博碩士論文 972402012 詳細資訊

以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:5 、訪客IP:
姓名 許伯任(Po-Jen Hsu)  查詢紙本館藏   畢業系所 物理學系
論文名稱 由超快速形狀辨識、時間序列分割、時間序列交互相關分析以及擴散理論方法研究蛋白質Transthyretin片斷與金屬叢集的分子動力學模擬
(Molecular dynamics simulations of a fragment of the protein transthyretin and metallic clusters diagnosed by the ultra-fast shape recognition technique, time series segmentation, time series cross correlation analysis and diffusion theory method)
★ 金屬叢集的融化現象★ 帶電膠體系統之液態-液態/固態相變研究
★ 低濃度電解質在奈米管內異常的擴散和導電性★ 一價和多價叢集原子的熱穩定現象
★ 金屬與合金分子叢集的結構★ 物理系統之能量與焓分佈之統計力學研究
★ 膠體系統平衡相域與動態凝聚之研究★ 合金金屬叢集的溫度效應
★ 介面膠體叢聚現象的理論研究★ 帶電膠體懸浮液的相圖與液態-玻璃相變研究
★ 膠體相圖之理論計算★ 膠體、棒狀粒子混合系統之相圖的理論分析
★ 利用時間序列的統計方法研究金屬叢集的動力學★ 由分子動力學模擬探討層狀石墨烯的成長與碳化矽基板上多層石墨烯的熱穩定性
★ 金銅合金金屬叢集(N=38)的磁性性質研究★ 膠體、盤狀粒子混合系統的兩階段動態相變區域
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摘要(中) 本研究主旨為發展新的統計與動態分析方法並應用在分子叢集(cluster)以及聚分子(polymer)的動靜態行為(例如相變、折疊),我們將原本運用在蛋白質資料庫(PDB)比對形狀的方法(shape matching technique)轉移到分子動力學的分子軌跡分析,所使用的方法包含統計機率分佈以及時間序列動態分析,諸如time series segmentation、time series cross correlation等。利用上述方法,我們發現形狀辨識技術(shape recognition technique )非常適合運用在有限尺度系統(finite-size system),因為任何有限系統的動態行為都與形狀的變化息息相關,本研究也是分子動力學文獻中少數以分子形狀變化作為分析的依據,是屬於具原創性的研究工作。

運用形狀辨識技術,我們獲得分子叢集相變(phase transition)的新觀點--利用形狀的機率分佈隨溫度的變化可以清楚解釋融化以及前置融化現象的機制,我們分析的對象為AgCu的合金,最大到45顆原子。而在聚分子方面,我們採用了TTR(105-115)這個素材,其與視網膜病變以及大腦疾病息息相關,因此研究他的折疊與沈澱機制是最近非常熱門的議題,利用形狀辨識所產生的形狀時間序列(定溫與定壓下),並透過時間序列分析方法,我們了解了非常詳細的折疊機制,並呈現了一般蛋白質折疊分析(例如contact map)所無法得到的動態資訊。

最後,本論文的主要題目之一為擴散理論(Diffusion Theory)在TTR(105-115)的應用,我們產生了非常龐大資料的分子動力學軌跡(2μs),模擬系統包含了數以千計的實體水分子,用以驗證Dr. A. Rapallo所發展的 Hybrid Basis Approach (HBA),HBA為結合廣泛應用的 Maximum Correlation Approximation (MCA)還有 Long Time Sorting Procedure (LTSP) 所得到的新理論方法,已於2008年在合成的聚分子上取得極佳的結果,其主要目的是在考慮聚分子或蛋白質長時間記憶效應,也就是計算其時間相關函式(time correlation function)的同時,還能保有區域動態(local dynamics)行為的特徵,而本論文則是將此方法應用到生物體內的蛋白質片斷,其複雜度高於合成聚分子。在TTR(105-115)的擴散理論計算中,我們發現HBA的精確度勝於LTSP,且能得到非常貼近模擬實驗的結果,換而言之,HBA可以作為目前Diffusion Theory領域裡一個非常強大的理論。我們期望這個理論能夠更進一步成功地應用在各式蛋白質與聚分子的動態理論分析上。
摘要(英) Part I--An ultrafast shape-recognition technique was used to analyze the phase transition of finite-size clusters, which, according to our research, has not yet been accomplished. The shape of clusters is the unique property that distinguishes clusters from bulk systems, and is comprehensive and natural for structural analysis. In this study, an isothermal molecular dynamics simulation was performed to generate a structural database for shape recognition of Ag-Cu metallic clusters using empirical many-body potential. The probability contour of the shape similarity exhibits the characteristics of both the specific heat and Lindemann index (bond length fluctuation) of clusters. Moreover, our implementation of the substructure to the probability of shapes provides a detailed observation of the atom/shell-resolved analysis, and the behaviors of the clusters were reconstructed based on the statistical information. The method is efficient, flexible, and applicable in any type of finite-size system, including polymers and nanostructures.

Part II--Folded conformations of proteins in thermodynamically stable states have long lifetimes. Between such stable folded conformations the protein will generally stray from one random conformation to another leading thus to rapid fluctuations. Brief structural changes therefore occur before folding and unfolding events. These short-lived movements are easily overlooked in studies of folding/unfolding for they represent momentary excursions of the protein to explore conformations in the neighborhood of the stable conformation. The present study looks for precursory signatures of protein folding/unfolding within these rapid fluctuations through a combination of three techniques: (1) ultrafast shape recognition, (2) time series segmentation, and (3) time series clustering. The first procedure measures the differences between statistical distributions of atoms in different conformations by calculating shape similarity indices from molecular dynamics simulation trajectories. The second procedure is used to discover the times at which the protein makes transitions from one conformation to another. Finally, the third technique exploits spatial fingerprints of the stable conformations, since strongly correlated atoms in different conformations are different because the bond and steric constraints are different, to map out the sequences of changes preceding the actual folding and unfolding events. The aforementioned high-frequency fluctuations are therefore characterized by distinct correlational changes and structural changes associated with rate-limiting precursors translate into brief segments. Guided by these technical procedures, we identify not only the signatures of transitions between α helix and β hairpin for transthyretin fragment TTR(105-115) (the model system chosen in this work for illustration), but also the important role played by weaker correlations in such protein folding dynamics.

