1932

Abstract

Protein aggregation involves the self-assembly of normally soluble proteins into large supramolecular assemblies. The typical end product of aggregation is the amyloid fibril, an extended structure enriched in β-sheet content. The aggregation process has been linked to a number of diseases, most notably Alzheimer's disease, but fibril formation can also play a functional role in certain organisms. This review focuses on theoretical studies of the process of fibril formation, with an emphasis on the computational models and methods commonly used to tackle this problem.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-physchem-040513-103738
2015-04-01
2024-12-14
Loading full text...

Full text loading...

/deliver/fulltext/physchem/66/1/annurev-physchem-040513-103738.html?itemId=/content/journals/10.1146/annurev-physchem-040513-103738&mimeType=html&fmt=ahah

Literature Cited

  1. Chiti F, Dobson C. 1.  2006. Protein misfolding, functional amyloid, and human disease. Annu. Rev. Biochem. 75:333–66 [Google Scholar]
  2. Knowles TP, Vendruscolo M, Dobson CM. 2.  2014. The amyloid state and its association with protein misfolding diseases. Nat. Rev. Mol. Cell Biol. 15:384–96 [Google Scholar]
  3. Knowles TPJ, Buehler MJ. 3.  2011. Nanomechanics of functional and pathological amyloid material. Nat. Nanotechnol. 6:469–79 [Google Scholar]
  4. Fitzpatrick AWP, Debelouchina GT, Bayro MJ, Clare DK, Caporini MA. 4.  et al. 2013. Atomic structure and hierarchical assembly of a cross-β amyloid fibril. Proc. Natl. Acad. Sci. USA 110:5468–73 [Google Scholar]
  5. Sunde M, Serpell LC, Bartlam M, Fraser PE, Pepys MB, Blake CC. 5.  1997. Common core structure of amyloid fibrils by synchrotron X-ray diffraction. J. Mol. Biol. 273:729–39 [Google Scholar]
  6. Thirumalai D, Klimov D, Dima R. 6.  2003. Emerging ideas on the molecular basis of protein and peptide aggregation. Curr. Opin. Struct. Biol. 13:146–59 [Google Scholar]
  7. Straub J, Thirumalai D. 7.  2011. Toward a molecular theory of early and late events in monomer to amyloid fibril formation. Annu. Rev. Phys. Chem. 62:437–63 [Google Scholar]
  8. Knowles TP, Waudby CA, Devlin GL, Cohen SI, Aguzzi A. 8.  et al. 2009. An analytical solution to the kinetics of breakable filament assembly. Science 326:1533–37 [Google Scholar]
  9. Michaels TC, Knowles TP. 9.  2014. Role of filament annealing in the kinetics and thermodynamics of nucleated polymerization. J. Chem. Phys. 140:214904 [Google Scholar]
  10. Friesner RA, Guallar V. 10.  2005. Ab initio quantum chemical and mixed quantum mechanics/molecular mechanics (QM/MM) methods for studying enzymatic catalysis. Annu. Rev. Phys. Chem. 56:389–427 [Google Scholar]
  11. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M. 11.  1983. CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4:187–217 [Google Scholar]
  12. Pearlman DA, Case DA, Caldwell JW, Ross WS, Cheatham TE III. 12.  et al. 1995. AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput. Phys. Commun. 91:1–41 [Google Scholar]
  13. Christen M, Hünenberger PH, Bakowies D, Baron R, Bürgi R. 13.  et al. 2005. The GROMOS software for biomolecular simulation: GROMOS05. J. Comput. Chem. 26:1719–51 [Google Scholar]
  14. Jorgensen WL, Tirado-Rives J. 14.  1988. The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J. Am. Chem. Soc. 110:1657–66 [Google Scholar]
  15. Monticelli L, Kandasamy SK, Periole X, Larson RG, Tieleman DP, Marrink SJ. 15.  2008. The MARTINI coarse-grained force field: extension to proteins. J. Chem. Theory Comput. 4:819–34 [Google Scholar]
  16. Sterpone F, Melchionna S, Tuffery P, Pasquali S, Mousseau N. 16.  et al. 2014. The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems. Chem. Soc. Rev. 43:4871–93 [Google Scholar]
  17. Cheon M, Chang I, Hall C. 17.  2010. Extending the prime model for protein aggregation to all 20 amino acids. Proteins 78:2950–60 [Google Scholar]
