Skip to main content
SpringerLink
Log in

Computer Simulations Aimed at Exploring Protein Aggregation and Dissociation

  • Protocol
  • First Online:
Computer Simulations of Aggregation of Proteins and Peptides

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2340))

Abstract

Protein aggregation can lead to well-defined structures that are functional, but is also the cause of the death of neuron cells in many neurodegenerative diseases. The complexity of the molecular events involved in the aggregation kinetics of amyloid proteins and the transient and heterogeneous characters of all oligomers prevent high-resolution structural experiments. As a result, computer simulations have been used to determine the atomic structures of amyloid proteins at different association stages as well as to understand fibril dissociation. In this chapter, we first review the current computer simulation methods used for aggregation with some atomistic and coarse-grained results aimed at better characterizing the early formed oligomers and amyloid fibril formation. Then we present the applications of non-equilibrium molecular dynamics simulations to comprehend the dissociation of protein assemblies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 149.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dobson CM, Knowles TPJ, Vendruscolo M (2019) The amyloid phenomenon and its significance in biology and medicine. Cold Spring Harb Perspect Biol 12:a033878

    Google Scholar 

  2. Nasica-Labouze J, Nguyen PH, Sterpone F et al (2015) Amyloid beta protein and Alzheimer’s disease: when computer simulations complement experimental studies. Chem Rev 115:3518–3563

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Caldarulo E, Barducci A, Wüthrich K, Parrinello M (2017) Prion protein β2-α2 loop conformational landscape. Proc Natl Acad Sci U S A 114:9617–9622

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Cliffe R, Sang JC, Kundel F, Finley D, Klenerman D, Ye Y (2019) Filamentous aggregates are fragmented by the proteasome holoenzyme. Cell Rep 26:2140–2149.e3

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Deger JM, Gerson JE, Kayed R (2015) The interrelationship of proteasome impairment and oligomeric intermediates in neurodegeneration. Aging Cell 14:715–724

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Banks HT, Doumic M, Kruse C (2017) A numerical scheme for the early steps of nucleation-aggregation models. J Math Biol 74:259–287

    PubMed  Google Scholar 

  7. Dammers C, Schwarten M, Buell AK et al (2017) Pyroglutamate-modified Aβ(3- 42) affects aggregation kinetics of Aβ(1-42) by accelerating primary and secondary pathways. Chem Sci 8:4996

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Roche J, Shen Y, Lee JH et al (2016) Monomeric Aβ(1-40) and Aβ(1-42) peptides in solution adopt very similar Ramachandran map distributions that closely resemble random coil. Biochemistry 55:762–765

    CAS  PubMed  Google Scholar 

  9. Schwalbe M, Ozenne V, Bibow S, Jaremko M, Gajda M, Jensen MR, Biernat J, Becker S, Mandelkow E, Zweckstetter M, Blackledge M (2014) Predictive atomic resolution descriptions of intrinsically disordered hTau40 and alpha-synuclein in solution from NMR and small angle scattering. Structure 22:238–249

    CAS  PubMed  Google Scholar 

  10. Theillet FX, Binolki A, Bekei B, Martorana A, Rose HM, Stuiver M, Verzini S, Lorenz D, van Rossum M, Goldfarb D, Selenko P (2016) Structural disorder of alpha-synuclein persists in mammalian cells. Nature 530:45–50

    CAS  PubMed  Google Scholar 

  11. Nagel-Steger L, Owen MC, Strodel B (2016) An account of amyloid oligomers: facts and figures obtained from experiments and simulations. ChemBioChem 17:657–676

    CAS  PubMed  Google Scholar 

  12. Chen SW, Drakulic S, Deas E, Ouberai M, Aprile FA, Arranz R, Ness S, Roodveldt C, Guilliams T, De-Genst EJ et al (2015) Structural characterization of toxic oligomers that are kinetically trapped during alpha-synuclein fibril formation. Proc Natl Acad Sci U S A 112:E1994–E2003

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Kreutzer AG, Nowick JS (2018) Elucidating the structures of amyloid oligomers with macrocyclic beta-hairpin peptides: insights into Alzheimer’s disease and other amyloid diseases. Acc Chem Res 51:706–718

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Li Y, Zhao C, Luo F et al (2018) Amyloid fibril structure of α-synuclein determined by cryo-electron microscopy. Cell Res 28:897–903

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Fitzpatrick AWP, Falcon B, He S et al (2017) Cryo-EM structures of tau filaments from Alzheimer’s disease. Nature 547:185–190

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Selkoe DG, Hardy J (2016) The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med 8:595–608

