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Modeling of Proteins and Their Interactions with Solvent

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Advances in Cell Mechanics

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Abstract

Sickle cell anemia is the first disease whose cause was pinpointed at a genetic level. Hydrophobic interaction is the main cause for sickle hemoglobin (hemoglobin S) sticking to itself. Interaction of water with hydrophobic objects plays a major role in molecular self-assembly processes[1-3], as well in the process of aggregation of hemoglobin S. In this chapter, slow motion analysis and model reduction methods were employed for the study of hemoglobin-hemoglobin interaction. Through this study, a new computational framework was presented for calculating the normal modes and interactions of proteins, macromolecular assemblies, and surrounding solvents. The framework employs a combination of Molecular Dynamics (MD) simulation and Principal Component Analysis (PCA). It enables the capture and visualization of the molecules’ normal modes and interactions over time scales that are computationally challenging, providing a starting point for experimental and further computational studies of protein conformational changes. We hope that the modeling protocols or procedures established for this known pathology will eventually shed some light on other complex biological systems that are not well known.

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References

  1. Lum K, Chandler D and Weeks J D. Hydrophobicity at small and large length scales. The Journal of Physical Chemistry B, 103(22): 4570–4577, 1999.

    Article  Google Scholar 

  2. Israelachvili J and Wennerstrom H. Role of hydration and water structure in biological and colloidal interactions. Nature, 379(6562): 219–225, 1996.

    Article  Google Scholar 

  3. Tanford C. The Hydrophobic Effect: Formation of Micelles and Biological Membranes. Wiley, New York, 1980.

    Google Scholar 

  4. Michael G. Rossmann, Morais M C, et al. Combining X-Ray crystallography and electron microscopy. Structure, 13(3): 355–362, 2005.

    Article  Google Scholar 

  5. Saibil H R. Conformational changes studied by cryo-electron microscopy. Nature Structural & Molecular Biology, 7(9): 711–714, 2000.

    Article  Google Scholar 

  6. Ishima R and Torchia D A. Protein dynamics from NMR. Nature Structural & Molecular Biology, 7(9): 740–743, 2000.

    Article  Google Scholar 

  7. Elber R. Long-timescale simulation methods. Current Opinion in Structural Biology, 15(2): 151–156, 2005. Theory and simulation/Macromolecular assemblages.

    Article  Google Scholar 

  8. Schlick T, Barth E and Mandziuk M. Biomolecular dynamics at long timesteps: Bridging the timescale gap between simulation and experimentation. Annual Review of Biophysics and Biomolecular Structure, 26(1): 181–222, 1997.

    Article  Google Scholar 

  9. Tama F, Gadea F X, Marques O, et al. Building-block approach for determining low-frequency normal modes of macromolecules. Proteins: Structure, Function, and Genetics, 41(1): 1–7, 2000.

    Article  Google Scholar 

  10. Marques O and Sanejouand Y-H. Hinge-bending motion in citrate synthase arising from normal mode calculations. Proteins: Structure, Function, and Genetics, 23(4): 557–560, 1995.

    Article  Google Scholar 

  11. Perahia D and Mouawad L. Computation of low-frequency normal modes in macromolecules: Improvements to the method of diagonalization in a mixed basis and application to hemoglobin. In Third Conference on Computers in Chemistry. Computers & Chemistry, 19(3): 241–246, 1995.

    Google Scholar 

  12. Harris S A and Laughton C A. A simple physical description of DNA dynamics: Quasi-harmonic analysis as a route to the configurational entropy. Journal of Physics: Condensed Matter, 19(7): 076103, 2007.

    Article  Google Scholar 

  13. Barton N P, Verma C S and Caves L S D. Inherent flexibility of calmodulin domains: a normal-mode analysis study. The Journal of Physical Chemistry B, 106(42): 11036–11040, 2002.

    Article  Google Scholar 

  14. Ma Jianpeng. Usefulness and limitations of normal mode analysis in modeling dynamics of biomolecular complexes. Structure, 13(3): 373–380, 2005.

    Article  Google Scholar 

  15. Herrick J B. Peculiar elongated and sickle-shaped red blood corpuscles in a case of severe anemia. Arch Intern Med, VI(5): 517–521, 1910.