  18. Kinjo AR, Takada S. 18.  2003. Competition between protein folding and aggregation with molecular chaperones in crowded solutions: insight from mesoscopic simulations. Biophys. J. 85:3521–31 [Google Scholar]
  19. Irbäck A, Jónsson , Linnemann N, Linse B, Wallin S. 19.  2013. Aggregate geometry in amyloid fibril nucleation. Phys. Rev. Lett. 110:058101 [Google Scholar]
  20. Bieler NS, Knowles TP, Frenkel D, Vácha R. 20.  2012. Connecting macroscopic observables and microscopic assembly events in amyloid formation using coarse grained simulations. PLoS Comput. Biol. 8:e1002692 [Google Scholar]
  21. Li M, Klimov D, Straub J, Thirumalai D. 21.  2008. Probing the mechanisms of fibril formation using lattice models. J. Chem. Phys. 129:175101 [Google Scholar]
  22. Friedman R, Caflisch A. 22.  2011. Surfactant effects on amyloid aggregation kinetics. J. Mol. Biol. 414:303–12 [Google Scholar]
  23. Bellesia G, Shea JE. 23.  2007. Self-assembly of β-sheet forming peptides into chiral fibrillar aggregates. J. Chem. Phys. 126:245104 [Google Scholar]
  24. Carmichael SP, Shell MS. 24.  2012. A new multiscale algorithm and its application to coarse-grained peptide models for self-assembly. J. Phys. Chem. B 116:8383–93 [Google Scholar]
  25. Rosenman DJ, Connors CR, Chen W, Wang C, Garcia AE. 25.  2013. Aβ monomers transiently sample oligomer and fibril-like configurations: ensemble characterization using a combined MD/NMR approach. J. Mol. Biol. 425:3338–59 [Google Scholar]
  26. Wu C, Bowers MT, Shea JE. 26.  2011. On the origin of the stronger binding of PIB over thioflavin T to protofibrils of the Alzheimer amyloid-β peptide: a molecular dynamics study. Biophys. J. 100:1316–24 [Google Scholar]
  27. Best RB, Mittal J. 27.  2011. Free-energy landscape of the GB1 hairpin in all-atom explicit solvent simulations with different force fields: similarities and differences. Proteins 79:1318–28 [Google Scholar]
  28. Bitan G, Kirkitadze M, Lomakin A, Vollers S, Benedek G, Teplow D. 28.  2003. Amyloid β-protein (Aβ) assembly: Aβ40 and Aβ42 oligomerize through distinct pathways. Proc. Natl. Acad. Sci. USA 100:330–35 [Google Scholar]
  29. Bernstein SL, Dupuis NF, Lazo ND, Wyttenbach T, Condron MM. 29.  et al. 2009. Amyloid-β protein oligomerization and the importance of tetramers and dodecamers in the aetiology of Alzheimer's disease. Nat. Chem. 1:326–31 [Google Scholar]
  30. Ball KA, Phillips AH, Wemmer DE, Head-Gordon T. 30.  2013. Differences in β-strand populations of monomeric Aβ40 and Aβ42. Biophys. J. 104:2714–24 [Google Scholar]
  31. Lazo N, Grant M, Condron M, Rigby A, Teplow D. 31.  2005. On the nucleation of amyloid β-protein monomer folding. Protein Sci. 14:1581–96 [Google Scholar]
  32. Murray MM, Krone MG, Bernstein SL, Baumketner A, Condron MM. 32.  et al. 2009. Amyloid β-protein: experiment and theory on the 21–30 fragment. J. Phys. Chem. B 113:6041–46 [Google Scholar]
  33. Krone MG, Baumketner A, Bernstein SL, Wyttenbach T, Lazo ND. 33.  et al. 2008. Effects of familial Alzheimer's disease mutations on the folding nucleation of the amyloid β-protein. J. Mol. Biol. 381:221–28 [Google Scholar]
  34. Wu C, Murray MM, Bernstein SL, Condron MM, Bitan G. 34.  et al. 2009. The structure of Aβ42 C-terminal fragments probed by a combined experimental and theoretical study. J. Mol. Biol. 387:492–501 [Google Scholar]
  35. Wu C, Shea JE. 35.  2013. Structural similarities and differences between amyloidogenic and non-amyloidogenic islet amyloid polypeptide (IAPP) sequences and implications for the dual physiological and pathological activities of these peptides. PLoS Comput. Biol. 9:e1003211 [Google Scholar]
  36. Reddy AS, Wang L, Singh S, Ling YL, Buchanan L. 36.  et al. 2010. Stable and metastable states of human amylin in solution. Biophys. J. 99:2208–16 [Google Scholar]
  37. Dupuis NF, Wu C, Shea JE, Bowers M. 37.  2009. Human islet amyloid polypeptide monomers form ordered β-hairpins: a possible direct amyloidogenic precursor. J. Am. Chem. Soc. 191:18283–92 [Google Scholar]
