reduce cost and assist drug discovery. Protein kinases are one of the
protein families which are drug targets for cancer therapy. Here, we selected two kinases,
which are ERK2 and FGFR1 kinases, and used computer modeling to investigate binding
energy of inhibitor-protein complexes for these two kinases.
In the part of ERK2, we used thermodynamic integration MD method to compute
relative binding free energy of several ERK2-inhibitor complexes of interest. We carried
out computations to predict G for new analogs, focusing on placing polar and nonpolar
functional groups at the meta site of benzene ring, to see if these ligands have better
binding affinity than the above ligands. The computations resulted that a ligand with polar
–OH group has better binding affinity than the previous examined ligand by ~2.0
kcal/mol and two other ligands have better affinity by ~1.0 kcal/mol. The predicted better
inhibitors of this kind should be of interest to experimentalists for future experimental
enzyme and/or cell assays.
In addition to TI-MD simulation, we also worked on interactions of FGFR1
kinase-inhibitor complexes using docking computation, focusing on how enrichment
factor (EF) enhances in virtual screening by including side chain movement and applying
hydrogen bond constraint for this kinase. To this end, active and decoy compounds from
the Directory of Useful Decoys 1 database was obtained and benchmarked with GOLD
program. Interestingly, among combinations of side chains which were allowed to move,
EF is significantly higher with movement of Lys514 compared with others. In addition,
the effect of adding hydrogen bond constraint at a residue located in the hinge segment,
Ala564, was also examined. The results were analyzed and discussed. The present results
should be useful for virtual screening of large databases against this kinase.

1 Barrett, S. D. et al. The discovery of the benzhydroxamate MEK inhibitors CI-1040 and PD 0325901. Bioorganic & Medicinal Chemistry Letters 18, 6501-6504, doi:10.1016/j.bmcl.2008.10.054 (2008).
2 Manning, G., Whyte, D. B., Martinez, R., Hunter, T. & Sudarsanam, S. The Protein Kinase Complement of the Human Genome. Science 298, 1912-1934, doi:10.1126/science.1075762 (2002).
3 Gschwind, A., Fischer, O. M. & Ullrich, A. The discovery of receptor tyrosine kinases: targets for cancer therapy. Nat Rev Cancer 4, 361-370 (2004).
4 Cohen, P. Protein kinases [mdash] the major drug targets of the twenty-first century? Nat Rev Drug Discov 1, 309-315 (2002).
5 Steelman, L. S. et al. JAK//STAT, Raf//MEK//ERK, PI3K//Akt and BCR-ABL in cell cycle progression and leukemogenesis. Leukemia 18, 189-218 (0000).
6 McCubrey, J. A. et al. Roles of the Raf/MEK/ERK pathway in cell growth, malignant transformation and drug resistance. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research 1773, 1263-1284, doi:10.1016/j.bbamcr.2006.10.001 (2007).
7 Kohno, M. & Pouyssegur, J. Targeting the ERK signaling pathway in cancer therapy. Annals of Medicine 38, 200-211, doi:doi:10.1080/07853890600551037 (2006).
8 Khan, T. K. & Alkon, D. L. An internally controlled peripheral biomarker for Alzheimer’s disease: Erk1 and Erk2 responses to the inflammatory signal bradykinin. Proceedings of the National Academy of Sciences 103, 13203-13207, doi:10.1073/pnas.0605411103 (2006).
9 Matsushita, T. et al. Extracellular Signal-Regulated Kinase 1 (ERK1) and ERK2 Play Essential Roles in Osteoblast Differentiation and in Supporting Osteoclastogenesis. Molecular and Cellular Biology 29, 5843-5857, doi:10.1128/mcb.01549-08 (2009).
10 Barr, D. et al. Importance of Domain Closure for the Autoactivation of ERK2. Biochemistry 50, 8038-8048, doi:10.1021/bi200503a (2011).
11 Aronov, A. M. et al. Flipped Out:  Structure-Guided Design of Selective Pyrazolylpyrrole ERK Inhibitors‡. Journal of Medicinal Chemistry 50, 1280-1287, doi:10.1021/jm061381f (2007).
12 Kiwiel, K. C. Convergence and efficiency of subgradient methods for quasiconvex minimization. Mathematical Programming 90, 1-25, doi:10.1007/pl00011414 (2001).
13 Knyazev, A. V. & Lashuk, I. Steepest Descent and Conjugate Gradient Methods with Variable Preconditioning. SIAM J. Matrix Anal. Appl. 29, 1267-1280, doi:10.1137/060675290 (2007).
14 Kellenberger, E., Rodrigo, J., Muller, P. & Rognan, D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins: Structure, Function, and Bioinformatics 57, 225-242, doi:10.1002/prot.20149 (2004).
15 Leach, A. Molecular modelling : principles and applications. (Pearson Prentice Hall, 2001).
16 Kollman, P. A. et al. Calculating Structures and Free Energies of Complex Molecules:  Combining Molecular Mechanics and Continuum Models. Accounts of Chemical Research 33, 889-897, doi:10.1021/ar000033j (2000).