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Owen MC, Gnutt D, Gao M et al (2019) Effects of in vivo conditions on amyloid aggregation. Chem Soc Rev 48:3946–3996

    CAS  PubMed  Google Scholar 

  18. Derreumaux P (2001) Generating ensemble averages for small proteins from extended conformations by Monte Carlo simulations. Phys Rev Lett 85:206–209

    Google Scholar 

  19. Levine ZA, Shea JE (2017) Simulations of disordered proteins and systems with conformational heterogeneity. Curr Opin Struct Biol 43:95–103

    CAS  PubMed  Google Scholar 

  20. Bradley P, Misura KM, Baker D (2005) Towards high-resolution de novo structure prediction for small proteins. Science 309:1868–1871

    CAS  PubMed  Google Scholar 

  21. Das R, Andre I, Shen Y et al (2009) Simultaneous prediction of protein folding and docking at high resolution. Proc Natl Acad Sci U S A 106:18978–18983

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Lendel C, Bjerring M, Dubnovitsky A et al (2014) A hexameric peptide barrel as building block of amyloid-β protofibrils. Angew Chem Int Ed Eng 53:12756–12760

    CAS  Google Scholar 

  23. Li DW, Mohanty S, Irbäck A et al (2008) Formation and growth of oligomers: a Monte Carlo study of an amyloid tau fragment. PLoS Comput Biol 4:e1000238

    PubMed  PubMed Central  Google Scholar 

  24. Irback A, Jonsson S, Linnemann N et al (2013) Aggregate geometry in amyloid fibril nucleation. Phys Rev Lett 110:058101

    PubMed  Google Scholar 

  25. Šarić A, Michaels TCT, Zaccone A et al (2016) Kinetics of spontaneous filament nucleation via oligomers: insights from theory and simulation. J Chem Phys 145:211926

    PubMed  Google Scholar 

  26. Bieler NS, Knowles TP, Frenkel D et al (2012) Connecting macroscopic observables and microscopic assembly events in amyloid formation using coarse grained simulations. PLoS Comput Biol 8:e1002692

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Tran TT, Nguyen PH, Derreumaux P (2016) Lattice model for amyloid peptides: OPEP force field parametrization and applications to the nucleus size of Alzheimer’s peptides. J Chem Phys 144:205103

    PubMed  Google Scholar 

  28. Sterpone F, Doutreligne S, Tran TT et al (2018) Multi-scale simulations of biological systems using the OPEP coarse-grained model. Biochem Biophys Res Commun 498:296–304

    CAS  PubMed  Google Scholar 

  29. Li MS, Klimov DK, Straub JE et al (2008) Probing the mechanisms of fibril formation using lattice models. J Chem Phys 129:175101

    PubMed  PubMed Central  Google Scholar 

  30. Co NT, Li MS (2012) New method for determining size of critical nucleus of fibril formation of polypeptide chains. J Chem Phys 137:095101

    PubMed  Google Scholar 

  31. Abeln S, Vendruscolo M, Dobson C et al (2014) A simple lattice model that captures protein folding, aggregation and amyloid formation. PloS ONE 9:e85185

    PubMed  PubMed Central  Google Scholar 

  32. Wei GH, Derreumaux P, Normand M (2003) Sampling the complex energy landscape of a simple β-hairpin. J. Chem Phys 119:6403–6406

    CAS  Google Scholar 

  33. Sterpone F, Melchionna S, Tuffery P et al (2014) The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems. Chem Soc Rev 43:4871–4893

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Nath A, Sammalkorpi M, DeWitt DC, Trexler AJ, Elbaum-Garfinkle S, O’Hern CS, Rhoades E (2012) The conformation ensemble of alpha-synuclein and tau: combining single-molecule FRET and simulations. Biophys J 103:1940–1949

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Baul U, Chakraborty D, Mugnai ML, Straub JE, Thirumalai D (2019) Sequence effects on size, shape and structural heterogeneity in intrinsically disordered proteins. J Phys Chem B 123:3462–3474

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Santini S, Mousseau N, Derreumaux P (2004) In silico assembly of Alzheimer’s Abeta16-22 peptide into beta-sheets. J Am Chem Soc 126:11509–11516

    CAS  PubMed  Google Scholar 

  37. Melquiond A, Mousseau N, Derreumaux P (2006) Structures of soluble amyloid oligomers from computer simulations. Proteins 65:180–191

    CAS  PubMed  Google Scholar 

  38. Melquiond A, Boucher G, Mousseau N et al (2005) Following the aggregation of amyloid-forming peptides by computer simulations. J Chem Phys 122:174904