    Article  Google Scholar 

  16. Shaw D E, Dror R O, Salmon J K, et al. Millisecond-scale molecular dynamics simulations on anton. In SC’ 09: Proceedings of the Conference on High Performance Computing Networking, New York, USA, 2009. Storage and Analysis, pages 1–11, 2009.

    Google Scholar 

  17. Amadei A, Linssen A B M and Berendsen H J C. Essential dynamics of proteins. Proteins: Structure, Function, and Bioinformatics, 17(4): 412–425, 1993.

    Article  Google Scholar 

  18. Harrison R W. Variational calculation of the normal modes of a large macromolecule: methods and some initial results. Biopolymers, 23(12): 2943–2949, 1984.

    Article  Google Scholar 

  19. Brooks B and Karplus M. Normal modes for specific motions of macromolecules: Application to the hinge-bending mode of lysozyme. Proceedings of the National Academy of Sciences of the United States of America, 82(15): 4995–4999, 1985.

    Article  Google Scholar 

  20. Gibrat J F and Gõ N. Normal mode analysis of human lysozyme: Study of the relative motion of the two domains and characterization of the harmonic motion. Proteins: Structure, Function, and Genetics, 8(3): 258–279, 1990.

    Google Scholar 

  21. Teeter M M and Case D A. Harmonic and quasiharmonic descriptions of crambin. The Journal of Physical Chemistry, 94(21): 8091–8097, 1990.

    Article  Google Scholar 

  22. Weiner J H. Statistical mechanics of elasticity. CRC Press, New York, 1983.

    MATH  Google Scholar 

  23. Hayward S and Gõ N. Collective variable description of native protein dynamics. Annual Review of Physical Chemistry, 46(1): 223–250, 1995.

    Article  Google Scholar 

  24. Tirion M M. Large amplitude elastic motions in proteins from a singleparameter, atomic analysis. Phys. Rev. Lett., 77(9): 1905–1908, 1996.

    Article  Google Scholar 

  25. Rod T H, Radkiewicz J L and Brooks C L. Correlated motion and the effect of distal mutations in dihydrofolate reductase. Proceedings of the National Academy of Sciences of the United States of America, 100(12): 6980–6985, 2003.

    Article  Google Scholar 

  26. Cheng X, Ivanov I, Wang H, et al. Nanosecond-timescale conformational dynamics of the human 7 nicotinic acetylcholine receptor. Biophysical Journal, 93(8): 2622–2634, 2007.

    Article  Google Scholar 

  27. Bathe K J, Nitikitpaiboon C and Wang X. A mixed displacement-based finite element formulation for acoustic fluid-structure interaction. Computers & Structures, 56(2/3): 225–237, 1995.

    Article  MathSciNet  Google Scholar 

  28. Chakraverly S. Vibration of Plates. CRC Press, New York, 2009.

    Google Scholar 

  29. Karplus M and Kushick J N. Method for estimating the configurational entropy of macromolecules. Macromolecules, 14(2): 325–332, 1981.

    Article  Google Scholar 

  30. Ramanathan A and Agarwal P K. Computational identification of slow conformational fluctuations in proteins. The Journal of Physical Chemistry B, 113(52): 16669–16680, 2009.

    Article  Google Scholar 

  31. Noid D W, Fukui K, Sumpter B G, et al. Time-averaged normal coordinate analysis of polymer particles and crystals. Chemical Physics Letters, 316(3–4): 285–296, 2000.

    Article  Google Scholar 

  32. Agarwal P K, Billeter S R, et al. Network of coupled promoting motions in enzyme catalysis. Proceedings of the National Academy of Sciences of the United States of America, 99(5): 2794–2799, 2002.

    Article  Google Scholar 

  33. Agarwal P K. Enzymes: An integrated view of structure, dynamics and function. Microbial Cell Factories, 5(1): 2, 2006.

    Article  Google Scholar 

  34. Cannon W R and Benkovic S J. Solvation, reorganization energy, and biological catalysis. Journal of Biological Chemistry, 273(41): 26257–26260, 1998.

