簡易檢索 / 詳目顯示

研究生: 韓孟淮
Han, Meng-Huai
論文名稱: 蛋白質於氣液界面之折疊機制與吸附行為自由能分析
Free Energy Analysis of Protein Folding and Adsorption at the Air/Water Interface
指導教授: 邱繼正
Chiu, Chi-Cheng
學位類別: 碩士
Master
系所名稱: 工學院 - 化學工程學系
Department of Chemical Engineering
論文出版年: 2017
畢業學年度: 105
語文別: 英文
論文頁數: 99
中文關鍵詞: 蛋白質氣液界面分子動力學模擬吸附自由能結構自由能
外文關鍵詞: protein, air/water interface, molecular dynamics, adsorption free energy, conformational free energy
相關次數: 點閱:92下載:2
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 蛋白質屬於有機生物性高分子在生物系統中扮演各種重要的角色;而蛋白質的功能和其三維結構密切相關。蛋白質結構易受外在環境影響,如溶液酸鹼值、溫度、與濃度等。蛋白質目前可廣泛應用於不同領域,例如材料表面改質的添加劑、或是生物感測器探測分子等。因此,為瞭解蛋白質的結構和吸附界面的關係,本研究探討四種不同結構類型的蛋白質:α-螺旋、β-摺板、蛋白質可折疊為α-螺旋與β-摺板、和類澱粉蛋白短鍊,從水相吸附到氣液界面時結構的變化。利用分子動力學模擬,我們從微觀的角度分析蛋白質在水相和氣液界面的α-螺旋與β-摺板結構轉變時自由能變化。我們也提出蛋白質吸附的熱力學模型,用以估算蛋白質從水溶液吸附自氣液界面時所需要的自由能。我們也使用模擬來計算蛋白質的吸附能,藉以證實吸附模型的準確性。結果顯示,雖然從模擬和吸附模型得到的絕對吸附能有些差異,但是在吸附能的相對值上兩者的表現相當類似。因此我們認為此吸附模型可有效地推估α-螺旋與β-摺板結構的相對吸附自由能差,並對應到分子動力學模擬,結合為一完整的蛋白質吸附與構型轉換的熱力學迴圈。由此結合熱力學理論模型與分子模擬分析顯示,蛋白質於氣液界面時,其所排開的氣液接觸所造成氣液界面自由能之變化,是蛋白質吸附於界面的主要驅動力。而蛋白質的氨基酸序列,亦會影響其於氣液界面的二級結構傾向。進一步應用此一模型,我們也探討了類澱粉蛋白短鍊的纖維結構於氣液界面的吸附機制,由於疏水蛋白質支鍊基團的排列方式不同,我們發現纖維吸附面對於其吸附自由能有很大的影響。

    Proteins are biological polymers that play many important roles in biological systems. The protein functions are highly correlated with the protein structures, which are affected by the solvent pH, temperature, and concentrations, etc. Protein have been applied in various fields, such as the substrate surface modifications, or the detecting molecules in the biosensors. In order to probe the effects of adsorbed interface on the protein structures, we investigate the protein conformational changes at the air/water interfaces for four different groups of proteins with different secondary structural characteristics, i.e. α-helix native, β-hairpin native, protein with equal α- and β-probability, and small amyloid peptide fibrils. We applied molecular dynamics combined with methadyanmics to calculate the protein conformational free energies in bulk water and at air/water interface. Furthermore, we developed a thermodynamics model for protein adsorption that focuses on two contributions, i.e. the desolvation of peptide residues and the reduction of air-water interfacial energy. Via the comparison between the peptide adsorption free energies at the air/water interface obtain by the theoretical prediction and simulation data, we found the proposed thermodynamic model accurately predicted the relative adsorption free energies of peptide in different conformations. Combining the protein adsorption free energies estimated from the thermodynamic model and the conformational free energy calculated from MD simulations, the complete thermodynamic cycle of protein adsorption and conformational change can be constructed. The results showed that the air/water interfacial energy changes, caused by the peptide allocation at the interface that inclines the air/water contact, are the main driving force of the protein adsorbing at the interface. Furthermore, the stability of protein secondary structure is also affected by the desolvation of the amino acid exposed to the air phase. Hence, the peptide sequence is important for the protein secondary structural preference at the interface. The model was further applied to investigate the adsorption free energy of small amyloid peptide fibrils. Our results showed that, owing to the arrangement of hydrophobic residues, the adsorbing fibril face play important roles for amyloid fibril adsorption at the air/water interface.

