蛋白質的構型變化與其功能息息相關，尤其對於具有催化功能的酵素而言。當配體位於酵素的活性部位時，蛋白質會因配體的誘導而產生區域變化至與配體穩定結合，此行為影響著酵素是否可以進行催化反應。本論文使用線性響應理論(LRT)預測蛋白質構型變化的方向，進而探討蛋白質構型變化是否來自於配體放置於其活性部位所產生的影響。不同於過往的研究是：線性響應理論利用CHARMM22給予的參數結合擬真的力場(全原子力場)，詳細的計算配體對蛋白質內部造成的作用力，並結合彈性網絡模型求出的共變異矩陣，形成一個既快速(相較於使用分子動力學模擬)又細膩的方法來預測蛋白質因配體結合引起的構型變化。對於測試的蛋白質分類為構型變化與配體結合相關與不相關，發現線性響應理論並不適用於構型變化與配體結合無直接關係的蛋白質。在此篇研究中，對於構型變化與配體結合相關的蛋白質，我們在不需要結合狀態結構的資訊下，LRT在使用簡單吸引的擾動力下成功的預測蛋白質構性的變化，但是LRT使用分子動力學力場計算出來的擾動力與簡單吸引的擾動力對預測構型變化方向的結果並沒有差異，並且LRT預測的相關係數平均值比使用ANM的最低頻率mode預測的平均值更高，故推測使用LRT預測構型變化將比使用ANM的最低頻率mode更好。當LRT使用分子動力模擬取力，對配體位置有無做能量最小化對預測結果無顯著差異；若使用假設的簡單吸引力預測構型變化方向時，則做能量最小化有較高的相關係數平均值，推測有無能量最小化對假設簡單吸引力預測構型變化方向具有影響性，因此需要謹慎決定配體在蛋白質中的位置。

Conformational changes occurring in proteins are closely related to their biological functions.　Ligands, when located near the active sites of enzymes, induce conformational changes that stabilize the ligand-enzyme complex and then catalyze appropriate chemical reactions. In this study, we use linear response theory to predict the direction of conformational changes that occur upon ligand binding. In contrast with the previous studies, forces upon ligand binding are obtained from all-atom force field with parameters of CHARMM22. In addition, we use covariance matrix obtained from ANM on unbound structure (ligand-free), thus developing a fast (compared to the use of molecular dynamics simulations) and accurate method to predict conformational changes upon ligand binding. Our dataset consists of proteins that undergo coupled and independent domain motions upon ligand binding. We find that LRT cannot be used to predict conformational changes of the latter. In the case of proteins that undergo coupled domain motion, we are able to predict the conformational changes upon ligand binding without any information from the bound structure. In addition, following results are observed for proteins with coupled domain motion. The conformational changes predicted with linear response theory using either MD derived forces or simple attractive forces are not significantly different from each other. Also, we see that the average correlation coefficient in the case of LRT predicted conformational changes is higher than that from ANM first mode. If MD forces are used in LRT, energy minimization has no significant effect on the correlation coefficient. When simple perturbation forces are used in LRT, the average correlation coefficient is higher when the initial ligand/free-protein complex is energy-minimized. Hence, we speculate that LRT results are sensitive to the position of ligand, especially when simple perturbation forces are used.

[1] R. Koike, T. Amemiya, M. Ota, and A. Kidera, J. Mol. Biol. 379, 397 (2008).
[2] K.-E. Jaeger and T. Eggert, Curr. Opin. Biotechnol. 15, 305 (2004).
[3] J. Monod, J. Wyman, and J. P. Changeux, J. Mol. Biol. 12, 88 (1965).
[4] D. E. Koshland, G. Némethy, and D. Filmer, Biochemistry 5, 365 (1966).
[5] D. E. Koshland, Proc. Natl. Acad. Sci. U. S. A. 44, 98 (1958).
[6] Alan Fersht, Enzyme Structure and Mechanism, 2nd ed. (W H Freeman & Co (Sd), 1985).
[7] W. P. Jencks, Catalysis in Chemistry and Enzymology (1969), p. xvi, 836 p.
[8] M. Ikeguchi, J. Ueno, M. Sato, and A. Kidera, Phys. Rev. Lett. 94, 078102 (2005).
[9] L.-W. Yang, E. Eyal, I. Bahar, and A. Kitao, Bioinformatics 25, 606 (2009).
[10] S. G. Essiz and R. D. Coalson, J. Phys. Chem. B 113, 10859 (2009).
[11] W. Kabsch, Acta Crystallogr. Sect. A 34, 827 (1978).
[12] C. Eckart, Phys. Rev. 47, 552 (1935).
[13] S. B. Needleman and C. D. Wunsch, J. Mol. Biol. 48, 443 (1970).
[14] W. Humphrey, A. Dalke, and K. Schulten, J. Mol. Graph. 14, 33 (1996).
[15] A. R. Leach, Molecular Modelling: Principles and Applications (2001), p. 744.
[16] I. B. Qiang Cui, Normal Mode Analysis: Theory and Applications to Biological and Chemical Systems, 1st ed. (Chapman and Hall/CRC, 2005).
[17] A. R. Atilgan, S. R. Durell, R. L. Jernigan, M. C. Demirel, O. Keskin, and I. Bahar, Biophys. J. 80, 505 (2001).
[18] Frank S. Crawford Jr., Waves (Berkeley Physics Course, Vol. 3) (Tata McGraw-Hill, 2008).
[19] L.-W. Yang, Biophys. J. 100, 1784 (2011).
[20] K. Arnold, L. Bordoli, J. Kopp, and T. Schwede, Bioinformatics 22, 195 (2006).
[21] A. D. MacKerell,, D. Bashford, R. L. Dunbrack,, J. D. Evanseck, M. J. Field, S. Fischer, J. Gao, H. Guo, S. Ha, D. Joseph-McCarthy, L. Kuchnir, K. Kuczera, F. T. K. Lau, C. Mattos, S. Michnick, T. Ngo, D. T. Nguyen, B. Prodhom, W. E. Reiher, B. Roux, M. Schlenkrich, J. C. Smith, R. Stote, J. Straub, M. Watanabe, J. Wiórkiewicz-Kuczera, D. Yin, and M. Karplus, J. Phys. Chem. B 102, 3586 (1998).
[22] J. C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R. D. Skeel, L. Kalé, and K. Schulten, J. Comput. Chem. 26, 1781 (2005).
[23] J. D. Yesselman, D. J. Price, J. L. Knight, and C. L. Brooks, J. Comput. Chem. 33, 189 (2012).
[24] G. M. Downs, V. J. Gillet, J. D. Holliday, M. F. Lynch, I. Studies, and S. Sheffield, 215 (1989).
[25] G. P. Poornam, A. Matsumoto, H. Ishida, and S. Hayward, Proteins 76, 201 (2009).
[26] T. Amemiya, R. Koike, S. Fuchigami, M. Ikeguchi, and A. Kidera, J. Mol. Biol. 408, 568 (2011).
[27] A. Andreeva, D. Howorth, J.-M. Chandonia, S. E. Brenner, T. J. P. Hubbard, C. Chothia, and A. G. Murzin, Nucleic Acids Res. 36, D419 (2008).
[28] P. Kukić and J. E. Nielsen, Future Med. Chem. 2, 647 (2010).
[29] K. Hinsen, Proteins 33, 417 (1998).
[30] T. Amemiya, R. Koike, A. Kidera, and M. Ota, Nucleic Acids Res. 40, D554 (2012).
[31] M. Rostkowski, M. H. M. Olsson, C. R. Søndergaard, and J. H. Jensen, BMC Struct. Biol. 11, 6 (2011).
[32] S. Moréra, L. Larivière, J. Kurzeck, U. Aschke-Sonnenborn, P. S. Freemont, J. Janin, and W. Rüger, J. Mol. Biol. 311, 569 (2001).
[33] B. Andi, H. Xu, P. F. Cook, and A. H. West, Biochemistry 46, 12512 (2007).
[34] S. Fuchigami, S. Omori, M. Ikeguchi, and A. Kidera, J. Chem. Phys. 132, 104109 (2010).
[35] V. Alexandrov, U. Lehnert, N. Echols, D. Milburn, D. Engelman, and M. Gerstein, Protein Sci. 14, 633 (2005).