Part III--Improved basis sets for the study of polymer dynamics by means of the diffusion theory, and tests on a melt of cis-1,4-polyisoprene decamers, and a toluene solution of a 71-mer syndiotactic trans-1,2-polypentadiene were presented recently [R. Gaspari and A. Rapallo, J. Chem. Phys. 128, 244109 (2008)]. The proposed hybrid basis approach (HBA) combined two techniques, the long time sorting procedure and the maximum correlation approximation. The HBA takes advantage of the strength of these two techniques, and its basis sets proved to be very effective and computationally convenient in describing both local and global dynamics in cases of flexible synthetic polymers where the repeating unit is a unique type of monomer. The question then arises if the same efficacy continues when the HBA is applied to polymers of different monomers, variable local stiffness along the chain and with longer persistence length, which have different local and global dynamical properties against the above-mentioned systems. Important examples of this kind of molecular chains are the proteins, so that a fragment of the protein transthyretin is chosen as the system of the present study. This peptide corresponds to a sequence that is structured in β-sheets of the protein, and is located on the surface of the channel with thyroxin. The protein transthyretin forms amyloid fibrils in vivo, whereas the peptide fragment has been shown [C.P. Jaroniec, C.E. MacPhee, N.S. Astrof, C.M. Dobson, and R.G. Griffin, PNAS 99, 16748 (2002)] to form amyloid fibrils in vitro in extended β-sheet conformations. For these reasons the latter is given considerable attention in the literature, and studied also as an isolated fragment in water solution where both experimental and theoretical efforts have indicated the propensity of the system to form β turns or α-helices, but is otherwise predominantly unstructured. Differing from previous computational studies that employed implicit solvent, we performed in this work the classical molecular dynamics simulation on a realistic model solution with the peptide embedded in an explicit water environment, and calculated its dynamic properties both as an outcome of the simulations, and by the diffusion theory in reduced statistical-mechanical approach within HBA on the premise that the mode-coupling approach to the diffusion theory can give both the long-range and local dynamics starting from equilibrium averages which were obtained from detailed atomistic simulations.
關鍵字(中) ★ 分子動力學
★ 形狀辨識
★ 聚分子動態
★ 分子叢集
★ 擴散理論
關鍵字(英) ★ Molecular Dynamics
★ Shape recognition
★ Polymer Dynamics
★ Cluster
★ Diffusion Theory
論文目次 I A New Perspective of Shape Recognition to Discover the
Phase Transition of Finite-size Clusters 1
1 Intorduction 1
2 Methods 2
3 Results 4
4 Conclusion 8
5 Appendix 9
5.1 Model system 9
5.2 Simulation algorithm and thermal properties 9
II Biological polymer dynamics by molecular dynamics simulation 24
6 Introduction 24
7 Theoretical approach 25
7.1 Peptide fragments of transthyretin 26
7.2 Molecular dynamics simulation using GROMACS software 26
7.3 Polymer with finite-sized solvent 26
7.4 Force field 27
7.5 Energy minimization 28
7.6 Position restraint molecular dynamics 28
III Diusion coecient of water 40
8 The division of the cell 40
9 Mean Square Displacement 41
10 Analysis of the individual water molecule 41
IV Weak correlation effect on the folding of transthyretin fragment studied by the shape similarity technique and time series methods 53
11 Introduction 53
12 Methods 54
12.1 Shape recognition of the partial structures 54
12.2 Correlation filtering 56
12.3 Weak and strong correlation 57
13 Results and discussion 58
V Background of the diusion theory 69
14 Diusion theory 69
15 Diusion tensor 75
15.1 The Oseen tensor 76
15.2 The Rotne-Prager tensor 76
16 Hydrodynamic frictions 77
17 Time correlation function 78
VI GPGPU implemented time correlation function 82
18 Introduction 82
19 Methodology 82
20 Results and Conclusion 85
21 Appendix 85
VII Peptide dynamics by molecular dynamics and diffusion
theory methods with improved basis sets 88
22 Introduction 89
23 methods 92
23.1 Molecular dynamics simulation of TTR(105-115) 92
23.2 Formulation of diusion theory 95
23.3 Mode-coupling approximations: MCA, LTSP, and HBA methods 97
23.4 Diffusion tensor in the Rotne-Prager approximation 99
24 Results and discussion 100
25 Conclusions 104
VIII Precursory Signatures of Protein Folding/Unfolding: From
Time Series Correlation Analysis to Atomistic Mechanisms 117
26 Introduction 117
27 Methods 120
27.1 Molecular dynamics simulations 120
27.2 Ultrafast shape recognition with partial structures 121
27.3 Time series segmentation 124
27.4 Time series correlation analysis 126
28 Results of time series analysis 128
28.1 Similarity time series 128
28.2 Segments of head-tail similarity time series 128
28.3 Color map of cross correlations 129
28.4 Correlation filtering 129
28.5 Fingerprints and precursors 130
28.5.1 Alpha-helix conformation 130
28.5.2 Beta-hairpin conformation 132
28.5.3 Mixture of Alpha and Beta conformations 135
28.5.4 Summary of ngerprints and precursors 137
29 Atomic-resolution and classication of precursors 138
29.1 Stronger-than-average precursors 138
29.2 Weaker-than-average precursors 139
29.3 Comparison of cross correlation analysis and contact analysis 139
30 Conclusions 140
31 Appendix A 143
32 Appendix B 143
33 Appendix C 143
IX References 162
參考文獻 [1] Anne K Starace, Baopeng Cao, Oscar H Judd, Indrani Bhattacharyya, and Martin F Jarrold. Melting of size-selected aluminum nanoclusters with 84-128 atoms.
The Journal of chemical physics, 132(3):034302, 2010.
[2] Martin Schmidt and Hellmut Haberland. Phase transitions in clusters. C R Physique, 3(3):327340, 2002.
[3] Francesca Baletto and Riccardo Ferrando. Structural properties of nanoclusters: Energetic, thermodynamic, and kinetic effects. Reviews of Modern Physics, 77(1):371423, 2005.
[4] Y J Lee, J Y Maeng, E K Lee, B Kim, S Kim, and K K Han. Melting behaviors of icosahedral metal clusters studied by Monte Carlo simulations. Journal of Computational Chemistry, 21(5):380387, 2000.
[5] Ali Sebetci and Ziya B Guvenc. Molecular Dynamics Simulation of the Melting Behaviours of 12-, 13-, 14-Atom Icosahedral Platinum Clusters. Modelling and Simulation in Materials Science and Engineering, 12:1131, 2004.
[6] Murat Atis, Huseyin Akta3, and Ziya B Guvenc. Structures and melting of Ag N (N=7,12-14) clusters. Modelling and Simulation in Materials Science and Engineering, 13(8):14111432, December 2005.