    PubMed  Google Scholar 

  39. Chen W, Mousseau N, Derreumaux P (2006) The conformations of the amyloid-beta (21-30) fragment can be described by three families in solution. J Chem Phys 125:084911

    PubMed  Google Scholar 

  40. Röder K, Wales DJ (2018) Energy landscapes for the aggregation of Aβ17-42. J Am Chem Soc 140:4018–4027

    PubMed  Google Scholar 

  41. Huang J, Rauscher S, Nawrocki G, Ran T et al (2017) CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods 14:71–73

    CAS  PubMed  Google Scholar 

  42. Robustelli P, Piana S, Shaw DE (2018) Developing a molecular dynamics force field for both folded and disordered protein states. Proc Natl Acad Sci U S A 115:E4758–E4766

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Man VH, He X, Derreumaux P et al (2019) Effects of all-atom molecular mechanics force fields on amyloid peptide assembly: the case of Aβ16-22 dimer. J Chem Theory Comput 15:1440–1452

    PubMed  PubMed Central  Google Scholar 

  44. Barz B, Liao Q, Strodel B (2018) Pathways of amyloid-β aggregation depend on oligomer shape. J Am Chem Soc 140:319–327

    CAS  PubMed  Google Scholar 

  45. Viet MH, Nguyen PH, Derreumaux P et al (2014) Effect of the English familial disease mutation (H6R) on the monomers and dimers of Aβ40 and Aβ42. ACS Chem Neurosci 5:646–657

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Viet MH, Nguyen PH, Ngo ST et al (2013) Effect of the Tottori familial disease mutation (D7N) on the monomers and dimers of Aβ40 and Aβ42. ACS Chem Neurosci 4:1446–1457

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Jang H, Arce FT, Ramachandran S et al (2014) Disordered amyloidogenic peptides may insert into the membrane and assemble into common cyclic structural motifs. Chem Soc Rev 43:6750–6764

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Zhang M, Ren B, Chen H et al (2017) Molecular simulations of amyloid structures, toxicity and inhibition. Isr J Chem 57:586–601

    CAS  Google Scholar 

  49. Sahoo A, Matysiak S (2019) Computational insights into lipid assisted peptide misfolding and aggregation in neurodegeneration. Phys Chem Chem Phys 21:22679–22694

    CAS  PubMed  Google Scholar 

  50. Lindorff-Larsen K, Maragakis P, Piana S et al (2016) Picosecond to millisecond structural dynamics in human ubiquitin. J Phys Chem B 120:8313–8820

    CAS  PubMed  Google Scholar 

  51. Pan AC, Jacobson D, Yatsenko K et al (2019) Atomic-level characterization of protein-protein association. Proc Natl Acad Sci U S A 116:4244–4249

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Xu L, Chen Y, Wang X (2014) Assembly of amyloid β peptides in the presence of fibril seeds: one-pot coarse-grained molecular dynamics simulations. J Phys Chem B 118:9238–9246

    CAS  PubMed  Google Scholar 

  53. Pellarin R, Caflisch A (2006) Interpreting the aggregation kinetics of amyloid peptides. J Mol Biol 360:882–892

    CAS  PubMed  Google Scholar 

  54. Bellesia G, Shea J-E (2009) Effect of β-sheet propensity on peptide aggregation. J Chem Phys 130:145103

    PubMed  Google Scholar 

  55. Song W, Wei G, Mousseau N et al (2008) Self-assembly of the beta2-microglobulin NHVTLSQ peptide using a coarse-grained protein model reveals a beta-barrel species. J Phys Chem B 112:4410–4418

    CAS  PubMed  Google Scholar 

  56. Lu Y, Derreumaux P, Guo Z et al (2009) Thermodynamics and dynamics of amyloid peptide oligomerization are sequence dependent. Proteins 75:954–963

    CAS  PubMed  Google Scholar 

  57. Rojas AV, Maisuradze GG, Scheraga HA (2018) Dependence of the formation of tau and Aβ peptide mixed aggregates on the secondary structure of the N-terminal region of Aβ. J Phys Chem B 122:7049–7056

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Ge X, Sun Y, Ding F (2018) Structures and dynamics of β-barrel oligomer intermediates of amyloid-beta16-22 aggregation. Biochim Biophys Acta Biomembr 1860:1687–1697

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Bunce SJ, Wang Y, Stewart KL et al (2019) Molecular insights into the surface-catalyzed secondary nucleation of amyloid-β40 (Aβ40) by the peptide fragment Aβ16-22. Sci Adv 5:eaav8216