    Article  Google Scholar 

  35. Hammes G G. Multiple conformational changes in enzyme catalysis. Biochemistry, 41(26): 8221–8228, 2002. PMID: 12081470.

    Article  Google Scholar 

  36. Hammes-Schiffer S. Impact of enzymemotion on activity. Biochemistry, 41(45): 13335–13343, 2002.

    Article  Google Scholar 

  37. Caratzoulas S, Mincer J S and Schwartz S D. Identification of a proteinpromoting vibration in the reaction catalyzed by horse liver alcohol dehydrogenase. Journal of the American Chemical Society, 124(13): 3270–3276, 2002.

    Article  Google Scholar 

  38. Eisenmesser E Z, Bosco D A, Akke M, et al. Enzyme dynamics during catalysis. Science, 295(5559): 1520–1523, 2002.

    Article  Google Scholar 

  39. Benkovic S J and Hammes-Schiffer S. A perspective on enzyme catalysis. Science, 301(5637): 1196–1202, 2003.

    Article  Google Scholar 

  40. Agarwal P K. Role of protein dynamics in reaction rate enhancement by enzymes. Journal of the American Chemical Society, 127(43): 15248–15256, 2005.

    Article  Google Scholar 

  41. Eisenmesser E Z, Millet O, Labeikovsky W, et al. Intrinsic dynamics of an enzyme underlies catalysis. Nature, 438(7064): 117–121, 2005.

    Article  Google Scholar 

  42. Henzler-Wildman K A, Lei Ming, Thai V, et al. A hierarchy of timescales in protein dynamics is linked to enzyme catalysis. Nature, 450(7171): 913–916, Dec., 2007.

    Article  Google Scholar 

  43. Tournier A L and Smith J C. Principal components of the protein dynamical transition. Phys. Rev. Lett., 91(20): 208106, 2003.

    Article  Google Scholar 

  44. Harris S A, Sands Z A and Laughton C A. Molecular dynamics simulations of duplex stretching reveal the importance of entropy in determining the biomechanical properties of DNA. Biophysical Journal, 88(3): 1684–1691, 2005.

    Article  Google Scholar 

  45. Noy A, Pérez A, Laughton C A, et al. Theoretical study of large conformational transitions in DNA: The B-A conformational change in water and ethanol/water. Nucleic Acids Research, 35(10): 3330–3338, 2007.

    Article  Google Scholar 

  46. Dixit S B, Andrews D Q and Beveridge D L. Induced fit and the entropy of structural adaptation in the complexation of CAP and lambda-repressor with cognate DNA sequences. Biophysical Journal, 88(5): 3147–3157, 2005.

    Article  Google Scholar 

  47. Bradley M J, Chivers P T and Baker N A. Molecular dynamics simulation of the Escherichia coli NikR protein: Equilibrium conformational fluctuations reveal interdomain allosteric communication pathways. Journal of Molecular Biology, 378(5): 1155–1173, 2008.

    Article  Google Scholar 

  48. Brooks B R, Bruccoleri R E, Olafson B D, et al. Charmm: A program for macromolecular energy, minimization, and dynamics calculations. Journal of Computational Chemistry, 4(2): 187–217, 1983.

    Article  Google Scholar 

  49. Lange O F and Grubmüller H. Generalized correlation for biomolecular dynamics. Proteins: Structure, Function, and Bioinformatics, 62(4): 1053–1061, 2006.

    Article  Google Scholar 

  50. Waight A B, Love J and Wang D N. Structure and mechanism of a pentameric formate channel. Nat Struct Mol Biol, 17(1): 31–37, 2010.

    Article  Google Scholar 

  51. Kabsch W. A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A, 32(5): 922–923, 1976.

    Article  Google Scholar 

  52. Kabsch W. A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A, 34(5): 827–828, 1978.

    Article  Google Scholar 

  53. Durbin R, Eddy S R, Krogh A, et al. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, New York, 1999.

    Google Scholar 

  54. Phillips J C, Braun R, Wang Wei, et al. Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26(16): 1781–1802, 2005.

    Article  Google Scholar 

  55. Matsumura M, Wozniak J A, Sun D P, et al. Structural studies of mutants of T4 lysozyme that alter hydrophobic stabilization. Journal of Biological Chemistry, 264(27): 16059–16066, 1989.

    Google Scholar 

  56. Humphrey W, Dalke A and Schulten K. VMD-visual molecular dynamics. Journal of Molecular Graphics, 14: 33–38, 1996.

    Article  Google Scholar 

  57. Bhandarkar M, Brunner R, Chipot C, et al. NAMD User’s Guide, 2008.

    Google Scholar 

  58. Ryckaert J, Ciccotti G and Berendsen H. Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. Journal of Computational Physics, 23(3): 327–341, 1977.

    Article  Google Scholar 

  59. Kundu S, Melton J S, Sorensen D C, et al. Dynamics of proteins in crystals: Comparison of experiment with simple models. Biophysical Journal, 83(2): 723–732, 2002.

    Article  Google Scholar 

  60. Cattell R B. The scree test for the number of factors. Multivariate Behavioral Research, 1(2): 245–276, 1966.

    Article  Google Scholar 

  61. Bathe M. A finite element framework for computation of protein normal modes and mechanical response. Proteins: Structure, Function, and Bioinformatics, 70(4): 1595–1609, 2008.

    Google Scholar 

  62. Wallqvist A, Gallicchio E and Levy R M. A model for studying drying at hydrophobic interfaces: Structural and thermodynamic properties. The Journal of Physical Chemistry B, 105(28): 6745–6753, 2001.

    Article  Google Scholar 

  63. Scatena L F, Brown M G and Richmond G L. Water at hydrophobic surfaces: Weak hydrogen bonding and strong orientation effects. Science, 292(5518): 908–912, 2001.

    Article  Google Scholar 

  64. Adachi K, Konitzer P, Paulraj C G, et al. Role of Leu-beta 88 in the hydrophobic acceptor pocket for Val-beta 6 during hemoglobin S polymerization. Journal of Biological Chemistry, 269(26): 17477–17480, 1994.

    Google Scholar 

  65. Jensen M O, Mouritsen O G and Peters G H. The hydrophobic effect: Molecular dynamics simulations of water confined between extended hydrophobic and hydrophilic surfaces. The Journal of Chemical Physics, 120(20): 9729–9744, 2004.

    Article  Google Scholar 

  66. Kavanaugh J S, Moo-Penn W F and Arnone A. Accommodation of inser-tions in helixes: The mutation in hemoglobin catonsville (Pro 37α-Glu-Thr 38α) generates a 310 → α bulge. Biochemistry, 32(10): 2509–2513, 1993.

    Article  Google Scholar 

  67. Darden T, York D and Pedersen L. Particle mesh Ewald: An N· log(N) method for Ewald sums in large systems. The Journal of Chemical Physics, 98(12): 10089–10092, 1993.

    Article  Google Scholar 

  68. Essmann U, Perera L, Berkowitz M L, et al. A smooth particle mesh Ewald method. The Journal of Chemical Physics, 103(19): 8577–8593, 1995.

    Article  Google Scholar 

  69. Den Otter W K and Briels W J. Free energy from molecular dynamics with multiple constraints. Molecular Physics: An International Journal at the Interface between Chemistry and Physics, 98(12): 773–781, 2000.

    Google Scholar 

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Authors and Affiliations

  1. McCoy School of Engineering, College of Science and Mathematics, Midwestern State University, 3410 Taft Blvd., Wichita Falls, TX, 76308, USA

    X. Sheldon Wang

  2. Department of Computer Science, College of Computing Sciences New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, USA

    Tao Wu & Barry Cohen

Authors
  1. Tao Wu
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  2. X. Sheldon Wang
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  3. Barry Cohen
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Correspondence to X. Sheldon Wang .

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Editors and Affiliations

  1. Department of Civil and Environmental Engineering, The University of California at Berkeley, USA

    Shaofan Li

  2. Centre for Mechanics, Smart Structures and Microsystems, Cape Peninsula University of Technology, Cape Town, South Africa

    Bohua Sun

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© 2011 Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg

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Wu, T., Wang, X.S., Cohen, B. (2011). Modeling of Proteins and Their Interactions with Solvent. In: Li, S., Sun, B. (eds) Advances in Cell Mechanics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17590-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-17590-9_3

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