博碩士論文 93242004 詳細資訊




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姓名 游竣評(Chun-Ping YU)  查詢紙本館藏   畢業系所 物理學系
論文名稱 聚類組態系綜分析蛋白質摺疊模擬的動力學及熱力學
(Kinetics and thermodynamics of protein-folding simulations by clustering conformational ensemble)
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摘要(中) 使用AMBER 2003的力場和內含水的模型研究Trp-cage和protein G在全原子的動力學性質。複本交換的方法提升摺疊組態空間的採樣。摺疊模擬是由24個複本,溫度從276K到508K開始。對Trp-cage開始的結構是個完全延展開的結構而protein G是從自然狀態開始。分子模擬的組態系綜經由聚類方法OPTICS分析。結果顯示,對兩個蛋白質的摺疊組態空間是階層示的。對Trp-cage而言,代表中心聚類結構的平均結構與實驗的結構相較有1.2A的骨架方均根偏差,而protein G則是1.4 A。回歸樹分析對OPTICS產生的聚類序作的映射顯示,摺疊組態空間可以階層示的界定成四個形式:為摺疊型態,二節結構形成,三級結構形成,以及原生型態。三個特徵的因子支配Trp-cage的摺疊空間Pro12-Ψ雙面角, Leu2-Ψ雙面角和總能;protein G有四個因子:Lys4-Ψ,Thr18-Ψ, Glu42-Ψ和Asp22-Ψ.
摘要(英) The kinetics of the folding of the Trp-cage and protein G were studied in all-atomic molecular dynamics simulations using the AMBER 2003 force-field in implicit solvent. Replica exchange method (REM) was used to enhance sampling of folding conformational space. Folding simulations of twenty-four replicas of Trp-cage and protein G were run from extended state and native state, respectively, ranging from 276 K to 508 K. The conformational ensemble of molecular simulations was clustering by OPTICS. The results showed that the folding conformational spaces for both proteins are hierarchical. The average conformation representing centroid clustering structure for Trp-cage has a backbone root mean square deviation of 1.2 A relative to experimental structure, and 1.4 A for protein G. After regression tree analyze for mapping cluster ordering generated by OPTICS, the folding conformational space can demarcate four hierarchical regimes: unfolded state, formation of secondary structure, formation of tertiary structure, and native state. Three characterized factors for Trp-cage, Pro12-Ψ angle, Leu2-Ψ angle, and total energy, dominated folding space; and four factors for protein G: Lys4-Ψ, Thr18-Ψ, Glu42-Ψ, and Asp22-Ψ.
關鍵字(中) ★ 分子模擬
★ 聚類分析
★ 蛋白質摺疊
關鍵字(英) ★ modlecular dynamics
★ cluster analysis
★ protein folding
論文目次 中文摘要 i
ABSTRACT ii
誌謝 iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
CHAPTER
1. PROTEIN STRUCTURE AND FOLDING 1
1.1 Introduction 1
1.2 Hierarchical Structure of Proteins 1
1.2.1 Primary Structure 1
1.2.2 Secondary Structure 2
1.2.3 Tertiary Structure and Quaternary Structure 4
2. METHODS AND MATERIALS 5
2.1 Modeling System 5
2.1.1 Molecular Dynamics Algorithm 5
2.1.2 Force Fields 6
2.1.2.1 Non-bonded Interactions 7
2.1.2.2 Bonded Interactions 8
2.1.3 Replica-exchange method 9
2.2 Machine Learning 12
2.2.1 Cluster Analysis 12
2.2.1.1 K-Means Clustering 12
2.2.1.2 Hierarchical Clustering 13
2.2.1.3 Density-based Clustering 15
2.2.2 Decision Tree 17
2.3 Supervised Learning after Cluster Analysis 20
2.4 Materials 21
2.4.1 Trp-cage 21
2.4.2 Protein G 23
3. RESULTS 25
3.1 Trp-Cage 25
3.1.1 Characteristic Structures 25
3.1.2 Feature Selection 28
3.2 Protein G 29
3.2.1 Characteristic Structures 29
3.2.2 Feature Selection 31
4. DISCUSSION 33
4.1 Trp-cage 33
4.2 Protein G 34
5. CONCULSIONS 36
APPENDICES
A. CLUSTERING CONFORMATIONAL SPACE OF TRP-CAGE FOR VARIOUS TEMPERATURES 37
B. CLUSTERING CONFORMATIONAL SPACE OF PROTEIN G FOR VARIOUS TEMPERATURES 39
BIBLIOGRAPHY 41
參考文獻 1. Berman, H.M., et al., The Protein Data Bank. Nucleic Acids Res, 2000. 28(1): p. 235-42.
2. Kabsch, W. and C. Sander, Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 1983. 22(12): p. 2577-637.
3. Duan, Y. and P.A. Kollman, Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science, 1998. 282(5389): p. 740-4.
4. Snow, C.D., et al., Absolute comparison of simulated and experimental protein-folding dynamics. Nature, 2002. 420(6911): p. 102-6.
5. Ryckaert, J.-P.C., Giovanni; Berendsen, Herman J. C., Numerical Integration of the Carte-sian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes. Journal of Computational Physics, 1977. 23: p. 327.
6. Berk Hess, H.B., Herman J. C. Berendsen, Johannes G. E. M. Fraaije,, LINCS: A linear con-straint solver for molecular simulations. Journal of Computational Chemistry, 1997. 18(12): p. 1463-1472.
7. D.A. Case, T.A.D., T.E. Cheatham, III, C.L. Simmerling, J. Wang, R.E. Duke, R. Luo, K.M. Merz, B. Wang, D.A. Pearlman, M. Crowley, S. Brozell, V. Tsui, H. Gohlke, J. Mongan, V. Hornak, G. Cui, P. Beroza, C. Schafmeister, J.W. Caldwell, W.S. Ross, P.A. Kollman, AMBER 8. 2004.