17 Michel , J., Foloppe, N. & Essex, J. W. Rigorous Free Energy Calculations in Structure-Based Drug Design. Molecular Informatics 29, 570-578, doi:10.1002/minf.201000051 (2010).
18 Christ, C. D., Mark, A. E. & van Gunsteren, W. F. Basic ingredients of free energy calculations: A review. Journal of Computational Chemistry 31, 1569-1582, doi:10.1002/jcc.21450 (2010).
19 Steinbrecher, T. & Labahn, A. Towards Accurate Free Energy Calculations in Ligand Protein-Binding Studies. Current medicinal chemistry 17, 767-785, doi:citeulike-article-id:9520323 (2010).
20 Case, D. et al. Amber 11.
21 Duan, Y. et al. A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. Journal of Computational Chemistry 24, 1999-2012, doi:10.1002/jcc.10349 (2003).
22 Steinbrecher, T., Mobley, D. L. & Case, D. A. Nonlinear scaling schemes for Lennard-Jones interactions in free energy calculations. The Journal of Chemical Physics 127, 214108-214113 (2007).
23 Miyamoto, S. & Kollman, P. A. Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. Journal of Computational Chemistry 13, 952-962, doi:10.1002/jcc.540130805 (1992).
24 Quigley, D. & Probert, M. I. J. Langevin dynamics in constant pressure extended systems. The Journal of Chemical Physics 120, 11432-11441 (2004).
25 Del Rio, A., Baldi, B. F. & Rastelli, G. Activity Prediction and Structural Insights of Extracellular Signal-Regulated Kinase 2 Inhibitors with Molecular Dynamics Simulations. Chemical Biology & Drug Design 74, 630-635, doi:10.1111/j.1747-0285.2009.00903.x (2009).
26 Kim, M.-h., Chung, J. Y., Ryu, J.-S. & Hah, J.-M. Structure tuning of pyrazolylpyrrole derivatives as ERK inhibitors utilizing dual tools; 3D-QSAR and side-chain hopping. Bioorganic & Medicinal Chemistry Letters 21, 4900-4904, doi:10.1016/j.bmcl.2011.06.016 (2011).
27 Szebenyi, G. & Fallon, J. F. in International Review of Cytology Vol. Volume 185 (ed W. Jeon Kwang) 45-106 (Academic Press, 1998).
28 Powers, C. J., McLeskey, S. W. & Wellstein, A. Fibroblast growth factors, their receptors and signaling. Endocrine-Related Cancer 7, 165-197, doi:10.1677/erc.0.0070165 (2000).
29 Webster, M. K. & Donoghue, D. J. FGFR activation in skeletal disorders: Too much of a good thing. Trends in Genetics 13, 178-182, doi:10.1016/s0168-9525(97)01131-1 (1997).
30 Wilkie, A. O. M. Craniosynostosis: Genes and Mechanisms. Human Molecular Genetics 6, 1647-1656, doi:10.1093/hmg/6.10.1647 (1997).
31 Dailey, L., Ambrosetti, D., Mansukhani, A. & Basilico, C. Mechanisms underlying differential responses to FGF signaling. Cytokine & Growth Factor Reviews 16, 233-247, doi:10.1016/j.cytogfr.2005.01.007 (2005).
32 Browaeys-Poly, E., Cailliau, K. & Vilain, J.-P. Transduction cascades initiated by fibroblast growth factor 1 on Xenopus oocytes expressing MDA-MB-231 mRNAs: Role of Grb2, phosphatidylinositol 3-kinase, Src tyrosine kinase, and phospholipase Cγ. Cellular Signalling 13, 363-368, doi:10.1016/s0898-6568(01)00149-8 (2001).
33 Cotton, L. M., O’Bryan, M. K. & Hinton, B. T. Cellular Signaling by Fibroblast Growth Factors (FGFs) and Their Receptors (FGFRs) in Male Reproduction. Endocrine Reviews 29, 193-216, doi:10.1210/er.2007-0028 (2008).
34 Carpenter, G. & Ji, Q.-s. Phospholipase C-γ as a Signal-Transducing Element. Experimental Cell Research 253, 15-24, doi:10.1006/excr.1999.4671 (1999).
35 Drafahl, K. A., McAndrew, C. W. & Donoghue, D. J. in Handbook of Cell Signaling (Second Edition) (eds A. Bradshaw Ralph & A. Dennis Edward) 1939-1947 (Academic Press, 2010).
36 Rusnati, M. & Presta, M. Fibroblast Growth Factors/Fibroblast Growth Factor Receptors as Targets for the Development of Anti-Angiogenesis Strategies. Current Pharmaceutical Design 13, 2025-2044 (2007).
37 Eswarakumar, V. P., Lax, I. & Schlessinger, J. Cellular signaling by fibroblast growth factor receptors. Cytokine & Growth Factor Reviews 16, 139-149, doi:10.1016/j.cytogfr.2005.01.001 (2005).