    PubMed  PubMed Central  Google Scholar 

  60. Urbanc B, Betnel M, Cruz L et al (2010) Elucidation of amyloid β-protein oligomerization mechanisms: discrete molecular dynamics study. J Am Chem Soc 132:4266–4280

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Popov KI, Makepeace KAT, Petrotchenko EV, Dokholyan NV, Borchers CH (2019) Insight into the structure of the unstructured tau protein. Structure 27:1710–1715.e4

    CAS  PubMed  Google Scholar 

  62. Brodie NI, Popov KI, Petrotchenko EV, Dokholyan NV, Borchers CH (2019) Conformational ensemble of native alpha-synuclein in solution as determined by short-distance crosslinking constraint-guided discrete molecular dynamics simulations. PLoS Comput Biol 15(3):e1006859

    PubMed  PubMed Central  Google Scholar 

  63. Chiricotto M, Melchionna S, Derreumaux P et al (2019) Multiscale aggregation of the amyloid Aβ16–22 peptide: from disordered coagulation and lateral branching to Amorphous prefibrils. J Phys Chem Lett 10:1594–1599

    CAS  PubMed  Google Scholar 

  64. Brandner A, Timr S, Melchionna S et al (2019) Modelling lipid systems in fluid with Lattice Boltzmann Molecular Dynamics simulations and hydrodynamics. Sci Rep 9:16450

    Google Scholar 

  65. Granata D, Baftizadeh F, Habchi J et al (2015) The inverted free energy landscape of an intrinsically disordered peptide by simulations and experiments. Sci Rep 5:15449

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Baftizadeh F, Biarnes X, Pietrucci F et al (2012) Multidimensional view of amyloid fibril nucleation in atomistic detail. J Am Chem Soc 134:3886–3894

    CAS  PubMed  Google Scholar 

  67. Noé F, Olsson S, Kohler J, Wu H (2019) Boltzmann generators: sampling equilibrium states of many-body systems with deep learning. Science 365:eaaw1147

    PubMed  Google Scholar 

  68. Tarus B, Tran TT, Nasica-Labouze J et al (2015) Structures of the Alzheimer’s wild-type Aβ1-40 dimer from atomistic simulations. J Phys Chem B 119:10478–10487

    CAS  PubMed  Google Scholar 

  69. Nguyen PH, Sterpone F, Campanera JM et al (2016) Impact of the A2V mutation on the heterozygous and homozygous Aβ1-40 dimer structures from atomistic simulations. ACS Chem Neurosci 7:823–832

    CAS  PubMed  Google Scholar 

  70. Man VH, Nguyen PH, Derreumaux P (2017) High-resolution structures of the amyloid-β 1-42 dimers from the comparison of four atomistic force fields. J Phys Chem B 121:5977–5987

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Nguyen PH, Tarus B, Derreumaux P (2014) Familial Alzheimer A2V mutation reduces the intrinsic disorder and completely changes the free energy landscape of the Aβ1-28 monomer. J Phys Chem B 118:501–510

    CAS  PubMed  Google Scholar 

  72. Ngo ST, Nguyen PH, Derreumaux P (2020) Stability of Aβ11-40 trimers with parallel and antiparallel β-sheet organizations in a membrane-mimicking environment by replica exchange molecular dynamics simulation. J Phys Chem B 124:617–626

    CAS  PubMed  Google Scholar 

  73. Hung HM, Nguyen MT, Tran P-T, Truong K, Chapman J, Anh LHQ, Derreumaux P, Vu VV, Ngo ST (2020) Impact of the astaxanthin, betanin and EGCG compounds on small oligomers of the amyloid Aβ40 peptide. J Chem Inf Model 60:1399–1408

    Google Scholar 

  74. Zhang T, Xu W, Mu Y et al (2014) Atomic and dynamic insights into the beneficial effect of the 1,4-naphthoquinon-2-yl-L-tryptophan inhibitor on Alzheimer’s Aβ1-42 dimer in terms of aggregation and toxicity. ACS Chem Neurosci 5:148–159

    PubMed  Google Scholar 

  75. Nguyen P, Derreumaux P (2014) Understanding amyloid fibril nucleation and aβ oligomer/drug interactions from computer simulations. Acc Chem Res 47:603–611

    CAS  PubMed  Google Scholar 

  76. Zhang T, Zhang J, Derreumaux P et al (2013) Molecular mechanism of the inhibition of EGCG on the Alzheimer Aβ(1-42) dimer. J Phys Chem B 117:3993–4002

    CAS  PubMed  Google Scholar 

  77. Berthoumieu O, Nguyen PH, Castillo-Frias MP et al (2015) Combined experimental and simulation studies suggest a revised mode of action of the anti-Alzheimer disease drug NQ-Trp. Chemistry 21:12657–12666