[7] E K Yildirim, M Atis, and Z B Guvenc. Molecular dynamics simulation of melting behaviour of small gold clusters: Au N (N=12-14). Physica Scripta, 75(1):111
118, January 2007.
[8] Hong-Hai Liu, En-Yong Jiang, Hai-Li Bai, Ping Wu, Zhi-Qing Li, and Chang Q
Sun. Dislocation stimulus dependence of atomic collective vibration in an icosahe-
dral cluster. Journal of Nanoscience and Nanotechnology, 9(8):46684672, 2009.
[9] Florent Calvo. Solid-solution precursor to melting in onion-ring Pd-Pt nanoclusters: a case of second-order-like phase change? Faraday Discussions, 138:7588; discussion 119135, 433434, 2008.
[10] Charles Cleveland, W Luedtke, and Uzi Landman. Melting of Gold Clusters: Icosahedral Precursors. Physical Review Letters, 81(10):20362039, 1998.
[11] D Schebarchov and S C Hendy. Transition from Icosahedral to Decahedral Structure in a Coexisting Solid-Liquid Nickel Cluster. Physical Review Letters, 95(11):7, 2005.
[12] Z Kuntova, G Rossi, and R Ferrando. Melting of core-shell Ag-Ni and Ag-Co nanoclusters studied via molecular dynamics simulations. Physical Review B, 77(20):18, 2008.
[13] Yanting Wang, S Teitel, and Christoph Dellago. Melting and Equilibrium Shape of Icosahedral Gold Nanoparticles. Chemical Physics Letters, 394(4-6):4, 2003.
[14] Jerry O Ebalunode and Weifan Zheng. Molecular shape technologies in drug discovery: methods and applications. Current Topics in Medicinal Chemistry, 10(6):669679, 2010.
[15] Pedro J Ballester, Isaac Westwood, Nicola Laurieri, Edith Sim, and W Graham Richards. Prospective virtual screening with Ultrafast Shape Recognition: the identication of novel inhibitors of arylamine N-acetyltransferases. Journal of the
Royal Society, Interface / the Royal Society, 7(43):33542, February 2010.
[16] Edward O Cannon, Florian Nigsch, and John B O Mitchell. A novel hybrid ultrafast shape descriptor method for use in virtual screening. Chemistry Central journal, 2:3, January 2008.
[17] S K Lai, W D Lin, K L Wu, W H Li, and K C Lee. Specic heat and Lindemann-like parameter of metallic clusters: mono- and polyvalent metals. The Journal of chemical physics, 121(3):148798, July 2004.
[18] P J Hsu and S K Lai. Structures of bimetallic clusters. The Journal of chemical physics, 124(4):044711, 2006.
[19] F. A. Lindemann. The calculation of molecular Eigen-frequencies. Phys. Z., 11:609, 1910.
[20] Thomas L. Beck, Julius Jellinek, and R. Stephen Berry. Rare gas clusters: Solids, liquids, slush, and magic numbers. The Journal of Chemical Physics, 87(1):545, 1987.
[21] Young Joo Lee, Eok-Kyun Lee, Sehun Kim, and R. Nieminen. Eect of Potential Energy Distribution on the Melting of Clusters. Physical Review Letters, 86(6):9991002, February 2001.
[22] Gary A Breaux, Colleen M Neal, Baopeng Cao, and Martin F Jarrold. Melting, premelting, and structural transitions in size-selected aluminum clusters with around 55 atoms. Physical Review Letters, 94(17):173401, 2005.
163[23] Eva G Noya, Jonathan P K Doye, and Florent Calvo. Melting of aluminium clusters. 2006.
[24] Ping-Han Tang, Ten-Ming Wu, Tsung-Wen Yen, S K Lai, and P J Hsu. Comparative study of cluster Ag(17)Cu(2) by instantaneous normal mode analysis and by isothermal Brownian-type molecular dynamics simulation. The Journal of chemical physics, 135(9):094302, 2011.
[25] S.K. Lai, Yu-Ting Lin, P.J. Hsu, and S.a. Cheong. Dynamical study of metallic clusters using the statistical method of time series clustering. Computer Physics Communications, 182(4):10131026, April 2011.
[26] Douglas Poland. Intermediates in the melting transitions of aluminum nanoclusters. The Journal of chemical physics, 126(5):054507, 2007.
[27] Daojian Cheng, Xin Liu, Dapeng Cao, WenchuanWang, and Shiping Huang. Surface segregation of Ag-Cu-Au trimetallic clusters. Nanotechnology, 18(47):475702,
[28] Giovanni Barcaro, Alessandro Fortunelli, Giulia Rossi, Florin Nita, and Riccardo Ferrando. Electronic and structural shell closure in AgCu and AuCu nanoclusters. The journal of physical chemistry. B, 110(46):2319723203, 2006.
[29] Yi Rao, Yimin Lei, Xiangyuan Cui, Zongwen Liu, and Fuyi Chen. Optical and magnetic properties of Cu-doped 13-atom Ag nanoclusters. Journal of Alloys and Compounds, 565(0):5055, 2013.
[30] G Rossi, A Rapallo, C Mottet, A Fortunelli, F Baletto, and R Ferrando. Magic polyicosahedral core-shell clusters. Physical Review Letters, 93(10):105503, 2004.
[31] F Baletto, C Mottet, and R Ferrando. Time evolution of Ag-Cu and Ag-Pd core-shell nanoclusters. The European Physical Journal D Atomic Molecular and Optical Physics, 24(1-3):233236, 2003.
[32] Giovanni Barcaro and Alessandro Fortunelli. A study of bimetallic Cu-Ag, Au-Ag and Pd-Ag clusters adsorbed on a double-vacancy-defected MgO(100) terrace. Faraday discussions, 138:3747; discussion 119135, 433434, 2008.
[33] C Mottet, G Rossi, F Baletto, and R Ferrando. Single impurity effect on the melting of nanoclusters. Physical review letters, 95(3):035501, 2005.
[34] Perla B. Balbuena, Julibeth M. Martinez De La Hoz, and Rafael Callejas Tovar. Size eect on the stability of Cu-Ag nanoalloys. Molecular Simulation, 35(10-11):785794, 2009.
[35] Suk Jun Kim, Eric a. Stach, and Carol a. Handwerker. Fabrication of conductive interconnects by Ag migration in Cu-Ag core-shell nanoparticles. Applied Physics
Letters, 96(14):144101, 2010.
[36] P J Hsu, J S Luo, S K Lai, J F Wax, and J-L Bretonnet. Melting scenario in metallic clusters. The Journal of chemical physics, 129(19):194302, November
[37] Ph. Dugourd, R. R. Hudgins, D. E. Clemmer, and M. F. Jarrold. High-resolution ion mobility measurements. Review of Scientic Instruments, 68(2):1122, 1997.
[38] Alexandre A. Shvartsburg, Robert R. Hudgins, Philippe Dugourd, and Martin F. Jarrold. Structural Elucidation of Fullerene Dimers by High-Resolution Ion Mobility Measurements and Trajectory Calculation Simulations. The Journal of Physical Chemistry A, 101(9):16841688, February 1997.
[39] David E Clemmer and Martin F Jarrold. SPECIAL FEATURE : Ion Mobility Measurements and their Applications to Clusters and Biomolecules. J. Mass Spectrosc., 32(April):577592, 1997.
[40] A A Shvartsburg and M F Jarrold. Solid clusters above the bulk melting point. Physical Review Letters, 85(12):25302, 2000.
[41] M F Mesleh, J M Hunter, A A Shvartsburg, G C Schatz, and M F Jarrold. Structural Information from Ion Mobility Measurements: Eects of the Long-Range Potential. The Journal of Physical Chemistry, 100(40):1608216086, 1996.
[42] Robert R Hudgins, Mark A Ratner, and Martin F Jarrold. Design of helices that are stable in vacuo. J. Am. Chem. Soc., 120(49):1297412975, 1998.
[43] Thomas Wyttenbach, John E Bushnell, and Michael T Bowers. Salt Bridge Structures in the Absence of Solvent? The Case for the Oligoglycines. J. Am. Chem. Soc., 120(20):50985103, 1998.
[44] F A Fernandez-Lima, H Wei, Y Q Gao, and D H Russell. On the structure elucidation using ion mobility spectrometry and molecular dynamics. The journal of physical chemistry. A, 113(29):82218234, 2009.
[45] Fabien Chirot, Florent Calvo, Florian Albrieux, Jerome Lemoine, Yury O. Tsybin, and Philippe Dugourd. Statistical Analysis of Ion Mobility Spectrometry. I. Unbiased and Guided Replica-Exchange Molecular Dynamics. Journal of The American Society for Mass Spectrometry, 23(2):386396, 2012.
[46] Florent Calvo, Fabien Chirot, Florian Albrieux, Jerome Lemoine, Yury O. Tsybin, Pascal Pernot, and Philippe Dugourd. Statistical Analysis of Ion Mobility
Spectrometry. II. Adaptively Biased Methods and Shape Correlations. Journal of The American Society for Mass Spectrometry, 23(7):12791288, 2012.
[47] F. Calvo. Phenomenological model for the statistical study of charged metal clusters. Physical Review B, 60(23):1560115604, December 1999.
165[48] F Calvo and F Spiegelmann. Mechanisms of phase transitions in sodium clusters: From molecular to bulk behavior. The Journal of Chemical Physics, 112(6):2888-2908, 2000.
[49] J. P. Neirotti, F. Calvo, David L. Freeman, and J. D. Doll. Phase changes in 38-atom Lennard-Jones clusters. I. A parallel tempering study in the canonical ensemble. The Journal of Chemical Physics, 112(23):10340, 2000.
[50] F. Calvo, J. P. Neirotti, David L. Freeman, and J. D. Doll. Phase changes in 38-atom Lennard-Jones clusters. II. A parallel tempering study of equilibrium and dynamic properties in the molecular dynamics and microcanonical ensembles. The Journal of Chemical Physics, 112(23):10350, 2000.
[51] E. Marinari and G. Parisi. Simulated Tempering - A New Monte-carlo Scheme.
Europhysics Letters, 19(6):451458, 1992.
[52] Robert H. Swendsen and Jian-Sheng Wang. Replica Monte Carlo Simulation of Spin-Glasses. Physical Review Letters, 57(21):26072609, 1986.
[53] Yuji Sugita and Yuko Okamoto. Replica-exchange molecular dynamics method for protein folding. Chemical Physics Letters, 314(November):141151, 1999.
[54] Manuel Athenes and Florent Calvo. Multiple-replica exchange with information retrieval. Chemphyschem : a European journal of chemical physics and physical chemistry, 9(16):23322339, 2008.
[55] Raju Gupta. Lattice relaxation at a metal surface. Physical Review B, 23(12):62656270, 1981.
[56] C. Mottet, G. Treglia, and B. Legrand. Structures of a Ag monolayer deposited on Cu (111), Cu (100), and Cu (110) substrates: An extended tight-binding quenched-molecular-dynamics study. Physical Review B, 46(24):16018, 1992.
[57] A Bulgac and D Kusnezov. Canonical ensemble averages from pseudomicrocanonical dynamics. Physical Review A, 42(8):50455048, 1990.
[58] Shuichi Nose. A molecular dynamics method for simulations in the canonical ensemble. Molecular Physics, 52(2):255268, 1984.
[59] William G Hoover. Canonical dynamics: Equilibrium phase-space distributions. Physical Review A, 31(3):16951697, 1985.
[60] Aurel Bulgac and Dimitri Kusnezov. Thermal properties of Na8 microclusters. Physical review letters, 68(9):13351338, 1992.
[61] Nengjiu Ju and Bulgac Aurel. Finite-temperature properties of sodium clusters. Physical Review B, 48(4):2721, 1993.
166[62] Nikta Fakhri, Frederick C MacKintosh, Brahim Lounis, Laurent Cognet, and Matteo Pasquali. Brownian motion of sti laments in a crowded environment. Science (New York, N.Y.), 330(6012):18047, December 2010.
[63] Aneta T Petkova, Yoshitaka Ishii, John J Balbach, Oleg N Antzutkin, Richard D Leapman, Frank Delaglio, and Robert Tycko. A structural model for Alzheimer′s beta-amyloid brils based on experimental constraints from solid state NMR.
Proc Natl Acad Sci USA, 99(26):1674216747, 2002.
[64] Aneta T Petkova, Richard D Leapman, Zhihong Guo, Wai-Ming Yau, Mark P Mattson, and Robert Tycko. Self-propagating, molecular-level polymorphism in Alzheimer′s beta-amyloid brils. Science (New York, N.Y.), 307(5707):2625,
January 2005.
[65] Christopher P Jaroniec, Cait E MacPhee, Nathan S Astrof, Christopher M Dobson, and Robert G Grin. Molecular conformation of a peptide fragment of transthyretin in an amyloid bril. Proceedings of the National Academy of Sciences of the United States of America, 99(26):1674816753, 2002.
[66] Christopher P Jaroniec, Cait E Macphee, Vikram S Bajaj, Michael T Mcmahon, Christopher M Dobson, and Robert G Grin. High-resolution molecular structure of a peptide in an amyloid bril determined by magic angle spinning NMR spectroscopy. Proceedings of the National Academy of Sciences of the United
States of America, 101(3):7116, 2004.
[67] Emanuele Paci, Jorg Gsponer, Xavier Salvatella, and Michele Vendruscolo. Molecular dynamics studies of the process of amyloid aggregation of peptide fragments of transthyretin. Journal of Molecular Biology, 340(3):555569, 2004.
[68] J A Jarvis, A Kirkpatrick, and D J Craik. 1H NMR analysis of bril-forming peptide fragments of transthyretin. International journal of peptide and protein research, 44(4):38898, October 1994.
[69] Christopher M Dobson. Principles of protein folding, misfolding and aggregation. Seminars in cell developmental biology, 15(1):316, 2004.
[70] Da-Wei Li, Li Han, and Shuanghong Huo. Structural and pathway complexity of beta-strand reorganization within aggregates of human transthyretin(105-115) peptide. The journal of physical chemistry. B, 111(19):54255433, 2007.
[71] G La Penna, Paola Carbone, Rita Carpentiero, Arnaldo Rapallo, and Angelo Perico. Polyisoprene local dynamics in solution: Comparison between molecular dynamics simulations and high order diusion theory. The Journal of Chemical Physics, 114(4):1876, 2001.
167[72] Arnaldo Rapallo, Giulia Rossi, Riccardo Ferrando, Alessandro Fortunelli, Benjamin C. Curley, Lesley D. Lloyd, Gary M. Tarbuck, and Roy L. Johnston. Global optimization of bimetallic cluster structures. I. Size-mismatched Ag-Cu, Ag-Ni, and Au-Cu systems. The Journal of Chemical Physics, 122(19):194308, 2005.
[73] Pedro J Ballester and W Graham Richards. Ultrafast shape recognition to search compound databases for similar molecular shapes. Journal of Computational Chemistry, 28(10):17111723, 2007.
[74] Roberto Gaspari and Arnaldo Rapallo. Formulation of improved basis sets for the study of polymer dynamics through diusion theory methods. The Journal of chemical physics, 128(24):244109, 2008.
[75] W L Jorgensen and J Tirado-Rives. The OPLS Potential Functions for Proteins. Energy Minimizations for Crystals of Cyclic Peptides and Crambin. Journal of the American Chemical Society, 110(6):16571666, 1988.
[76] George A Kaminski, Richard A Friesner, Julian Tirado-Rives, and William L Jorgensen. Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides. The Journal of Physical Chemistry B, 105(28):64746487, 2001.
[77] Michael W Mahoney and William L Jorgensen. Diusion constant of the TIP5P model of liquid water. The Journal of Chemical Physics, 114(1):363, 2001.
[78] M Parrinello and A Rahman. Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics, 52(12):71827190, 1981.
[79] David Van Der Spoel and Erik Lindahl. Brute-Force Molecular Dynamics Simulations of Villin Headpiece: Comparison with NMR Parameters. The Journal of Physical Chemistry B, 107(40):1117811187, 2003.
[80] Berk Hess and Nico F A Van Der Vegt. Hydration thermodynamic properties of amino acid analogues: a systematic comparison of biomolecular force elds and water models. The Journal of Physical Chemistry B, 110(35):1761617626, 2006.
[81] A Glattli, X Daura, andWilfred F Van Gunsteren. A novel approach for designing simple point charge models for liquid water with three interaction sites. Journal of Computational Chemistry, 24(9):10871096, 2003.
[82] Shuichi Nose and M L Klein. Constant pressure molecular dynamics for molecular systems. Molecular Physics, 50(5):10551076, 1983.
[83] Michael R Sawaya, Shilpa Sambashivan, Rebecca Nelson, Magdalena I Ivanova, Stuart A Sievers, Marcin I Apostol, Michael J Thompson, Melinda Balbirnie, Jed J W Wiltzius, Heather T McFarlane, Anders OMadsen, Christian Riekel, and 168David Eisenberg. Atomic structures of amyloid cross-beta spines reveal varied steric zippers. Nature, 447(7143):453457, 2007.
[84] C Blake and L Serpell. Synchrotron X-ray studies suggest that the core of the transthyretin amyloid bril is a continuous beta-sheet helix. Structure London England 1993, 4(8):989998, 1996.
[85] Filip Meersman, Christopher Dobson, and Karel Heremans. Protein unfolding, amyloid bril formation and congurational energy landscapes under high pressure conditions. Chemical Society reviews, 35(10):908917, 2006.
[86] M Sunde and C C Blake. From the globular to the brous state: protein structure and structural conversion in amyloid formation. Quarterly reviews of biophysics,
31(1):139, 1998.
[87] J W Kelly. The alternative conformations of amyloidogenic proteins and their multi-step assembly pathways. Current Opinion in Structural Biology, 8(1):101 6, 1998.
[88] C M Dobson. The structural basis of protein folding and its links with human disease. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 356(1406):133145, 2001.
[89] Fabrizio Chiti and Christopher M Dobson. Protein Misfolding, Functional Amyloid, and Human Disease. Annual Review of Biochemistry, 75(1):333366, 2006.
[90] Sally L Gras. Amyloid Fibrils: From Disease to Design. New Biomaterial Applications for Self-Assembling Cross- Fibrils. Australian Journal of Chemistry, 60(5):333, 2007.
[91] T P Knowles, A W Fitzpatrick, S Meehan, H R Mott, M Vendruscolo, C M Dobson, and M E Welland. Role of intermolecular forces in dening material properties of protein nanobrils. Science, 318(5858):19001903, 2007.
[92] Senli Guo and Boris B Akhremitchev. Packing density and structural heterogeneity of insulin amyloid brils measured by AFM nanoindentation. Biomacromolecules, 7(5):16301636, 2006.
[93] Thomas Scheibel. Protein bers as performance proteins: new technologies and applications. Current opinion in biotechnology, 16(4):427433, 2005.
[94] Filip Meersman, Raul Quesada Cabrera, Paul F McMillan, and Vladimir Dmitriev. Structural and mechanical properties of TTR105-115 Amyloid brils from compression experiments. Biophysical Journal, 100(1):193197, 2011.
[95] Patrick Mesquida, E Macarena Blanco, and Rachel A McKendry. Patterning amyloid peptide brils by AFM charge writing. Langmuir The Acs Journal Of Surfaces And Colloids, 22(22):90899091, 2006.
169[96] Pedro Bernaola-Galvan, Plamen Ivanov, Luis Nunes Amaral, and H Stanley. Scale Invariance in the Nonstationarity of Human Heart Rate. Physical Review Letters, 87(16):168105, 2001.
[97] J C Wong, H Lian, and S A Cheong. Detecting macroeconomic phases in the Dow Jones Industrial Average time series. Physica aStatistical Mechanics and Its Applications, 388(21):46354645, 2009.
[98] Yiting Zhang, Gladys Hui Ting Lee, Jian Cheng Wong, Jun Liang Kok, Manamohan Prusty, and Siew Ann Cheong. Will the US Economy Recover in 2010? A Minimal Spanning Tree Study. Physica aStatistical Mechanics and Its Applications, 390(11):20202050, 2011.
[99] Siew Ann Cheong, Robert Paulo Fornia, Gladys Lee, Jun Liang Kok, Woei Shyr Yim, Danny Yuan Xu, and Yiting Zhang. The Japanese Economy in Crises: A Time Series Segmentation Study. SSRN Electronic Journal, 2011.
[100] J Lin. Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1):145151, 1991.
[101] Siew-Ann Cheong, Paul Stodghill, David J Schneider, Samuel W Cartinhour, and Christopher R Myers. The Context Sensitivity Problem in Biological Sequence Segmentation. Transactions on Computational Biology and Bioinformatics, page 39, 2009.
[102] Jorg Gsponer, Urs Haberthur, and Amedeo Caisch. The role of side-chain interactions in the early steps of aggregation: Molecular dynamics simulations of an amyloid-forming peptide from the yeast prion Sup35. Proceedings of the National Academy of Sciences of the United States of America, 100(9):51549, 2003.
[103] X Chang and K F Freed. Test of Theory for Long Time Dynamics of Floppy Molecules in Solution Using Brownian Dynamics Simulation of Octane. J. Chem. Phys., 99(10):80168030, 1993.
[104] Angelo Perico, Roberto Pratolongo, Karl F. Freed, Richard W. Pastor, and Attila Szabo. Positional time correlation function for one-dimensional systems with barrier crossing: Memory function corrections to the optimized Rouse-Zimm approximation. The Journal of Chemical Physics, 98(1):564, 1993.
[105] Angelo Perico and Roberto Pratolongo. Maximum-Correlation Mode-Coupling Approach to the Smoluchowski Dynamics of Polymers. Macromolecules, 30(19):59585969, 1997.
[106] W H Tang, X Chang, and K F Freed. Theory for Long Time Polymer and Protein dynamics: Basis Functions and Time Correlation Functions. J. Chem. Phys., 103(21):94929501, 1995.
170[107] K S Kostov and K F Freed. Long-time dynamics of Met-enkephalin: comparison of theory with Brownian dynamics simulations. Biophysical journal, 76(1 Pt 1):149163, 1999.
[108] Andrea Giachetti, Giovanni La La Penna, Angelo Perico, and Lucia Banci. Modeling the backbone dynamics of reduced and oxidized solvated rat microsomal cytochrome b5. Biophysical journal, 87(1):498512, 2004.
[109] Min-yi Shen My and Karl F Freed. Long time dynamics of Met-enkephalin: comparison of explicit and implicit solvent models. Biophysical journal, 82(4):1791
1808, 2002.
[110] Min-yi Shen and Karl F. Freed. Long time dynamics of Met-enkephalin: Tests of mode-coupling theory and implicit solvent models. The Journal of Chemical Physics, 118(11):5143, 2003.
[111] Simone Fausti, Giovanni La Penna, Carla Cuniberti, and Angelo Perico. Mode-Coupling Smoluchowski Dynamics of a Double-Stranded DNA Oligomer. Biopolymers, 50(6):613629, 1999.
[112] G La Penna, S Fausti, A Perico, and J A Ferretti. Smoluchowski dynamics of the vnd/NK-2 homeodomain from Drosophila melanogaster: second-order maximum correlation approximation. Biopolymers, 54(2):89103, August 2000.
[113] S Fausti, G La Penna, J Paoletti, D Genest, G Lancelot, and A Perico. Modeling the dynamics of a mutated stem-loop in the SL1 domain of HIV-1Lai genomic RNA by 1H-NOESY spectra. Journal of biomolecular NMR, 20(4):333349, 2001.
[114] P Westermark, K Sletten, B Johansson, and G G Cornwell. Fibril in senile systemic amyloidosis is derived from normal transthyretin. Proceedings of the National Academy of Sciences of the United States of America, 87(7):28432845, 1990.
[115] HJC Berendsen, D. van der Spoel, and R. van Drunen. GROMACS: A message-passing parallel molecular dynamics implementation. Computer Physics Communications, 91(1-3):4356, 1995.
[116] M. Parrinello and A. Rahman. Crystal Structure and Pair Potentials: A Molecular-Dynamics Study. Physical Review Letters, 45(14):11961199, 1980.
[117] RWPastor and M Karplus. Parametrizatlon of the Friction Constant for Stochastic Simulations of Polymers. J. Phys. Chem., 92:26362641, 1988.
[118] Jens Rotne and Stephen Prager. Variational treatment of hydrodynamic interaction in polymers. The Journal of Chemical Physics, 50(11):48314837, 1969.
171[119] Hiromi Yamakawa. Transport Properties of Polymer Chains in Dilute Solution: Hydrodynamic Interaction. J. Chem. Phys., 53(1):436, 1970.
[120] Robert Zwanzig. Theoretical basis for the Rouse-Zimm model in polymer solution dynamics. The Journal of Chemical Physics, 60(7):2717, 1974.
[121] M Karplus and G A Petsko. Molecular dynamics simulations in biology. Nature, 347(6294):631639, 1990.
[122] Martin Karplus and J Andrew McCammon. Molecular dynamics simulations of biomolecules. Nature Structural Biology, 35(6):646652, 2002.
[123] M Karplus and J Kuriyan. Molecular dynamics and protein function. Proceedings of the National Academy of Sciences of the United States of America, 102(19):66796685, 2005.
[124] U H Hansmann and Y Okamoto. New Monte Carlo algorithms for protein folding. Current Opinion in Structural Biology, 9(2):177183, 1999.
[125] R D Taylor, P J Jewsbury, and J W Essex. A review of protein-small molecule docking methods. Journal of computeraided molecular design, 16(3):151166, 2002.
[126] B Rost. Review: protein secondary structure prediction continues to rise. Journal of Structural Biology, 134(2-3):204218, 2001.
[127] Jack Schonbrun, William J Wedemeyer, and David Baker. Protein structure prediction in 2002. Current Opinion in Structural Biology, 12(3):348354, 2002.
[128] C Floudas, H Fung, S Mcallister, M Monnigmann, and R Rajgaria. Advances in protein structure prediction and de novo protein design: A review. Chemical Engineering Science, 61(3):966988, 2006.
[129] Yang Zhang. Progress and challenges in protein structure prediction. Current Opinion in Structural Biology, 18(3):342348, 2008.
[130] J E Shea and C L Brooks. From folding theories to folding proteins: a review and assessment of simulation studies of protein folding and unfolding. Annual Review of Physical Chemistry, 52(1):499535, 2001.
[131] Christopher D Snow, Eric J Sorin, Young Min Rhee, and Vijay S Pande. How well can simulation predict protein folding kinetics and thermodynamics? Annual Review of Biophysics and Biomolecular Structure, 34(1):4369, 2005.
[132] Harold A Scheraga, Mey Khalili, and Adam Liwo. Protein-folding dynamics: overview of molecular simulation techniques. Annual Review of Physical Chemistry, 58(1):5783, 2007.
172[133] S B Prusiner. Prion diseases and the BSE crisis. Science, 278(5336):245251, 1997.
[134] R N Rosenberg. The molecular and genetic basis of AD: the end of the beginning: the 2000 Wartenberg lecture. Neurology, 54(11):20452054, 2000.
[135] Dennis J Selkoe and Marcia B Podlisny. Deciphering the genetic basis of Alzheimer′s disease. Annual review of genomics and human genetics, 3:6799, January 2002.
[136] T Foltynie, S Sawcer, C Brayne, and RA Barker. The genetic basis of Parkinson′s disease. Journal of Neurology Neurosurgery Psychiatry, 73(4):363370, 2002.
[137] Matthew James Farrer. Genetics of Parkinson disease: paradigm shifts and future prospects. Nature Reviews Genetics, 7(4):306318, 2006.
[138] J Liu, L A Campos, M Cerminara, X Wang, R Ramanathan, D S English, and V Munoz. Exploring one-state downhill protein folding in single molecules. Proceedings of the National Academy of Sciences, 109(13):38, 2011.
[139] Hoi Sung Chung, Irina V Gopich, Kevin McHale, Troy Cellmer, John M Louis, and William A Eaton. Extracting rate coecients from single-molecule photon trajectories and FRET eciency histograms for a fast-folding protein. The Journal of Physical Chemistry A, 115(16):36423656, 2011.
[140] Shu-qun Liu, Xing-lai Ji, Yan Tao, and De-yong Tan. Protein Folding , Binding and Energy Landscape : A Synthesis. In P Kaumaya, editor, InTech, pages 207252, Croatia, 2012.
[141] J D Bryngelson, J N Onuchic, N D Socci, and P G Wolynes. Funnels, Pathways and the Energy Landscape of Protein Folding: A Synthesis. Proteins, 21(3):53, 1994.
[142] Valerie Daggett and Alan R Fersht. Is there a unifying mechanism for protein folding? Trends in Biochemical Sciences, 28(1):1825, 2003.
[143] Elizabeth R Morris and Mark S Searle. Overview of protein folding mechanisms: experimental and theoretical approaches to probing energy landscapes. Current protocols in protein science editorial board John E Coligan et al , Chapter 28(April):Unit28.2, 2012.
[144] A R Fersht. Nucleation mechanisms in protein folding. Current Opinion in Structural Biology, 7(1):39, 1997.
[145] Alan R Fersht. Transition-state structure as a unifying basis in protein-folding mechanisms: Contact order, chain topology, stability, and the extended nucleus mechanism. Proceedings of the National Academy of Sciences of the United States of America, 97(4):15251529, 2000.
[146] P E Leopold, M Montal, and J N Onuchic. Protein folding funnels: a kinetic approach to the sequence-structure relationship. Proceedings of the National Academy of Sciences of the United States of America, 89(18):87218725, 1992.
[147] David J Wales. The energy landscape as a unifying theme in molecular science. hilosophical Transactions of the Royal Society - Series A: Mathematical, Physical and Engineering Sciences, 363(1827):357375; discussion 375377, 2005.
[148] C M Dobson and M Karplus. The fundamentals of protein folding: bringing together theory and experiment. Current Opinion in Structural Biology, 9(1):92 101, 1999.
[149] Jose Nelson Onuchic and Peter G Wolynes. Theory of protein folding. Current opinion in structural biology, 14(1):705, February 2004.
[150] Stefano Gianni, Christian D Geierhaas, Nicoletta Calosci, Per Jemth, Geerten W Vuister, Carlo Travaglini-Allocatelli, Michele Vendruscolo, and Maurizio Brunori. A PDZ domain recapitulates a unifying mechanism for protein folding. Proceedings of the National Academy of Sciences of the United States of America, 104(1):128133, 2007.
[151] Maurizio Brunori, Stefano Gianni, Rajanish Giri, Angela Morrone, and Carlo Travaglini-Allocatelli. Morphogenesis of a protein: folding pathways and the energy landscape. Biochemical Society Transactions, 40(2):42932, 2012.
[152] Ylva Ivarsson, Carlo Travaglini-Allocatelli, Maurizio Brunori, and Stefano Gianni. Mechanisms of protein folding. European biophysics journal EBJ, 37(6):721728, 2008.
[153] Maksym Tsytlonok and Laura S Itzhaki. The how′s and why′s of protein folding intermediates. Archives of biochemistry and biophysics, 531(1-2):1423, 2013.
[154] K Lindor-Larsen, S Piana, R O Dror, and D E Shaw. How Fast-Folding Proteins Fold. Science, 334(6055):517520, 2011.
[155] Jerey K Weber and Vijay S Pande. Protein folding is mechanistically robust. Biophysical Journal, 102(4):859867, 2012.
[156] Malgorzata Pokrzywa, Ingrid Dacklin, Dan Hultmark, and Erik Lundgren. Misfolded transthyretin causes behavioral changes in a Drosophila model for transthyretin-associated amyloidosis. European Journal of Neuroscience, 26(4):913924, 2007.
[157] Robert E Steward, Roger S Armen, and Valerie Daggett. Dierent disease-causing mutations in transthyretin trigger the same conformational conversion. Protein engineering design selection PEDS, 21(3):187195, 2008.
[158] Mingfeng Yang, Boyan Yordanov, Yaakov Levy, Rafael Bruschweiler, and Shuanghong Huo. The sequence-dependent unfolding pathway plays a critical role in the amyloidogenicity of transthyretin. Biochemistry, 45(39):1199212002,
[159] Robert Tycko. Progress towards a molecular-level structural understanding of amyloid brils. Current opinion in structural biology, 14(1):96103, February 2004.
[160] Jesper So rensen, Donald Hamelberg, Birgit Schio tt, and J Andrew McCammon. Comparative MD analysis of the stability of transthyretin providing insight into the brillation mechanism. Biopolymers, 86(1):7382, 2007.
[161] Ming Lei, Mingfeng Yang, and Shuanghong Huo. Intrinsic versus mutation dependent instability/exibility: a comparative analysis of the structure and dynamics of wild-type transthyretin and its pathogenic variants. Journal of Structural Biology, 148(2):153168, 2004.
[162] Massimiliano Porrini, Ulrich Zachariae, Perdita E. Barran, and Cait E. MacPhee. Eect of Protonation State on the Stability of Amyloid Oligomers Assembled from TTR(105-115). The Journal of Physical Chemistry Letters, pages 1233 1238, 2013.
[163] Roger S. Armen, Darwin O V Alonso, and Valerie Daggett. Anatomy of an amyloidogenic intermediate: Conversion of beta-sheet to alpha-sheet structure in transthyretin at acidic pH. Structure, 12(10):18471863, 2004.
[164] Po-Jen Hsu. a New Perspective of Shape Recognition To Discover the Phase Transition of Finite-Size Clusters. Journal of computational chemistry, pages 111, 2014.
[165] J F Gibrat, T Madej, and S H Bryant. Surprising similarities in structure comparison. Current Opinion in Structural Biology, 6(3):377385, 1996.
[166] S E Brenner, C Chothia, and T J Hubbard. Population statistics of protein structures: lessons from structural classications. Curr Opin Struct Biol, 7(3):36976., 1997.
[167] J L Sussman, D Lin, J Jiang, N O Manning, J Prilusky, O Ritter, and E E Abola. Protein Data Bank (PDB): Database of Three-Dimensional Structural Information of Biological Macromolecules. Acta Crystallographica Section D Biological Crystallography, 54(6):10781084, 1998.
[168] Ajay and Mark A Murcko. Computational Methods to Predict Binding Free Energy in Ligand-Receptor Complexes. Journal of Medicinal Chemistry, 38(26):49534967, 1995.
175[169] T Lengauer and M Rarey. Computational methods for biomolecular docking. Current Opinion in Structural Biology, 6(3):402406, 1996.
[170] Michael K Gilson and Huan-Xiang Zhou. Calculation of protein-ligand binding quanities. Annual review of biophysics and biomolecular structure, 36:2142, 2007.
[171] Stefan Henrich, Outi M H Salo-Ahen, Bingding Huang, Friedrich F Rippmann,
Gabriele Cruciani, and Rebecca C Wade. Computational approaches to identifying and characterizing protein binding sites for ligand design. Journal of molecular recognition JMR, 23(2):209219, 2010.
[172] Jonas Bostrom, Anders Hogner, and Stefan Schmitt. Do structurally similar ligands bind in a similar fashion? Journal of Medicinal Chemistry, 49(23):6716 6725, 2006.
[173] A. Wunderlin and H. Haken. Generalized Ginzburg-Landau equations, slaving principle and center manifold theorem. Zeitschrift fur Physik B Condensed Matter, 44(1-2):135141, March 1981.
[174] H Haken. Are cooperative phenomena governed by universal principles. Naturwissenschaften, 67(3):121128, 1980.
[175] Hermann Haken. Slaving principle revisited. Physica D: Nonlinear Phenomena, 97(1-3):95103, 1996.
[176] L M Pecora and T L Carroll. Synchronization in chaotic systems. Physical Review Letters, 64(8):821824, 1990.
[177] P Bernaola-Galvan, R Roman-Roldan, and J L Oliver. Compositional segmentation and long-range fractal correlations in DNA sequences. Physical Review E Statistical Physics Plasmas Fluids And Related Interdisciplinary Topics , 53(5):5181
5189, 1996.
[178] R Roman-Roldan, P Bernaola-Galvan, and J L Oliver. Sequence compositional complexity of DNA through an entropic segmentation method. Physical Review Letters, 80(6):13441347, 1998.
[179] Wentian Li. DNA Segmentation as A Model Selection Process. Genetics, page 7, 2001.
[180] Wentian Li. New stopping criteria for segmenting DNA sequences. Physical Review Letters, 86(25):58155818, 2001.
[181] G Schwarz. Estimating the dimension of a model. Annals of Statistics, 6(2):461 464, 1978.
176[182] Siew-Ann Cheong, Paul Stodghill, David J Schneider, Samuel W Cartinhour, and Christopher R Myers. Extending the Recursive Jensen-Shannon Segmentation of Biological Sequences. Transactions on Computational Biology and Bioinformatics, page 30, 2009.
[183] Changjun Chen and Yi Xiao. Observation of multiple folding pathways of beta-hairpin trpzip2 from independent continuous folding trajectories. Bioinformatics (Oxford, England), 24(5):659665, 2008.
[184] Yi Xiao, Changjun Chen, and Yi He. Folding mechanism of beta-hairpin trpzip2: Heterogeneity, transition state and folding pathways. International journal of molecular sciences, 10(6):28382848, 2009.
[185] L Holm and C Sander. Mapping the protein universe. Science (New York, N.Y.), 273(5275):595603, 1996.
[186] Pietro Di Lena, Marco Vassura, Luciano Margara, Piero Fariselli, and Rita Casadio. On the Reconstruction of Three-dimensional Protein Structures from Contact Maps, 2009.
[187] F. Morcos, A. Pagnani, B. Lunt, A. Bertolino, D. S. Marks, C. Sander, R. Zecchina, J. N. Onuchic, T. Hwa, and M. Weigt. PNAS Plus: Direct-coupling analysis of residue coevolution captures native contacts across many protein families, 2011.
[188] M Vendruscolo, E Kussell, and E Domany. Recovery of protein structure from contact maps. Folding & design, 2(5):295306, 1997.
[189] Alessandro Vullo, Ian Walsh, and Gianluca Pollastri. A two-stage approach for improved prediction of residue contact maps. BMC bioinformatics, 7:180, 2006.
指導教授 賴山強(San-Kiong Lai) 審核日期 2014-5-15
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