38 Plotnikov, A. N., Schlessinger, J., Hubbard, S. R. & Mohammadi, M. Structural Basis for FGF Receptor Dimerization and Activation. Cell 98, 641-650 (1999).
39 Ravindranathan, K. P. et al. Discovery of Novel Fibroblast Growth Factor Receptor 1 Kinase Inhibitors by Structure-Based Virtual Screening. Journal of Medicinal Chemistry 53, 1662-1672, doi:10.1021/jm901386e (2010).
40 Makino, S. & Kuntz, I. D. Automated flexible ligand docking method and its application for database search. Journal of Computational Chemistry 18, 1812-1825, doi:10.1002/(sici)1096-987x(19971115)18:14<1812::aid-jcc10>3.0.co;2-h (1997).
41 Morris, G. M. et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry 19, 1639-1662, doi:10.1002/(sici)1096-987x(19981115)19:14<1639::aid-jcc10>3.0.co;2-b (1998).
42 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 47, 1739-1749, doi:10.1021/jm0306430 (2004).
43 Abagyan, R., Totrov, M. & Kuznetsov, D. ICM\—a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 15, 488-506, doi:10.1002/jcc.540150503 (1994).
44 Jones, G., Willett, P. & Glen, R. C. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. Journal of Molecular Biology 245, 43-53, doi:10.1016/s0022-2836(95)80037-9 (1995).
45 Goldberg, D. Genetic Algorithms in Search, Optimization, and Machine Learning. (Addison-Wesley Professional, 1989).
46 Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W. & Taylor, R. D. Improved protein–ligand docking using GOLD. Proteins: Structure, Function, and Bioinformatics 52, 609-623, doi:10.1002/prot.10465 (2003).
47 Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V. & Mee, R. P. 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 11, 425-445, doi:10.1023/a:1007996124545 (1997).
48 Huang, S.-Y. & Zou, X. Advances and Challenges in Protein-Ligand Docking. International Journal of Molecular Sciences 11, 3016-3034 (2010).
49 Parenti, M. D. & Rastelli, G. Advances and applications of binding affinity prediction methods in drug discovery. Biotechnology Advances 30, 244-250, doi:10.1016/j.biotechadv.2011.08.003 (2012).
50 Huang, N., Shoichet, B. K. & Irwin, J. J. Benchmarking Sets for Molecular Docking. Journal of Medicinal Chemistry 49, 6789-6801, doi:10.1021/jm0608356 (2006).
51 Lovell, S. C., Word, J. M., Richardson, J. S. & Richardson, D. C. The penultimate rotamer library. Proteins: Structure, Function, and Bioinformatics 40, 389-408, doi:10.1002/1097-0134(20000815)40:3<389::aid-prot50>3.0.co;2-2 (2000).
52 Martí-Renom, M. A. et al. COMPARATIVE PROTEIN STRUCTURE MODELING OF GENES AND GENOMES. Annual Review of Biophysics and Biomolecular Structure 29, 291-325, doi:doi:10.1146/annurev.biophys.29.1.291 (2000).
53 Kuntal, B., Aparoy, P. & Reddanna, P. EasyModeller: A graphical interface to MODELLER. BMC Research Notes 3, 226 (2010).
54 Mohammadi, M. & McMahon, G. Structures of the tyrosine kinase domain of fibroblast growth factor receptor in complex with inhibi. Science 276, 955 (1997).
55 Eathiraj, S. et al. A Novel Mode of Protein Kinase Inhibition Exploiting Hydrophobic Motifs of Autoinhibited Kinases. Journal of Biological Chemistry 286, 20677-20687, doi:10.1074/jbc.M110.213736 (2011).
56 Perola, E. Minimizing false positives in kinase virtual screens. Proteins: Structure, Function, and Bioinformatics 64, 422-435, doi:10.1002/prot.21002 (2006).
57 Laurie, A. T. R. & Jackson, R. M. Q-SiteFinder: an energy-based method for the prediction of protein–ligand binding sites. Bioinformatics 21, 1908-1916, doi:10.1093/bioinformatics/bti315.
58 Najmanovich, R., Kuttner, J., Sobolev, V. & Edelman, M. Side-chain flexibility in proteins upon ligand binding. Proteins: Structure, Function, and Bioinformatics 39, 261-268, doi:10.1002/(sici)1097-0134(20000515)39:3<261::aid-prot90>3.0.co;2-4 (2000).
59 Mishra, N. et al. Structure based virtual screening of GSK-3β: Importance of protein flexibility and induced fit. Bioorganic & Medicinal Chemistry Letters 19, 5582-5585, doi:10.1016/j.bmcl.2009.08.042 (2009).
60 Cavasotto, C. N. & Abagyan, R. A. Protein Flexibility in Ligand Docking and Virtual Screening to Protein Kinases. Journal of Molecular Biology 337, 209-225, doi:10.1016/j.jmb.2004.01.003 (2004).
61 Totrov, M. & Abagyan, R. Flexible ligand docking to multiple receptor conformations: a practical alternative. Current Opinion in Structural Biology 18, 178-184, doi:10.1016/j.sbi.2008.01.004 (2008).