    CAS  PubMed  Google Scholar 

  78. Jin Y, Sun Y, Chen Y, Lei J et al (2019) Molecular dynamics simulations reveal the mechanism of graphene oxide nanosheet inhibition of Aβ1-42 peptide aggregation. Phys Chem Chem Phys 21:10981–10991

    CAS  PubMed  Google Scholar 

  79. Fu Z, Luo Y, Derreumaux P et al (2009) Induced beta-barrel formation of the Alzheimer’s Abeta25-35 oligomers on carbon nanotube surfaces: implication for amyloid fibril inhibition. Biophys J 97:1795–1803

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Li H, Luo Y, Derreumaux P et al (2011) Carbon nanotube inhibits the formation of β-sheet-rich oligomers of the Alzheimer’s amyloid-β(16-22) peptide. Biophys J 101:2267–2276

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Nguyen PH, Campanera JM, Ngo ST et al (2019) Tetrameric Aβ40 and Aβ42 β-barrel structures by extensive atomistic simulations. I. In a bilayer mimicking a neuronal Membrane. J Phys Chem B 123:3643–3648

    CAS  PubMed  Google Scholar 

  82. Nguyen PH, Campanera JM, Ngo ST et al (2019) Tetrameric Aβ40 and Aβ42 β-barrel structures by extensive atomistic simulations. II. In aqueous solution. J Phys Chem B 123:6750–6756

    CAS  PubMed  Google Scholar 

  83. Ngo ST, Nguyen PH, Derreumaux P (2020) Impact of A2T and D23N mutations on tetrameric Aβ42 barrel within a dipalmitoylphosphatidylcholine lipid bilayer membrane by replica exchange molecular dynamics. J Phys Chem B 124:1175–1182

    CAS  PubMed  Google Scholar 

  84. Tuffery P, Derreumaux P (2012) Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches. J R Soc Interface 9:20–33

    CAS  PubMed  Google Scholar 

  85. Nasica-Labouze J, Meli M, Derreumaux P et al (2011) A multiscale approach to characterize the early aggregation steps of the amyloid-forming peptide GNNQQNY from the yeast prion sup-35. PLoS Comput Biol 7:e1002051

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Smith AK, Lockhart C, Klimov DK (2016) Does replica exchange with solute tempering efficiently sample Aβ peptide conformational ensembles? J Chem Theory Comput 12:5201–5214

    CAS  PubMed  Google Scholar 

  87. Dominguez L, Foster L, Straub JE et al (2016) Impact of membrane lipid composition on the structure and stability of the transmembrane domain of amyloid precursor protein. Proc Natl Acad Sci U S A 113:E5281–E5287

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Nguyen HL, Krupa P, Hai NM et al (2019) Structure and physicochemical properties of the Aβ42 tetramer: multiscale molecular dynamics simulations. J Phys Chem B 123(34):7253–7269

    CAS  PubMed  Google Scholar 

  89. Chebaro Y, Jiang P, Zang T 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 116:8412–8422

    CAS  PubMed  Google Scholar 

  90. Cote Y, Delarue P, Scheraga HA, Senet P, Maisuradze GG (2018) From a highly disordered to a metastable state: uncovering insights of α-synuclein. ACS Chem Neurosci 9:1051–1065

    CAS  PubMed  Google Scholar 

  91. Marinari E, Parisi G (1992) Simulated tempering: a new Monte Carlo scheme. Europhys Lett 19:451–458

    CAS  Google Scholar 

  92. Irback A, Potthast F (1995) Studies of an off-lattice model for protein folding: sequence dependence and improved sampling at finite temperature. J Chem Phys 103:10298–10305

    Google Scholar 

  93. Hansmann UHE, Okamoto Y (1997) Numerical comparisons of three recently proposed algorithms in the protein folding problem. J Comput Chem 18:920–933

    CAS  Google Scholar 

  94. Park S, Pande VS (2007) Choosing weights for simulated tempering. Phys Rev E 76:017603

    Google Scholar 

  95. Mitsutake A, Okamoto Y (2000) Replica-exchange simulated tempering method for simulations of frustrated systems. Chem Phys Lett 332:131–138

    CAS  Google Scholar 

  96. Mitsutake A, Okamoto Y (2004) Replica-exchange extensions of simulated tempering method. J Chem Phys 121:2491–2504

    CAS  PubMed  Google Scholar 

  97. Mitsutake A, Okamoto Y (2009) Multidimensional generalized-ensemble algorithms for complex systems. J Chem Phys 130:214105