  38. Miller Y, Ma B, Nussinov R. 38.  2010. Polymorphism in Alzheimer Aβ amyloid organization reflects conformational selection in a rugged energy landscape. Chem. Rev. 110:4820–38 [Google Scholar]
  39. Zhao J, Yu X, Liang G, Zheng J. 39.  2011. Heterogeneous triangular structures of human islet amyloid polypeptide (amylin) with internal hydrophobic cavity and external wrapping morphology reveal the polymorphic nature of amyloid fibrils. Biomacromolecules 12:1781–94 [Google Scholar]
  40. Wu C, Bowers M, Shea JE. 40.  2010. Molecular structures of quiescently grown and brain-derived polymorphic fibrils of the Alzheimer amyloid Aβ9-40 peptide: a comparison to agitated fibrils. PLoS Comput. Biol. 6:e1000693 [Google Scholar]
  41. Buchete N, Hummer G. 41.  2007. Structure and dynamics of parallel β-sheets, hydrophobic core, and loops in Alzheimer's Aβ fibrils. Biophys. J. 92:3032–39 [Google Scholar]
  42. Lemkul JA, Bevan DR. 42.  2012. The role of molecular simulations in the development of inhibitors of amyloid β-peptide aggregation for the treatment of Alzheimer's disease. ACS Chem. Neurosci. 3:845–56 [Google Scholar]
  43. Ngo ST, Li MS. 43.  2012. Curcumin binds to Aβ1-40 peptides and fibrils stronger than ibuprofen and naproxen. J. Phys. Chem. B 116:10165–75 [Google Scholar]
  44. Wu C, Scott J, Shea JE. 44.  2012. Binding of Congo Red to amyloid protofibrils of the Alzheimer Aβ9-40 peptide probed by molecular dynamics simulations. Biophys. J. 103:550–57 [Google Scholar]
  45. Takeda T, Chang WE, Raman EP, Klimov DK. 45.  2010. Binding of nonsteroidal anti-inflammatory drugs to Aβ fibril. Proteins 78:2849–60 [Google Scholar]
  46. Schor M, Vreede J, Bolhuis PG. 46.  2012. Elucidating the locking mechanism of peptides onto growing amyloid fibrils through transition path sampling. Biophys. J. 103:1296–304 [Google Scholar]
  47. Esler W, Stimson E, Jennings J, Vinters H, Ghilardi J. 47.  et al. 2000. Alzheimer's disease amyloid propagation by a template-dependent dock-lock mechanism. Biochemistry 39:6288–95 [Google Scholar]
  48. Xi W, Li W, Wang W. 48.  2012. Template induced conformational change of amyloid-β monomer. J. Phys. Chem. B 116:7398–405 [Google Scholar]
  49. Morriss-Andrews A, Brown FLH, Shea JE. 49.  2014. A coarse-grained model for peptide aggregation on a membrane surface. J. Phys. Chem. B 118:8420–32 [Google Scholar]
  50. Simunovic M, Srivastava A, Voth GA. 50.  2013. Linear aggregation of proteins on the membrane as a prelude to membrane remodeling. Proc. Natl. Acad. Sci. USA 110:20396–401 [Google Scholar]
  51. Li H, Gorfe AA. 51.  2013. Aggregation of lipid-anchored full-length H-Ras in lipid bilayers: simulations with the MARTINI force field. PLoS ONE 8:e71018 [Google Scholar]
  52. Pannuzzo M, Milardi D, Raudino A, Karttunen M, La Rosa C. 52.  2013. Analytical model and multiscale simulations of Aβ peptide aggregation in lipid membranes: towards a unifying description of conformational transitions, oligomerization and membrane damage. Phys. Chem. Chem. Phys. 15:8940–51 [Google Scholar]
  53. Santo KP, Berkowitz ML. 53.  2012. Difference between magainin-2 and melittin assemblies in phosphatidylcholine bilayers: results from coarse-grained simulations. J. Phys. Chem. B 116:3021–30 [Google Scholar]
  54. Parton DL, Klingelhoefer JW, Sansom MS. 54.  2011. Aggregation of model membrane proteins, modulated by hydrophobic mismatch, membrane curvature, and protein class. Biophys. J. 101:691–99 [Google Scholar]
  55. Simunovic M, Mim C, Marlovits TC, Resch G, Unger VM, Voth GA. 55.  2013. Protein-mediated transformation of lipid vesicles into tubular networks. Biophys. J. 105:711–19 [Google Scholar]
  56. Hung A, Yarovsky I. 56.  2011. Inhibition of peptide aggregation by lipids: insights from coarse-grained molecular simulations. J. Mol. Graph. Model. 29:597–607 [Google Scholar]
  57. Keller A, Fritzsche M, Yu YP, Liu Q, Li YM. 57.  et al. 2011. Influence of hydrophobicity on the surface-catalyzed assembly of the islet amyloid polypeptide. ACS Nano 5:2770–78 [Google Scholar]
  58. Zhu M, Souillac P, Ionescu-Zanetti C, Carter S, Fink A. 58.  2002. Surface-catalyzed amyloid fibril formation. J. Biol. Chem. 277:50914–22 [Google Scholar]
  59. Kowalewski T, Holtzman DM. 59.  1999. In situ atomic force microscopy study of Alzheimer's β-amyloid peptide on different substrates: new insights into mechanism of β-sheet formation. Proc. Natl. Acad. Sci. USA 96:3688–93 [Google Scholar]
  60. Green JD, Goldsbury C, Kistler J, Cooper GJS, Aebi U. 60.  2004. Human amylin oligomer growth and fibril elongation define two distinct phases in amyloid formation. J. Biol. Chem. 279:12206–12 [Google Scholar]
  61. Losic D, Martin LL, Aguilar MI, Small DH. 61.  2006. β-amyloid fibril formation is promoted by step edges of highly oriented pyrolytic graphite. Peptide Sci. 84:519–26 [Google Scholar]
  62. Giacomelli CE, Norde W. 62.  2003. Influence of hydrophobic Teflon particles on the structure of amyloid β-peptide. Biomacromolecules 4:1719–26 [Google Scholar]
  63. Ha C, Park CB. 63.  2006. Ex situ atomic force microscopy analysis of β-amyloid self-assembly and deposition on a synthetic template. Langmuir 22:6977–85 [Google Scholar]
  64. O'Brien EP, Ziv G, Haran G, Brooks BR, Thirumalai D. 64.  2008. Effects of denaturants and osmolytes on proteins are accurately predicted by the molecular transfer model. Proc. Natl. Acad. Sci. USA 105:13403–8 [Google Scholar]
  65. Wu C, Shea JE. 65.  2011. Coarse-grained models for protein aggregation. Curr. Opin. Struct. Biol. 21:209–20 [Google Scholar]
  66. Morriss-Andrews A, Shea JE. 66.  2014. Simulations of protein aggregation: insights from atomistic and coarse-grained models. J. Phys. Chem. Lett. 5:1899–908 [Google Scholar]
  67. Barz B, Urbanc B. 67.  2014. Minimal model of self-assembly: emergence of diversity and complexity. J. Phys. Chem. B 118:3761–70 [Google Scholar]
  68. Auer S, Meersman F, Dobson C, Vendruscolo M. 68.  2008. A generic mechanism of emergence of amyloid protofilaments from disordered oligomeric aggregates. PLoS Comput. Biol. 4:e1000222 [Google Scholar]
  69. Zhang J, Muthukumar M. 69.  2009. Simulations of nucleation and elongation of amyloid fibrils. J. Chem. Phys. 130:035102 [Google Scholar]
  70. Paparcone R, Cranford SW, Buehler MJ. 70.  2011. Self-folding and aggregation of amyloid nanofibrils. Nanoscale 3:1748–55 [Google Scholar]
  71. Li M, Co N, Reddy G, Hu C, Straub J, Thirumalai D. 71.  2010. Factors governing fibrillogenesis of polypeptide chains revealed by lattice models. Phys. Rev. Lett. 105:218101 [Google Scholar]
  72. Ni R, Abeln S, Schor M, Stuart MAC, Bolhuis PG. 72.  2013. Interplay between folding and assembly of fibril-forming polypeptides. Phys. Rev. Lett. 111:058101 [Google Scholar]
  73. Magno A, Pellarin R, Caflisch A. 73.  2012. Mechanisms and kinetics of amyloid aggregation investigated by a phenomenological coarse-grained model. Computational Modeling of Biological Systems: From Molecules to Pathways NV Dokholyan 191–214 New York: Wiley [Google Scholar]
  74. Pellarin R, Caflisch A. 74.  2006. Interpreting the aggregation kinetics of amyloid peptides. J. Mol. Biol. 360:882–92 [Google Scholar]
  75. Morriss-Andrews A, Bellesia G, Shea JE. 75.  2012. β-sheet propensity controls the kinetic pathways and morphologies of seeded peptide aggregation. J. Chem. Phys. 137:145104 [Google Scholar]
  76. Pellarin R, Guarnera E, Caflisch A. 76.  2007. Pathways and intermediates of amyloid fibril formation. J. Mol. Biol. 374:917–24 [Google Scholar]
  77. Pellarin R, Schuetz P, Guarnera E, Caflisch A. 77.  2010. Amyloid fibril polymorphism is under kinetic control. J. Am. Chem. Soc. 132:14960–70 [Google Scholar]
  78. Friedman R, Pellarin R, Caflisch A. 78.  2009. Amyloid aggregation on lipid bilayers and its impact on membrane permeability. J. Mol. Biol. 387:407–15 [Google Scholar]
  79. Bellesia G, Shea JE. 79.  2009. Effect of β-sheet propensity on peptide aggregation. J. Chem. Phys. 130:145103 [Google Scholar]
  80. Bellesia G, Shea JE. 80.  2009. Diversity of kinetic pathways in amyloid fibril formation. J. Chem. Phys. 131:111102 [Google Scholar]
  81. Morriss-Andrews A, Shea JE. 81.  2012. Kinetic pathways to peptide aggregation on surfaces: the effects of β-sheet propensity and surface attraction. J. Chem. Phys. 136:065103 [Google Scholar]
  82. Brannigan G, Philips P, Brown F. 82.  2005. Flexible lipid bilayers in implicit solvent. Phys. Rev. E 72:011915 [Google Scholar]
  83. Morriss-Andrews A, Bellesia G, Shea JE. 83.  2011. Effects of surface interactions on peptide aggregate morphology. J. Chem. Phys. 135:085102 [Google Scholar]
  84. Thota N, Luo Z, Hu Z, Jiang J. 84.  2013. Self-assembly of amphiphilic peptide (AF)6H5K15: coarse-grained molecular dynamics simulation. J. Phys. Chem. B 117:9690–98 [Google Scholar]
  85. Seo M, Rauscher S, Pomès R, Tieleman DP. 85.  2012. Improving internal peptide dynamics in the coarse-grained MARTINI model: toward large-scale simulations of amyloid- and elastin-like peptides. J. Chem. Theory Comput. 8:1774–85 [Google Scholar]
  86. Lee OS, Cho V, Schatz GC. 86.  2012. Modeling the self-assembly of peptide amphiphiles into fibers using coarse-grained molecular dynamics. Nano Lett. 12:4907–13 [Google Scholar]
  87. Guo C, Luo Y, Zhou R, Wei G. 87.  2012. Probing the self-assembly mechanism of diphenylalanine-based peptide nanovesicles and nanotubes. ACS Nano 6:3907–18 [Google Scholar]
  88. Sørensen J, Periole X, Skeby KK, Marrink SJ, Schiøtt B. 88.  2011. Protofibrillar assembly toward the formation of amyloid fibrils. J. Phys. Chem. Lett. 2:2385–90 [Google Scholar]
  89. Frederix PW, Ulijn RV, Hunt NT, Tuttle T. 89.  2011. Virtual screening for dipeptide aggregation: toward predictive tools for peptide self-assembly. J. Phys. Chem. Lett. 2:2380–84 [Google Scholar]
  90. Marrink SJ, Tieleman DP. 90.  2013. Perspective on the Martini model. Chem. Soc. Rev. 42:6801–22 [Google Scholar]
  91. Phelps EM, Hall CK. 91.  2012. Structural transitions and oligomerization along polyalanine fibril formation pathways from computer simulations. Proteins 80:1582–97 [Google Scholar]
  92. Cheon M, Chang I, Hall CK. 92.  2012. Influence of temperature on formation of perfect tau fragment fibrils using PRIME20/DMD simulations. Protein Sci. 21:1514–27 [Google Scholar]
  93. Wagoner VA, Cheon M, Chang I, Hall CK. 93.  2012. Fibrillization propensity for short designed hexapeptides predicted by computer simulation. J. Mol. Biol. 416:598–609 [Google Scholar]
  94. Wagoner VA, Cheon M, Chang I, Hall CK. 94.  2011. Computer simulation study of amyloid fibril formation by palindromic sequences in prion peptides. Proteins 79:2132–45 [Google Scholar]
  95. Cheon M, Chang I, Hall CK. 95.  2011. Spontaneous formation of twisted Aβ16-22 fibrils in large-scale molecular-dynamics simulations. Biophys. J. 101:2493–501 [Google Scholar]
  96. Wagoner VA, Cheon M, Chang I, Hall CK. 96.  2014. Impact of sequence on the molecular assembly of short amyloid peptides. Proteins 82:1469–83 [Google Scholar]
  97. Sawaya MR, Sambashivan S, Nelson R, Ivanova MI, Sievers SA. 97.  et al. 2007. Atomic structures of amyloid cross-β spines reveal varied steric zippers. Nature 447:453–57 [Google Scholar]
  98. de la Paz ML, Serrano L. 98.  2004. Sequence determinants of amyloid fibril formation. Proc. Natl. Acad. Sci. USA 101:87–92 [Google Scholar]
  99. de la Paz ML, Goldie K, Zurdo J, Lacroix E, Dobson CM. 99.  et al. 2002. De novo designed peptide-based amyloid fibrils. Proc. Natl. Acad. Sci. USA 99:16052–57 [Google Scholar]
  100. Mehta AK, Lu K, Childers WS, Liang Y, Dublin SN. 100.  et al. 2008. Facial symmetry in protein self-assembly. J. Am. Chem. Soc. 130:9829–35 [Google Scholar]
  101. Balbach J, Ishii Y, Antzutkin O, Leapman R, Rizzo N. 101.  et al. 2000. Amyloid fibril formation by Aβ16-22, a seven-residue fragment of the Alzheimer's β-amyloid peptide, and structural characterization by solid state NMR. Biochemistry 39:13748–59 [Google Scholar]
  102. Tjernberg LO, Näslund J, Lindqvist F, Johansson J, Karlström AR. 102.  et al. 1996. Arrest of β-amyloid fibril formation by a pentapeptide ligand. J. Biol. Chem. 271:8545–48 [Google Scholar]
  103. Ricchiuto P, Brukhno AV, Auer S. 103.  2012. Protein aggregation: kinetics versus thermodynamics. J. Phys. Chem. B 116:5384–90 [Google Scholar]
  104. Hoang TX, Trovato A, Seno F, Banavar JR, Maritan A. 104.  2004. Geometry and symmetry presculpt the free-energy landscape of proteins. Proc. Natl. Acad. Sci. USA 101:7960–64 [Google Scholar]
  105. Meral D, Urbanc B. 105.  2013. Discrete molecular dynamics study of oligomer formation by N-terminally truncated amyloid β-protein. J. Mol. Biol. 425:2260–75 [Google Scholar]
  106. Urbanc B, Betnel M, Cruz L, Li H, Fradinger E. 106.  et al. 2011. Structural basis for Aβ C-terminal fragments: discrete molecular dynamics study. J. Mol. Biol. 410:316–28 [Google Scholar]
  107. Urbanc B, Betnel M, Cruz L, Bitan G, Teplow D. 107.  2010. Elucidation of amyloid β-protein oligomerization mechanisms: discrete molecular dynamics study. J. Am. Chem. Soc. 132:4266–80 [Google Scholar]
  108. Peng S, Ding F, Urbanc B, Buldyrev S, Cruz L. 108.  et al. 2004. Discrete molecular dynamics simulations of peptide aggregation. Phys. Rev. E 69:041908 [Google Scholar]
  109. Ding F, Furukawa Y, Nukina N, Dokholyan NV. 109.  2012. Local unfolding of Cu, Zn superoxide dismutase monomer determines the morphology of fibrillar aggregates. J. Mol. Biol. 421:548–60 [Google Scholar]
  110. Redler RL, Wilcox KC, Proctor EA, Fee L, Caplow M, Dokholyan NV. 110.  2011. Glutathionylation at Cys-111 induces dissociation of wild type and FALS mutant SOD1 dimers. Biochemistry 50:7057–66 [Google Scholar]
  111. Côté S, Laghaei R, Derreumaux P, Mousseau N. 111.  2012. Distinct dimerization for various alloforms of the amyloid-β protein: Aβ1-40, Aβ1-42, and Aβ1-40 (D23N). J. Phys. Chem. B 116:4043–55 [Google Scholar]
  112. Spill YG, Pasquali S, Derreumaux P. 112.  2011. Impact of thermostats on folding and aggregation properties of peptides using the optimized potential for efficient structure prediction coarse-grained model. J. Chem. Theory Comput. 7:1502–10 [Google Scholar]
  113. Chebaro Y, Jiang P, Zang T, Mu Y, Nguyen PH. 113.  et al. 2012. Structures of Aβ17–42 trimers in isolation and with five small-molecule drugs using a hierarchical computational procedure. J. Phys. Chem. B 116:8412–22 [Google Scholar]
  114. Lu Y, Wei G, Derreumaux P. 114.  2012. Structural, thermodynamical, and dynamical properties of oligomers formed by the amyloid NNQQ peptide: insights from coarse-grained simulations. J. Chem. Phys. 137:025101 [Google Scholar]
  115. Nasica-Labouze J, Meli M, Derreumaux P, Colombo G, Mousseau N. 115.  2011. A multiscale approach to characterize the early aggregation steps of the amyloid-forming peptide GNNQQNY from the yeast prion Sup-35. PLoS Comp. Biol. 7:e1002051 [Google Scholar]
  116. Nasica-Labouze J, Mousseau N. 116.  2012. Kinetics of amyloid aggregation: a study of the GNNQQNY prion sequence. PLoS Comput. Biol. 8:e1002782 [Google Scholar]
  117. Laio A, Parrinello M. 117.  2002. Escaping free-energy minima. Proc. Natl. Acad. Sci. USA 99:12562–66 [Google Scholar]
  118. Gronau G, Qin Z, Buehler MJ. 118.  2013. Effect of sodium chloride on the structure and stability of spider silk's N-terminal protein domain. Biomater. Sci. 1:276–84 [Google Scholar]
  119. Camilloni C, Schaal D, Schweimer K, Schwarzinger S, De Simone A. 119.  2012. Energy landscape of the prion protein helix 1 probed by metadynamics and NMR. Biophys. J. 102:158–67 [Google Scholar]
  120. Rossetti G, Cossio P, Laio A, Carloni P. 120.  2011. Conformations of the Huntingtin N-term in aqueous solution from atomistic simulations. FEBS Lett. 585:3086–89 [Google Scholar]
  121. Wang H, Barreyro L, Provasi D, Djemil I, Torres-Arancivia C. 121.  et al. 2011. Molecular determinants and thermodynamics of the amyloid precursor protein transmembrane domain implicated in Alzheimer's disease. J. Mol. Biol. 408:879–95 [Google Scholar]
  122. Torrie GM, Valleau JP. 122.  1977. Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J. Comput. Phys. 23:187–99 [Google Scholar]
  123. Rivera E, Straub J, Thirumalai D. 123.  2009. Sequence and crowding effects in the aggregation of a 10-residue fragment derived from islet amyloid polypeptide. Biophys. J. 96:4552–60 [Google Scholar]
  124. Davis C, Berkowitz M. 124.  2009. Interaction between amyloid-β (1–42) peptide and phospholipid bilayers: a molecular dynamics study. Biophys. J. 96:785–97 [Google Scholar]
  125. Sugita Y, Okamoto Y. 125.  1999. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314:141–51 [Google Scholar]
  126. Sugita Y, Kitao A, Okamoto Y. 126.  2000. Multidimensional replica-exchange method for free-energy calculations. J. Chem. Phys. 113:6042–51 [Google Scholar]
  127. Fukunishi H, Watanabe O, Takada S. 127.  2002. On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: application to protein structure prediction. J. Chem. Phys. 116:9058–67 [Google Scholar]
  128. Ostermeir K, Zacharias M. 128.  2013. Advanced replica-exchange sampling to study the flexibility and plasticity of peptides and proteins. Biochim. Biophys. Acta 1834:847–53 [Google Scholar]
  129. Kim J, Straub JE, Keyes T. 129.  2012. Replica exchange statistical temperature molecular dynamics algorithm. J. Phys. Chem. B 116:8646–53 [Google Scholar]
  130. Kim J, Straub JE, Keyes T. 130.  2006. Statistical-temperature Monte Carlo and molecular dynamics algorithms. Phys. Rev. Lett. 97:050601 [Google Scholar]
  131. Swendsen RH, Wang JS. 131.  1986. Replica Monte Carlo simulation of spin-glasses. Phys. Rev. Lett. 57:2607–9 [Google Scholar]
  132. Enciso M, Rey A. 132.  2012. Simple model for the simulation of peptide folding and aggregation with different sequences. J. Chem. Phys. 136:215103 [Google Scholar]
  133. Swope WC, Pitera JW, Suits F. 133.  2004. Describing protein folding kinetics by molecular dynamics simulations. 1. Theory. J. Phys. Chem. B 108:6571–81 [Google Scholar]
  134. Park S, Pande VS. 134.  2006. Validation of Markov state models using Shannon's entropy. J. Chem. Phys. 124:054118 [Google Scholar]
  135. Swope WC, Pitera JW, Suits F, Pitman M, Eleftheriou M. 135.  et al. 2004. Describing protein folding kinetics by molecular dynamics simulations. 2. Example applications to alanine dipeptide and a β-hairpin peptide. J. Phys. Chem. B 108:6582–94 [Google Scholar]
  136. Kelley NW, Vishal V, Krafft GA, Pande VS. 136.  2008. Simulating oligomerization at experimental concentrations and long timescales: a Markov state model approach. J. Chem. Phys. 129:214707 [Google Scholar]
  137. Zhou T, Caflisch A. 137.  2012. Free energy guided sampling. J. Chem. Theory Comput. 8:2134–40 [Google Scholar]
  138. Dellago C, Bolhuis PG, Csajka FS, Chandler D. 138.  1998. Transition path sampling and the calculation of rate constants. J. Chem. Phys. 108:1964–77 [Google Scholar]
  139. Bolhuis PG, Chandler D, Dellago C, Geissler PL. 139.  2002. Transition path sampling: throwing ropes over rough mountain passes, in the dark. Annu. Rev. Phys. Chem. 53:291–318 [Google Scholar]
  140. Peters B, Trout BL. 140.  2006. Obtaining reaction coordinates by likelihood maximization. J. Chem. Phys. 125:054108 [Google Scholar]
  141. Maragliano L, Fischer A, Vanden-Eijnden E, Ciccotti G. 141.  2006. String method in collective variables: minimum free energy paths and isocommittor surfaces. J. Chem. Phys. 125:024106 [Google Scholar]
  142. E W, Ren W, Vanden-Eijnden E. 142.  2002. String method for the study of rare events. Phys. Rev. B 66:052301 [Google Scholar]
  143. Cohen SI, Vendruscolo M, Dobson CM, Knowles TP. 143.  2012. From macroscopic measurements to microscopic mechanisms of protein aggregation. J. Mol. Biol. 421:160–71 [Google Scholar]
  144. Tirion MM. 144.  1996. Large amplitude elastic motions in proteins from a single-parameter, atomic analysis. Phys. Rev. Lett. 77:1905–8 [Google Scholar]
  145. Hinsen K. 145.  1998. Analysis of domain motions by approximate normal mode calculations. Proteins 33:417–29 [Google Scholar]
  146. Yoon G, Kwak J, Kim JI, Na S, Eom K. 146.  2011. Mechanical characterization of amyloid fibrils using coarse-grained normal mode analysis. Adv. Funct. Mater. 21:3454–63 [Google Scholar]
  147. Xu Z, Paparcone R, Buehler MJ. 147.  2010. Alzheimer's Aβ(1–40) amyloid fibrils feature size-dependent mechanical properties. Biophys. J. 98:2053–62 [Google Scholar]
  148. Shell MS. 148.  2008. The relative entropy is fundamental to multiscale and inverse thermodynamic problems. J. Chem. Phys. 129:144108 [Google Scholar]
  149. Izvekov S, Voth GA. 149.  2005. A multiscale coarse-graining method for biomolecular systems. J. Phys. Chem. B 109:2469–73 [Google Scholar]
  150. Wang Y, Voth GA. 150.  2010. Molecular dynamics simulations of polyglutamine aggregation using solvent-free multiscale coarse-grained models. J. Phys. Chem. B 114:8735–43 [Google Scholar]
  151. Reith D, Pütz M, Müller-Plathe F. 151.  2003. Deriving effective mesoscale potentials from atomistic simulations. J. Comput. Chem. 24:1624–36 [Google Scholar]
  152. Bezkorovaynaya O, Lukyanov A, Kremer K, Peter C. 152.  2012. Multiscale simulation of small peptides: consistent conformational sampling in atomistic and coarse-grained models. J. Comput. Chem. 33:937–49 [Google Scholar]
  153. Alder BJ, Wainwright T. 153.  1959. Studies in molecular dynamics. I. General method. J. Chem. Phys. 31:459–66 [Google Scholar]
  154. Izvekov S, Parrinello M, Burnham CJ, Voth GA. 154.  2004. Effective force fields for condensed phase systems from ab initio molecular dynamics simulation: a new method for force-matching. J. Chem. Phys. 120:10896–913 [Google Scholar]
  155. Larini L, Shea JE. 155.  2012. Coarse-grained modeling of simple molecules at different resolutions in the absence of good sampling. J. Phys. Chem. B 116:8337–49 [Google Scholar]
  156. Chiu CC, Singh S, de Pablo JJ. 156.  2013. Effect of proline mutations on the monomer conformations of amylin. Biophys. J. 105:1227–35 [Google Scholar]
  157. Singhal N, Snow CD, Pande VS. 157.  2004. Using path sampling to build better Markovian state models: predicting the folding rate and mechanism of a tryptophan zipper beta hairpin. J. Chem. Phys. 121:415–25 [Google Scholar]
  158. Prinz JH, Wu H, Sarich M, Keller B, Senne M. 158.  et al. 2011. Markov models of molecular kinetics: generation and validation. J. Chem. Phys. 134:174105 [Google Scholar]
  159. Elmer SP, Park S, Pande VS. 159.  2005. Foldamer dynamics expressed via Markov state models. II. State space decomposition. J. Chem. Phys. 123:114903 [Google Scholar]
  160. Dickson A, Brooks CL III. 160.  2014. WExplore: hierarchical exploration of high-dimensional spaces using the weighted ensemble algorithm. J. Phys. Chem. B 118:3532–42 [Google Scholar]
  161. Huber GA, Kim S. 161.  1996. Weighted-ensemble Brownian dynamics simulations for protein association reactions. Biophys. J. 70:97–110 [Google Scholar]
  162. Dickson A, Mustoe AM, Salmon L, Brooks CL III. 162.  2014. Efficient in silico exploration of RNA interhelical conformations using Euler angles and WExplore. Nucl. Acids Res. 42:12126–37 [Google Scholar]
/content/journals/10.1146/annurev-physchem-040513-103738
Loading
/content/journals/10.1146/annurev-physchem-040513-103738
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error