    摘要 I Abstract II Acknowledgment IV Table of Contents V List of Tables VII List of Figures IX List of Symbols XV CHAPTER 1 INTRODUCTION 1 CHAPTER 2 METHODS 13 2.1 Molecular Dynamics Simulation Details 13 2.1.1 Initial Protein Conformations 13 2.1.2 Protein in Bulk Water 14 2.1.3 Protein at Air/Water interface 14 2.1.4 Simulation Parameters 14 2.1.5 RSMD Analysis 15 2.2 Protein Conformational Free Energy 16 2.2.1 Metadynamics 16 2.2.2 Bias-Exchange 18 2.2.3 Collective Variables 19 2.2.4 Peptide Conformation Free Energy Difference 21 2.3 Protein Adsorption Free Energy 22 2.3.1 Adsorption Free Energy Estimation 22 2.3.2 Desolvation Free Energy 23 2.3.3 Surface Energy Change 24 2.3.4 Adsorption Free Energy Calculation via Umbrella Sampling 25 CHAPTER 3 RESULTS AND DISCUSSIONS 37 3.1 Validation of the Adsorption Free Energy Estimation 37 3.2 α-native Protein 40 3.2.1 Equilibrium Molecular Dynamics 41 3.2.2 Protein Conformational Free Energy 42 3.2.3 Contacts of Water 43 3.2.4 Adsorption Behavior 45 3.3 β-native Protein 47 3.3.1 Equilibrium Molecular Dynamics 47 3.3.2 Protein Conformational Free Energy 48 3.3.3 Contacts of Water 49 3.3.4 Adsorption Behavior 51 3.4 α/β Equal Probability Protein 52 3.4.1 Equilibrium Molecular Dynamics 53 3.4.2 Protein Conformational Free Energy 54 3.4.3 Contacts of Water 54 3.4.4 Adsorption Behavior 55 3.5 Small Peptide Aggregation 56 3.5.1 Dimerization Free Energy 56 3.5.2 Adsorption Behavior 57 CHAPTER 4 CONCLUSIONS 89 References 91

    Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., & Walter, P. (2003). Molecular biology of the cell: Garland Science.
    Aliev, A. E., Kulke, M., Khaneja, H. S., Chudasama, V., Sheppard, T. D., & Lanigan, R. M. (2014). Motional timescale predictions by molecular dynamics simulations: case study using proline and hydroxyproline sidechain dynamics. Proteins, 82(2), 195-215.
    Araki, M., & Tamura, A. (2007). Transformation of an alpha-helix peptide into a beta-hairpin induced by addition of a fragment results in creation of a coexisting state. Proteins, 66(4), 860-868.
    Aurenhammer, F. (1991). Voronoi Diagrams —A Survey of a Fundamental Geometric Data Structure. ACM Computing Surveys, 23(3), 345-405.
    Baftizadeh, F., Biarnes, X., Pietrucci, F., Affinito, F., & Laio, A. (2012). Multidimensional view of amyloid fibril nucleation in atomistic detail. Journal of the American Chemical Society, 134(8), 3886-3894.
    Ball, D. W., Hill, J. W., & Scott, R. J. (2012). Introduction to Chemistry: General, Organic, and Biological. Creative Commons.
    Banushkina, P. V., & Krivov, S. V. (2013). High-Resolution Free-Energy Landscape Analysis of alpha-Helical Protein Folding: HP35 and Its Double Mutant. J Chem Theory Comput, 9(12), 5257-5266.
    Barducci, A., Bonomi, M., & Parrinello, M. (2011). Metadynamics. Wiley Interdisciplinary Reviews: Computational Molecular Science, 1(5), 826-843.
    Barua, B., Lin, J. C., Williams, V. D., Kummler, P., Neidigh, J. W., & Andersen, N. H. (2008). The Trp-cage: optimizing the stability of a globular miniprotein. Protein Eng Des Sel, 21(3), 171-185.
    Berg, J. M., Tymoczko, J. L., & Stryer, L. (2002). Biochemistry (5 ed.): W.H. Freeman and Co.
    Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2000). The protein data bank. Nucleic acids research, 28(1), 235-242.
    Bonomi, M., Branduardi, D., Bussi, G., Camilloni, C., Provasi, D., Raiteri, P., Donadio, D., Marinelli, F., Pietrucci, F., & Broglia, R. A. (2009). PLUMED: A portable plugin for free-energy calculations with molecular dynamics. Computer Physics Communications, 180(10), 1961-1972.
    Bussi, G., Gervasio, F. L., Laio, A., & Parrinello, M. (2006). Free-energy landscape for β hairpin folding from combined parallel tempering and metadynamics. Journal of the American Chemical Society, 128(41), 13435-13441.
    Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.
    Chang, J., Lenhoff, A. M., & Sandler, S. I. (2007). Solvation free energy of amino acids and side-chain analogues. The Journal of Physical Chemistry B, 111(8), 2098-2106.
    Chary, K. V., & Govil, G. (2008). NMR in biological systems: from molecules to human (Vol. 6): Springer Science & Business Media.
    Chiu, C. C., Singh, S., & de Pablo, J. J. (2013). Effect of proline mutations on the monomer conformations of amylin. Biophys J, 105(5), 1227-1235.
    Dill, K. A. (1999). Polymer principles and protein folding. Protein Science, 8(06), 1166-1180.
    Dill, K. A., & MacCallum, J. L. (2012). The Protein-Folding Problem, 50 Years On. Science, 338, 1042-1046.
    Ding, Y., Bernardo, D. N., Krogh-Jespersen, K., & Levy, R. M. (1995). Solvation free energies of small amides and amines from molecular dynamics/free energy perturbation simulations using pairwise additive and many-body polarizable potentials. The Journal of Physical Chemistry, 99(29), 11575-11583.
    Engin, O., & Sayar, M. (2012). Adsorption, folding, and packing of an amphiphilic peptide at the air/water interface. J Phys Chem B, 116(7), 2198-2207.
    Englander, S. W., & Mayne, L. (2014). The nature of protein folding pathways. Proc Natl Acad Sci U S A, 111(45), 15873-15880.
    Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Le, H., & Pedersen, L. G. (1995). A smooth particle mesh Ewald method. J Chem Phys, 103, 8577-8593.
    Fukunishi, H., Watanabe, O., & Takada, S. (2002). On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: Application to protein structure prediction. The Journal of Chemical Physics, 116(20), 9058-9067.
    Gallagher, T., Alexander, P., Bryan, P., & L., G. G. (1994). Two Crystal Structures of the B 1 Immunoglobulin-Binding Domain of Streptococcal Protein G and Comparison with NMR. Biochemistry, 33, 4721-4729.
    Goncalves, A. M., Pedro, A. Q., Santos, F. M., Martins, L. M., Maia, C. J., Queiroz, J. A., & Passarinha, L. A. (2014). Trends in protein-based biosensor assemblies for drug screening and pharmaceutical kinetic studies. Molecules, 19(8), 12461-12485.
    Granata, D., Camilloni, C., Vendruscolo, M., & Laio, A. (2013). Characterization of the free-energy landscapes of proteins by NMR-guided metadynamics. Proc Natl Acad Sci U S A, 110(17), 6817-6822.
    Grossfield, A. An implementation of WHAM: the Weighted Histogram Analysis Method Version 2.0.9.
    Hall, M. N., & Tamanoi, F. (2010). Structure, function and regulation of tor complexes from yeasts to mammals (Vol. 27): Academic Press.
    Heim, M., Römer, L., & Scheibel, T. (2010). Hierarchical structures made of proteins. The complex architecture of spider webs and their constituent silk proteins. Chemical Society Reviews, 39(1), 156-164.
    Hess, B., Bekker, H., Berendsen, H. J. C., & Fraaije, J. G. E. M. (1997). LINCS: a linear constraint solver for molecular simulations. J Comput Chem, 18, 1463-1472.
    Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual Molecular Dynamics. Journal of Molecular Graphics, 13, 33-38.
    Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. J Chem Phys, 79(2), 926-935.
    Kästner, J. (2011). Umbrella sampling. Wiley Interdisciplinary Reviews: Computational Molecular Science, 1(6), 932-942.
    Kearsley, S. K. (1989). On the orthogonal transformation used for structural comparisons. Acta Crystallographica Section A: Foundations of Crystallography, 45(2), 208-210.
    Krotee, P., Rodriguez, J. A., Sawaya, M. R., Cascio, D., Reyes, F. E., Shi, D., Hattne, J., Nannenga, B. L., Oskarsson, M. E., Philipp, S., Griner, S., Jiang, L., Glabe, C. G., Westermark, G. T., Gonen, T., & Eisenberg, D. S. (2017). Atomic structures of fibrillar segments of hIAPP suggest tightly mated beta-sheets are important for cytotoxicity. Elife, 6.
    Kumar, S., Bouzida, D., Swendsen, R. H., Kollman, P. A., & Rosenberg, J. M. (1992). The Weighted Histogram Analysis Method for Free-Energy Calculations on Biomolecules. I. The Method. Journal of Computational Chemistry, 13(8), 1011-1021.
    Laio, A., & Gervasio, F. L. (2008). Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Reports on Progress in Physics, 71(12), 126601.
    Lin, C. Y., Chen, N. Y., & Mou, C. Y. (2012). Folding a protein with equal probability of being helix or hairpin. Biophys J, 103(1), 99-108.
    Lucent, D., Vishal, V., & Pande, V. S. (2007). Protein folding under confinement: a role for solvent. Proc Natl Acad Sci U S A, 104(25), 10430-10434.
    Martyna, G. J., Klein, M. L., & Tuckerman, M. (1992). Nose-Hoover chains: The canonical ensemble via continuous dynamics. J Chem Phys, 97(4), 2635-2643.
    Martyna, G. J., Tobias, D. J., & Klein, M. L. (1994). Constant pressure molecular dynamics algorithms. J Chem Phys, 101(5), 4177-4189.
    Mathews, C. K., van Holde, K. E., Appling, D. R., & Anthony-Cahill, S. J. (2013). Biochemistry (4 ed.).
    Nellas, R. B., Johnson, Q. R., & Shen, T. (2013). Solvent-induced alpha- to 3(10)-helix transition of an amphiphilic peptide. Biochemistry, 52(40), 7137-7144.
    Nelson, R., Sawaya, M. R., Balbirnie, M., Madsen, A. O., Riekel, C., Grothe, R., & Eisenberg, D. (2005). Structure of the cross-beta spine of amyloid-like fibrils. Nature, 435(7043), 773-778.
    Nosé, S. (1984). A unified formulation of the constant temperature molecular dynamics methods. The Journal of Chemical Physics, 81(1), 511-519.
    Park, J.-H., Lee, J.-W., & Park, H.-S. (2010). Computational Prediction of Solvation Free Energies of Amino Acids with Genetic Algorithm. Bulletin of the Korean Chemical Society, 31(5), 1247-1251.
    Parrinello, M., & Rahman, A. (1981). Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics, 52(12), 7182-7190.
    Pastor, M. T., Lopez de la Paz, M., Lacroix, E., Serrano, L., & Perez-Paya, E. (2002). Combinatorial approaches: a new tool to search for highly structured beta-hairpin peptides. Proc Natl Acad Sci U S A, 99(2), 614-619.
    Piana, S., & Laio, A. (2007). A Bias-Exchange Approach to Protein Folding. J Phys Chem B, 111, 4553-4559.
    Pieranski, P. (1980). Two-Dimensional Interfacial Colloidal Crystals. Physical Review Letters, 45(7), 569-572.
    Pietrucci, F., & Laio, A. (2009). A Collective Variable for the Efficient Exploration of Protein Beta-Sheet Structures: Application to SH3 and GB1. J Chem Theory Comput, 5, 2197-2201.
    Pronk, S., Pall, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D., Hess, B., & Lindahl, E. (2013). GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 29(7), 845-854.
    Raiteri, P., Laio, A., Gervasio, F. L., Micheletti, C., & Parrinello, M. (2006). Efficient reconstruction of complex free energy landscapes by multiple walkers metadynamics. The Journal of Physical Chemistry B, 110(8), 3533-3539.
    Reddy, A. S., Chopra, M., & de Pablo, J. J. (2010). GNNQQNY--investigation of early steps during amyloid formation. Biophys J, 98(6), 1038-1045.
    Roux, B. (1995). The calculation of the potential of mean force using computer simulations. Computer Physics Communications, 91(1), 275-282.
    Saladin, K. S. (2012). Anatomy & physiology : the unity of form and function. New York, NY: McGraw-Hill.
    Soriaga, A. B., Sangwan, S., Macdonald, R., Sawaya, M. R., & Eisenberg, D. (2015). Crystal Structures of IAPP Amyloidogenic Segments Reveal a Novel Packing Motif of Out-of-Register Beta Sheets. J Phys Chem B.
    Souaille, M., & Roux, B. t. (2001). Extension to the weighted histogram analysis method: combining umbrella sampling with free energy calculations. Computer Physics Communications, 135(1), 40-57.
    Tenidis, K., Waldner, M., Bernhagen, J., Fischle, W., Bergmann, M., Weber, M., Merkle, M.-L., Voelter, W., Brunner, H., & Kapurniotu, A. (2000). Identification of a Penta- and Hexapeptide of Islet Amyloid Polypeptide (IAPP) with Amyloidogenic and Cytotoxic Properties. J Mol Biol, 295, 1055-1071.
    Tomlinson, I. M. (2004). Next-generation protein drugs. Nature Bio, 22(5), 521-522.
    Torrie, G. M., & Valleau, J. P. (1977). Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. Journal of Computational Physics, 23(2), 187-199.
    Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. (2005). GROMACS: fast, flexible, and free. J Comput Chem, 26(16), 1701-1718.
    Wang, K.-H., Lin, W.-D., Wu, J.-Y., & Lee, Y.-L. (2013). Conformation transitions of adsorbed proteins by interfacial forces at an air–liquid interface and their effect on the catalytic activity of proteins. Soft Matter, 9(9), 2717.
    Webber, M. J., Appel, E. A., Meijer, E. W., & Langer, R. (2016). Supramolecular biomaterials. Nat Mater, 15(1), 13-26.
    Wolfenden, R. (1978). Interaction of the peptide bond with solvent water: a vapor phase analysis. Biochemistry, 17(1), 201-204.
    Wolfenden, R., Andersson, L., Cullis, P., & Southgate, C. (1981). Affinities of amino acid side chains for solvent water. Biochemistry, 20(4), 849-855.
    Zerze, G. H., Miller, C. M., Granata, D., & Mittal, J. (2015). Free energy surface of an intrinsically disordered protein: comparison between temperature replica exchange molecular dynamics and bias-exchange metadynamics. J Chem Theory Comput, 11(6), 2776-2782.
    Zerze, G. H., Mullen, R. G., Levine, Z. A., Shea, J. E., & Mittal, J. (2015). To What Extent Does Surface Hydrophobicity Dictate Peptide Folding and Stability near Surfaces? Langmuir, 31(44), 12223-12230.

    無法下載圖示 校內:2019-01-01公開
    校外:不公開
    電子論文尚未授權公開,紙本請查館藏目錄
    QR CODE