    PubMed  Google Scholar 

  98. Favrin G, Irback A, Mohanty S (2004) Oligomerization of amyloid Aβ16-22 peptides using hydrogen bonds and hydrophobicity forces. Biophys J 87:3657–3664

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Nguyen PH, Okamoto Y, Derreumaux P (2013) Simulated tempering with fast on-the-fly weights determination. J Chem Phys 138:061102

    PubMed  Google Scholar 

  100. Zhang T, Nguyen PH, Nassica-Labouze J et al (2015) Folding atomistic proteins in explicit solvent using simulated tempering. J Phys Chem B 119:6941–6951

    CAS  PubMed  Google Scholar 

  101. Nguyen PH, Li MS, Derreumaux P (2011) Effects of all-atom force fields on amyloid oligomerization: replica exchange molecular dynamics simulations of the Aβ(16-22) dimer and trimer. Phys Chem Chem Phys 13:9778–9788

    CAS  PubMed  Google Scholar 

  102. Viet MH, Derreumaux P, Nguyen PH (2015) Multiple atomistic force fields in a single advanced sampling simulation. J Chem Phys 143:021101

    Google Scholar 

  103. Doig AJ, Del Castillo-Frias MP, Berthoumieu O et al (2017) Why is research on amyloid-β failing to give new drugs for Alzheimer’s disease? ACS Chem Neurosci 8:1435–1437

    CAS  PubMed  Google Scholar 

  104. Smith JF, Knowles TP, Dobson CM et al (2006) Characterization of the nanoscale properties of individual amyloid fibrils. Proc Natl Acad Sci U S A 103:15806–15811

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Fitzpatrick AW, Vanacore GM, Zewail AH (2015) Nanomechanics and intermolecular forces of amyloid revealed by four-dimensional electron microscopy. Proc Natl Acad Sci U S A 112:3380–3385

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Reches M, Gazit E (2003) Casting metal nanowires within discrete self-assembled peptide nanotubes. Science 300:625–627

    CAS  PubMed  Google Scholar 

  107. Booth DR, Sunde M, Bellotti V et al (1997) Instability, unfolding and aggregation of human lysozyme variants underlying amyloid fibrillogenesis. Nature 385:787–793

    CAS  PubMed  Google Scholar 

  108. Waldauer SA, Hassan S, Paoli B et al (2012) Photocontrol of reversible amyloid formation with a minimal-design peptide. J Phys Chem B 116:8961

    CAS  PubMed  Google Scholar 

  109. Johny M, Vijayalakshmi K, Das A et al (2017) Modulating the Phe–Phe dipeptide aggregation landscape via covalent attachment of an azobenzene photoswitch. Chem Commun 53:9348

    CAS  Google Scholar 

  110. Deeg AA, Schrader TE, Kempter S et al (2011) Light-triggered aggregation and disassembly of amyloid-like structures. ChemPhysChem 12:559

    CAS  PubMed  Google Scholar 

  111. Measey TJ, Gai F (2012) Light-triggered disassembly of amyloid fibrils. Langmuir 28:34

    Google Scholar 

  112. Kawasaki T, Fujioka J, Imai T et al (2014) Mid-infrared free-electron laser tuned to the amide I band for converting insoluble amyloid-like protein fibrils into the soluble monomeric form. Lasers Med Sci 29:1701–1707

    PubMed  PubMed Central  Google Scholar 

  113. Kawasaki T, Fujioka J, Imai T et al (2012) Effect of mid-infrared free-electron laser irradiation on refolding of amyloid-like fibrils of lysozyme into native form. Protein J 31:710–716

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Kawasaki T, Imai T, Tsukiyama K (2014) Use of a mid-infrared free-electron laser (MIR-FEL) for dissociation of the amyloid fibril aggregates of a peptide. J Analyt Sci Methods Instrument 4:9–18

    Google Scholar 

  115. Kawasaki T, Yaji T, Imai T et al (2014) Synchrotron-infrared microscopy analysis of amyloid fibrils irradiated by mid-infrared free- electron laser. Am J Anal Chem 5:384–394

    CAS  Google Scholar 

  116. Leinenga G, Gotz J (2015) Scanning ultrasound removes amyloid-β and restores memory in an Alzheimer’s disease mouse model. Sci Transl Med 7:278ra33

    PubMed  Google Scholar 

  117. Chatani E, Lee YH, Yagi H et al (2009) Ultrasonication-dependent production and breakdown lead to minimum-sized amyloid fibrils. Proc Natl Acad Sci U S A 106:1119

    Google Scholar 

  118. Yagi H, Hasegawa K, Yoshimura Y et al (2009) Acceleration of the depolymerization of amyloid β fibrils by ultrasonication. Biochim Biophys Acta 1834:2480–2485