8. Still, W.C., et al., Semianalytical Treatment of Solvation for Molecular Mechanics and Dynamics. Journal of the American Chemical Society, 1990. 112(16): p. 6127-6129.
9. Tseng, C.Y., C.P. Yu, and H.C. Lee, Integrity of H1 helix in prion protein revealed by molecular dynamic simulations to be especially vulnerable to changes in the relative orientation of H1 and its S1 flank. Eur Biophys J, 2009. 38(5): p. 601-11.
10. Sugita, Y. and Y. Okamoto, Replica-exchange molecular dynamics method for protein folding. Chemical Physics Letters, 1999. 314(1-2): p. 141-151.
11. Mitsutake, A., Y. Sugita, and Y. Okamoto, Generalized-ensemble algorithms for molecular simulations of biopolymers. Biopolymers, 2001. 60(2): p. 96-123.
12. Hukushima, K. and K. Nemoto, Exchange Monte Carlo Method and Application to Spin Glass Simulations. Journal of the Physical Society of Japan, 1996. 65(6): p. 1604.
13. Okamoto, Y., Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations. J Mol Graph Model, 2004. 22(5): p. 425-39.
14. Predescu, C., M. Predescu, and C.V. Ciobanu, On the efficiency of exchange in parallel tempering monte carlo simulations. J Phys Chem B, 2005. 109(9): p. 4189-96.
15. Ester, M., et al., A Density-Based Algorithm for Discovering Clusters in Large Spatial Da-tabases with Noise. Proceedings of 2nd International Conference on Knowledge Discov-ery and Data Mining (KDD-96), 1996: p. 226-231.
16. Ankerst, M., et al., OPTICS: ordering points to identify the clustering structure, in Proceedings of the 1999 ACM SIGMOD international conference on Management of data. 1999, ACM: Philadelphia, Pennsylvania, United States. p. 49-60.
17. Neidigh, J.W., R.M. Fesinmeyer, and N.H. Andersen, Designing a 20-residue protein. Na-ture Structural Biology, 2002. 9(6): p. 425-430.
18. Qiu, L.L., et al., Smaller and faster: The 20-residue Trp-cage protein folds in 4 mu s. Jour-nal of the American Chemical Society, 2002. 124(44): p. 12952-12953.
19. Snow, C.D., B. Zagrovic, and V.S. Pande, The Trp cage: folding kinetics and unfolded state topology via molecular dynamics simulations. J Am Chem Soc, 2002. 124(49): p. 14548-9.
20. Simmerling, C., B. Strockbine, and A.E. Roitberg, All-atom structure prediction and folding simulations of a stable protein. J Am Chem Soc, 2002. 124(38): p. 11258-9.
21. Pitera, J.W. and W. Swope, Understanding folding and design: replica-exchange simula-tions of "Trp-cage" miniproteins. Proc Natl Acad Sci U S A, 2003. 100(13): p. 7587-92.
22. Chowdhury, S., et al., Ab initio folding simulation of the Trp-cage mini-protein approaches NMR resolution. J Mol Biol, 2003. 327(3): p. 711-7.
23. Zhou, R., Trp-cage: folding free energy landscape in explicit water. Proc Natl Acad Sci U S A, 2003. 100(23): p. 13280-5.
24. Juraszek, J. and P.G. Bolhuis, Sampling the multiple folding mechanisms of Trp-cage in explicit solvent. Proc Natl Acad Sci U S A, 2006. 103(43): p. 15859-64.
25. Mok, K.H., et al., A pre-existing hydrophobic collapse in the unfolded state of an ultrafast folding protein. Nature, 2007. 447(7140): p. 106-9.
26. Yong Duan, C.W., Shibasish Chowdhury, Mathew C. Lee, Guoming Xiong, Wei Zhang, Rong Yang, Piotr Cieplak, Ray Luo, Taisung Lee, James Caldwell, Junmei Wang, Peter Kollman,, A point-charge force field for molecular mechanics simulations of proteins based on con-densed-phase quantum mechanical calculations. Journal of Computational Chemistry, 2003. 24(16): p. 1999-2012.
27. Onufriev, A., D. Bashford, and D.A. Case, Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins-Structure Function and Bioinformatics, 2004. 55(2): p. 383-394.
28. Gronenborn, A.M., et al., A novel, highly stable fold of the immunoglobulin binding do-main of streptococcal protein G. Science, 1991. 253(5020): p. 657-61.
29. Shirts, M.R. and V.S. Pande, Mathematical analysis of coupled parallel simulations. Phys Rev Lett, 2001. 86(22): p. 4983-7.
30. Shakhnovich, E.I., Theoretical studies of protein-folding thermodynamics and kinetics. Curr Opin Struct Biol, 1997. 7(1): p. 29-40.
31. Shimada, J., E.L. Kussell, and E.I. Shakhnovich, The folding thermodynamics and kinetics of crambin using an all-atom Monte Carlo simulation. J Mol Biol, 2001. 308(1): p. 79-95.
32. Kussell, E., J. Shimada, and E.I. Shakhnovich, A structure-based method for derivation of all-atom potentials for protein folding. Proc Natl Acad Sci U S A, 2002. 99(8): p. 5343-8.
33. Shimada, J. and E.I. Shakhnovich, The ensemble folding kinetics of protein G from an all-atom Monte Carlo simulation. Proc Natl Acad Sci U S A, 2002. 99(17): p. 11175-80.
指導教授 李弘謙(Hoong-chien Lee) 審核日期 2009-7-26
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