    Google Scholar 

  119. Viet MH, Derreumaux P, Li MS et al (2015) Picosecond dissociation of amyloid fibrils with infrared laser: a nonequilibrium simulation study. J Chem Phys 143:155101

    Google Scholar 

  120. Viet MH, Truong PM, Derreumaux P et al (2015) Picosecond melting of peptide nanotubes using an infrared laser: a nonequilibrium simulation study. Phys Chem Chem Phys 17:27275

    Google Scholar 

  121. Viet MH, Nguyen VO, Derreumaux P et al (2016) Picosecond infrared laser-induced all-atom nonequilibrium molecular dynamics simulation of dissociation of viruses. Phys Chem Chem Phys 18:11951

    Google Scholar 

  122. Domin D, Viet MH, Nguyen-Thi VO et al (2018) Breaking down cellulose fibrils with a mid-infrared laser. Cellulose 25:5553–5568

    CAS  Google Scholar 

  123. Botan V, Backus EHG, Pfister R et al (2007) Energy transport in peptide helix. Proc Natl Acad Sci U S A 104:12749

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Kawasaki T, Viet M, Sugimoto Y, Nobuyuki S, Yamamoto H, Tsukiyama K, Wang J, Derreumaux P, Nguyen P (2020) Infrared laser induced amyloid fibril dissociation: a joint experimental/theoretical study on the GNNQQNY peptide. J Phys Chem B 124:6266. https://doi.org/10.1021/acs.jpcb.0c05385

    Article  CAS  PubMed  Google Scholar 

  125. Derreumaux P, Vergoten G (1995) A new spectroscopic molecular mechanics force field. Parameters for proteins. J Chem Phys 102:8586

    CAS  Google Scholar 

  126. Okumura H, Itoh SG (2014) Amyloid fibril disruption by ultrasonic cavitation: nonequilibrium molecular dynamics simulations. J Am Chem Soc 136:10549–10552

    CAS  PubMed  Google Scholar 

  127. Man VH, Derreumaux P, Nguyen PH (2016) Nonequilibrium all-atom molecular dynamics simulation of the ultrasound induced bubble vibration and application to dissociate amyloid fibrils. J Chem Phys 145:174113

    Google Scholar 

  128. Man VH, Derreumaux P, Nguyen PH (2018) Rayleigh-Plesset equation of the bubble stable cavitation in water: a nonequilibrium all-atom molecular dynamics simulation study. J Chem Phys 148:094505

    PubMed Central  Google Scholar 

  129. Ilie IM, Caflisch A (2019) Simulation studies of amyloidogenic polypeptides and their aggregates. Chem Rev 119:6956–6993

    CAS  PubMed  Google Scholar 

  130. Nguyen PH, Sterpone F, Derreumaux P (2020) Aggregation of disease-related peptides. Prog Mol Biol Transl Sci 170:435–460

    PubMed  Google Scholar 

  131. Derreumaux P, Man VH, Wang J, Nguyen PH (2020) Tau R3-R4 domain dimer of the wild type and phosphorylated Ser356 sequences. I. In Solution by Atomistic Simulations. J Phys Chem B 124:2975–2983

    CAS  PubMed  Google Scholar 

  132. Cao Y, Tang X, Yuan M, Han W (2020) Computational studies of protein aggregation mediated by amyloid: fibril elongation and secondary nucleation. Prog Mol Biol Transl Sci 170:461–504

    CAS  PubMed  Google Scholar 

  133. Nam HB, Kouza M, Zung H, Li MS (2010) Relationship between population of the fibril-prone conformation in the monomeric state and oligomer formation times of peptides: insights from all-atom simulations. J Chem Phys 132:165104

    PubMed  Google Scholar 

  134. Chebaro Y, Pasquali S, Derreumaux P (2012) The coarse-grained OPEP force field for non-amyloid and amyloid proteins. J Phys Chem B 116:8741–8752

    CAS  PubMed  Google Scholar 

  135. Sterpone F, Nguyen PH, Kalimeri M et al (2013) Importance of the ion-pair interactions in the OPEP coarse-grained force field: parametrization and validation. J Chem Theory Comput 9:4574–4584

    CAS  PubMed  PubMed Central  Google Scholar 

  136. Zhang Y, Liu H, Yang S, Luo R, Chen H-F (2019) Well-balanced force field ff03CMAP for folded and disordered proteins. J Chem Theory Comput 15:6769–6780

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Shabane PS, Izadi S, Onufriev AV (2019) General purpose water model can improve atomistic simulations of intrinsically disordered proteins. J Chem Theory Comput 15:2620–2634

    CAS  PubMed  Google Scholar 

  138. Pietrek LM, Stelzl LS, Hummer G (2020) Hierarchical ensembles of intrinsically disordered proteins at atomic resolution in molecular dynamics simulations. J Chem Theory Comput 16:725–737

    PubMed  Google Scholar 

  139. Shen Y, Maupetit J, Derreumaux P, Tufféry P (2014) Improved PEP-FOLD approach for peptide and miniprotein structure prediction. J Chem Theory Comput 10:4745–4758

    CAS  PubMed  Google Scholar 

  140. Lu Y, Shi XF, Nguyen PH, Sterpone F, Salsbury FR Jr, Derreumaux P (2019) Amyloid-β(29-42) dimeric conformations in membranes rich in omega-3 and omega-6 polyunsaturated fatty acids. J Phys Chem B 123:2687–2696

    CAS  PubMed  Google Scholar 

  141. Sterpone F, Derreumaux P, Melchionna S (2018) Molecular mechanism of protein unfolding under shear: a lattice Boltzmann molecular dynamics study. J Phys Chem B 122:1573–1579

    CAS  PubMed  Google Scholar 

  142. Chiricotto M, Sterpone F, Derreumaux P, Melchionna S (2016) Multiscale simulation of molecular processes in cellular environments. Phil Trans A Math Phys Eng Sci 374:2080

    Google Scholar 

  143. Lu Y, Shi XF, Salsbury FR Jr, Derreumaux P (2018) Influence of electric field on the amyloid-β(29-42) peptides embedded in a membrane bilayer. J Chem Phys 148:045105

    PubMed  Google Scholar 

  144. Mioduszewski L, Cieplak M (2018) Disordered peptide chains in an alpha-C-based coarse-grained model. Phys Chem Chem Phys 20:19057–19070

    CAS  PubMed  Google Scholar 

  145. Mioduszewski L, Cieplak M (2020) Protein droplets in systems of disordered homopeptides and the amyloid glass phase. Phys Chem Chem Phys 22:15592–15599

    CAS  PubMed  Google Scholar 

  146. Mioduszewski L, Rozycki B, Cieplak M (2020) Pseudo-improper-dihedral model for intrinsically disordered proteins. J Chem Theor Comput 16:4726–4733

    CAS  Google Scholar 

  147. Nguyen PH, Derreumaux P (2020) Structures of the intrinsically Aβ, tau and α -synuclein proteins in aqueous solution from computer simulations. Biophys Chem 264:106421

    CAS  PubMed  Google Scholar 

  148. Doig AJ, Derreumaux P (2015) Inhibition of protein aggregation and amyloid formation by small molecules. Curr Opin Struct Biol 3:50–56

    Google Scholar 

  149. Mahul-Mellier AL, Burtscher J, Maharjan N, Weerens L, Croisier M, Kuttler F, Leleu M, Knott GW, Lashuel HA (2020) The process of Lewy body formation, rather than simply α-synuclein fibrillization, is one of the major drivers of neurodegeneration. Proc Natl Acad Sci U S A 117:4971–4982

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We acknowledge the support by the “Initiative d’Excellence” program from the French State (Grant “DYNAMO”, ANR-11-LABX-0011-01, and “CACSICE”, ANR-11-EQPX-0008).

Notes

The authors declare no competing financial interest.

Author information

Authors and Affiliations

  1. Laboratoire de Biochimie Théorique, UPR 9080, CNRS, Université de Paris, Paris, France

    Phuong H. Nguyen & Philippe Derreumaux

  2. Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, Paris, France

    Phuong H. Nguyen & Philippe Derreumaux

Authors
  1. Phuong H. Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philippe Derreumaux
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Philippe Derreumaux .

Editor information

Editors and Affiliations

  1. Division of Theoretical Physics, Polish Academy of Sciences, Warsaw, Poland

    Mai Suan Li

  2. Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, USA

    Andrzej Kloczkowski

  3. Instutute of Physics, Polish Academy of Sciences, Warsaw, Poland

    Marek Cieplak

  4. Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, USA

    Maksim Kouza

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Nguyen, P.H., Derreumaux, P. (2022). Computer Simulations Aimed at Exploring Protein Aggregation and Dissociation. In: Li, M.S., Kloczkowski, A., Cieplak, M., Kouza, M. (eds) Computer Simulations of Aggregation of Proteins and Peptides . Methods in Molecular Biology, vol 2340. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1546-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1546-1_9

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1545-4

  • Online ISBN: 978-1-0716-1546-1

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation