Skip to main content
SpringerLink
Log in
Book cover

Tau Protein pp 33–59Cite as

Molecular Dynamics Simulation of Tau Peptides for the Investigation of Conformational Changes Induced by Specific Phosphorylation Patterns

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1523))

Abstract

The Tau protein plays an important role due to its biomolecular interactions in neurodegenerative diseases. The lack of stable structure and various posttranslational modifications such as phosphorylation at various sites in the Tau protein pose a challenge for many experimental methods that are traditionally used to study protein folding and aggregation. Atomistic molecular dynamics (MD) simulations can help around deciphering relationship between phosphorylation and various intermediate and stable conformations of the Tau protein which occur on longer timescales. This chapter outlines protocols for the preparation, execution, and analysis of all-atom MD simulations of a 21-amino acid-long phosphorylated Tau peptide with the aim of generating biologically relevant structural and dynamic information. The simulations are done in explicit solvent and starting from nearly extended configurations of the peptide. The scaled MD method implemented in AMBER14 was chosen to achieve enhanced conformational sampling in addition to a conventional MD approach, thereby allowing the characterization of folding for such an intrinsically disordered peptide at 293 K. Emphasis is placed on the analysis of the simulation trajectories to establish correlations with NMR data (i.e., chemical shifts and NOEs). Finally, in-depth discussions are provided for commonly encountered problems.

This is a preview of subscription content, 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   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
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

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Iqbal K, del C. Alonso A, Chen S et al (2005) Tau pathology in Alzheimer disease and other tauopathies. Biochim Biophys Acta 1739(2–3):198–210

    Article  CAS  PubMed  Google Scholar 

  2. Noble W, Hanger DP, Miller CCJ et al (2013) The importance of tau phosphorylation for neurodegenerative diseases. Front Neurol 4:83

    Article  PubMed  PubMed Central  Google Scholar 

  3. Buee L, Bussiere T, Buee-Scherrer V et al (2000) Tau protein isoforms, phosphorylation and role in neurodegenerative disorders. Brain Res Brain Res Rev 33(1):95–130

    Article  CAS  PubMed  Google Scholar 

  4. Hanger DP, Anderton BH, Noble W (2009) Tau phosphorylation: the therapeutic challenge for neurodegenerative disease. Trends Mol Med 15(3):112–119

    Article  CAS  PubMed  Google Scholar 

  5. Hasegawa M, Morishima-Kawashima M, Takio K et al (1992) Protein sequence and mass spectrometric analyses of tau in the Alzheimer’s disease brain. J Biol Chem 267(24):17047–17054

    CAS  PubMed  Google Scholar 

  6. Hoffmann R, Lee VM, Leight S et al (1997) Unique Alzheimer's disease paired helical filament specific epitopes involve double phosphorylation at specific sites. Biochemistry 36(26):8114–8124

    Article  CAS  PubMed  Google Scholar 

  7. Lim J, Ping Lu K (2005) Pinning down phosphorylated tau and tauopathies. Biochim Biophys Acta 1739(2–3):311–322

    Article  CAS  PubMed  Google Scholar 

  8. Morishima-Kawashima M, Hasegawa M, Takio K et al (1995) Proline-directed and non-proline-directed phosphorylation of PHF-tau. J Biol Chem 270(2):823–829

    Article  CAS  PubMed  Google Scholar 

  9. Biernat J, Mandelkow EM, Schroter C et al (1992) The switch of tau protein to an Alzheimer-like state includes the phosphorylation of two serine-proline motifs upstream of the microtubule binding region. EMBO J 11(4):1593–1597

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Goedert M, Jakes R, Vanmechelen E (1995) Monoclonal antibody AT8 recognises tau protein phosphorylated at both serine 202 and threonine 205. Neurosci Lett 189(3):167–169

    Article  CAS  PubMed  Google Scholar 

  11. Mercken M, Vandermeeren M, Lubke U et al (1992) Monoclonal antibodies with selective specificity for Alzheimer tau are directed against phosphatase-sensitive epitopes. Acta Neuropathol 84(3):265–272

    Article  CAS  PubMed  Google Scholar 

  12. Zheng-Fischhofer Q, Biernat J, Mandelkow EM et al (1998) Sequential phosphorylation of TAU by glycogen synthase kinase-3beta and protein kinase A at Thr212 and Ser214 generates the Alzheimer-specific epitope of antibody AT100 and requires a paired-helical-filament-like conformation. Eur J Biochem 252(3):542–552

    Article  CAS  PubMed  Google Scholar 

  13. Goedert M, Jakes R, Crowther RA et al (1994) Epitope mapping of monoclonal antibodies to the paired helical filaments of Alzheimer’s disease: identification of phosphorylation sites in tau protein. Biochem J 301(Pt 3):871–877

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Jicha GA, Lane E, Vincent I et al (1997) A conformation- and phosphorylation-dependent antibody recognizing the paired helical filaments of Alzheimer's disease. J Neurochem 69(5):2087–2095

    Article  CAS  PubMed  Google Scholar 

  15. Otvos L Jr, Feiner L, Lang E et al (1994) Monoclonal antibody PHF-1 recognizes tau protein phosphorylated at serine residues 396 and 404. J Neurosci Res 39(6):669–673

    Article  CAS  PubMed  Google Scholar 

  16. Goedert M, Cohen ES, Jakes R et al (1992) p42 MAP kinase phosphorylation sites in microtubule-associated protein tau are dephosphorylated by protein phosphatase 2A1. Implications for Alzheimer’s disease [corrected]. FEBS Lett 312(1):95–99

    Article  CAS  PubMed  Google Scholar 

  17. Porzig R, Singer D, Hoffmann R (2007) Epitope mapping of mAbs AT8 and Tau5 directed against hyperphosphorylated regions of the human tau protein. Biochem Biophys Res Commun 358(2):644–649

    Article  CAS  PubMed  Google Scholar 

  18. Jeganathan S, Hascher A, Chinnathambi S et al (2008) Proline-directed pseudo-phosphorylation at AT8 and PHF1 epitopes induces a compaction of the paperclip folding of Tau and generates a pathological (MC-1) conformation. J Biol Chem 283(46):32066–32076

    Article  CAS  PubMed  Google Scholar 

  19. Amniai L, Barbier P, Sillen A et al (2009) Alzheimer disease specific phosphoepitopes of Tau interfere with assembly of tubulin but not binding to microtubules. FASEB J 23(4):1146–1152

    Article  CAS  PubMed  Google Scholar 

  20. Ball LJ, Kuhne R, Schneider-Mergener J et al (2005) Recognition of proline-rich motifs by protein-protein-interaction domains. Angew Chem Int Ed Engl 44(19):2852–2869

    Article  CAS  PubMed  Google Scholar 

  21. Uversky VN (2013) Unusual biophysics of intrinsically disordered proteins. Biochim Biophys Acta 1834(5):932–951

    Article  CAS  PubMed  Google Scholar 

  22. Papoian GA (2008) Proteins with weakly funneled energy landscapes challenge the classical structure–function paradigm. Proc Natl Acad Sci U S A 105(38):14237–14238

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Structure of IDPs (2009) In: Structure and function of intrinsically disordered proteins. Chapman and Hall/CRC, pp 121–142

    Google Scholar 

  24. Sinko W, Miao Y, de Oliveira CAF et al (2013) Population based reweighting of scaled molecular dynamics. J Phys Chem B 117(42):12759–12768

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hamelberg D, de Oliveira CA, McCammon JA (2007) Sampling of slow diffusive conformational transitions with accelerated molecular dynamics. J Chem Phys 127(15):155102

    Article  PubMed  Google Scholar 

  26. Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120(24):11919–11929

    Article  CAS  PubMed  Google Scholar 

  27. Hamelberg D, Shen T, McCammon JA (2005) Phosphorylation effects on cis/trans isomerization and the backbone conformation of serine-proline motifs: accelerated molecular dynamics analysis. J Am Chem Soc 127(6):1969–1974

    Article  CAS  PubMed  Google Scholar 

  28. Velazquez HA, Hamelberg D (2015) Dynamical role of phosphorylation on serine/threonine-proline Pin1 substrates from constant force molecular dynamics simulations. J Chem Phys 142(7):075102

    Article  PubMed  Google Scholar 

  29. Gandhi NS, Landrieu I, Byrne C et al (2015) A phosphorylation-induced turn defines the Alzheimer's disease AT8 antibody epitope on the tau protein. Angew Chem Int Ed Engl 54(23):6819–6823

    Article  CAS  PubMed  Google Scholar 

  30. Lippens G, Amniai L, Wieruszeski JM et al (2012) Towards understanding the phosphorylation code of tau. Biochem Soc Trans 40(4):698–703

    Article  CAS  PubMed  Google Scholar 

  31. Case DA, Babin V, Berryman JT et al (2014) AMBER14. University of California, San Francisco, CA

    Google Scholar 

  32. Abraham M, Apol E, Apostolov R et al (2014) GROMACS user manual version 4.6.7.

    Google Scholar 

  33. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38, 27-38

    Article  CAS  PubMed  Google Scholar 

  34. Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF chimera – a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612

    Article  CAS  PubMed  Google Scholar 

  35. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637

    Article  CAS  PubMed  Google Scholar 

  36. Han B, Liu Y, Ginzinger SW et al (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50(1):43–57

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Berendsen HJ, Postma JPM, van Gunsteren WF et al (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81(8):3684–3690

    Article  CAS  Google Scholar 

  38. Loncharich RJ, Brooks BR, Pastor RW (1992) Langevin dynamics of peptides: the frictional dependence of isomerization rates of N-acetylalanyl-Nʹ-methylamide. Biopolymers 32(5):523–535

    Article  CAS  PubMed  Google Scholar 

  39. Buckingham A (1960) Chemical shifts in the nuclear magnetic resonance spectra of molecules containing polar groups. Can J Chem 38(2):300–307

    Article  CAS  Google Scholar 

  40. Kukic P, Farrell D, Søndergaard C, Bjarnadottir U, Bradley J, Pollastri G, Nielsen JE (2010) Improving the analysis of NMR spectra tracking pH-induced conformational changes: removing artefacts of the electric field on the NMR chemical shift. Proteins 78:971–984

    Article  CAS  PubMed  Google Scholar 

  41. Hass MAS, Ringkjøbing Jensen M, Led JJ (2008) Probing electric fields in proteins in solution by NMR spectroscopy. Proteins 72:333–343

    Article  CAS  PubMed  Google Scholar 

  42. Stanley N, Esteban-Martín S, De Fabritiis G (2015) Progress in studying intrinsically disordered proteins with atomistic simulations. Prog Biophys Mol Biol 119(1):47–52

    Article  CAS  PubMed  Google Scholar 

  43. Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314(1–2):141–151

    Article  CAS  Google Scholar 

  44. Larini L, Gessel MM, LaPointe NE et al (2013) Initiation of assembly of tau (273–284) and its ΔK280 mutant: an experimental and computational study. Phys Chem Chem Phys 15(23):8916–8928

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Levine ZA, Larini L, Shea J-E (2014) Tau (273–284): a molecular dynamics study of intrinsically disordered protein conformations in the presence of osmolytes. Biophys J 106(2):483

    Article  Google Scholar 

  46. Ganguly P, Do TD, Larini L et al (2015) Tau assembly: the dominant role of PHF6 (VQIVYK) in microtubule binding region repeat R3. J Phys Chem B 119(13):4582–4593

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Mukrasch MD, Markwick P, Biernat J et al (2007) Highly populated turn conformations in natively unfolded tau protein identified from residual dipolar couplings and molecular simulation. J Am Chem Soc 129(16):5235–5243

    Article  CAS  PubMed  Google Scholar 

  48. Pierce LCT, Salomon-Ferrer R, de Augusto F. Oliveira C et al (2012) Routine access to millisecond time scale events with accelerated molecular dynamics. J Chem Theory Comput 8(9):2997–3002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Miao Y, Sinko W, Pierce L et al (2014) Improved reweighting of accelerated molecular dynamics simulations for free energy calculation. J Chem Theory Comput 10(7):2677–2689

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Jing Z, Sun H (2015) A comment on the reweighting method for accelerated molecular dynamics simulations. J Chem Theory Comput 11(6):2395–2397

    Article  CAS  PubMed  Google Scholar 

  51. Götz AW, Williamson MJ, Xu D et al (2012) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. J Chem Theory Comput 8(5):1542–1555

    Article  PubMed  PubMed Central  Google Scholar 

  52. Markwick PR, Bouvignies G, Salmon L et al (2009) Toward a unified representation of protein structural dynamics in solution. J Am Chem Soc 131(46):16968–16975

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Feldman HJ, Hogue CW (2000) A fast method to sample real protein conformational space. Proteins 39(2):112–131

    Article  CAS  PubMed  Google Scholar 

  54. Feldman HJ, Hogue CW (2002) Probabilistic sampling of protein conformations: new hope for brute force? Proteins 46(1):8–23

    Article  CAS  PubMed  Google Scholar 

  55. Marsh JA, Forman-Kay JD (2009) Structure and disorder in an unfolded state under nondenaturing conditions from ensemble models consistent with a large number of experimental restraints. J Mol Biol 391(2):359–374

    Article  CAS  PubMed  Google Scholar 

  56. Adzhubei AA, Sternberg MJ (1993) Left-handed polyproline II helices commonly occur in globular proteins. J Mol Biol 229(2):472–493

    Article  CAS  PubMed  Google Scholar 

  57. Berisio R, Loguercio S, De Simone A et al (2006) Polyproline helices in protein structures: a statistical survey. Protein Pept Lett 13(8):847–854

    Article  CAS  PubMed  Google Scholar 

  58. King SM, Johnson WC (1999) Assigning secondary structure from protein coordinate data. Proteins 35(3):313–320

    Article  CAS  PubMed  Google Scholar 

  59. Srinivasan R, Rose GD (1999) A physical basis for protein secondary structure. Proc Natl Acad Sci U S A 96(25):14258–14263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Cubellis MV, Cailliez F, Lovell SC (2005) Secondary structure assignment that accurately reflects physical and evolutionary characteristics. BMC Bioinformatics 6(Suppl 4):S8

    Article  PubMed  PubMed Central  Google Scholar 

  61. Mansiaux Y, Joseph AP, Gelly J-C et al (2011) Assignment of polyproline II conformation and analysis of sequence – structure relationship. PLoS One 6(3), e18401

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Cino EA, Choy W-Y, Karttunen M (2012) Comparison of secondary structure formation using 10 different force fields in microsecond molecular dynamics simulations. J Chem Theory Comput 8(8):2725–2740

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lindorff-Larsen K, Maragakis P, Piana S et al (2012) Systematic validation of protein force fields against experimental data. PLoS One 7(2), e32131

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Beauchamp KA, Lin Y-S, Das R et al (2012) Are protein force fields getting better? A systematic benchmark on 524 diverse NMR measurements. J Chem Theory Comput 8(4):1409–1414

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Palazzesi F, Prakash MK, Bonomi M et al (2015) Accuracy of current all-atom force-fields in modeling protein disordered states. J Chem Theory Comput 11(1):2–7

    Article  CAS  PubMed  Google Scholar 

  66. Nerenberg PS, Head-Gordon T (2011) Optimizing protein−solvent force fields to reproduce intrinsic conformational preferences of model peptides. J Chem Theory Comput 7(4):1220–1230

    Article  CAS  PubMed  Google Scholar 

  67. Ball KA, Phillips Aaron H, Wemmer David E et al (2013) Differences in β-strand populations of monomeric Aβ40 and Aβ42. Biophys J 104(12):2714–2724

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wang W, Ye W, Jiang C et al (2014) New force field on modeling intrinsically disordered proteins. Chem Biol Drug Des 84(3):253–269

    Article  CAS  PubMed  Google Scholar 

  69. Ye W, Ji D, Wang W et al (2015) Test and evaluation of ff99IDPs force field for intrinsically disordered proteins. J Chem Inf Model 55(5):1021–1029

    Article  CAS  PubMed  Google Scholar 

  70. Best RB, Mittal J (2010) Protein simulations with an optimized water model: cooperative helix formation and temperature-induced unfolded state collapse. J Phys Chem B 114(46):14916–14923

    Article  CAS  PubMed  Google Scholar 

  71. Horn HW, Swope WC, Pitera JW et al (2004) Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew. J Chem Phys 120(20):9665–9678

    Article  CAS  PubMed  Google Scholar 

  72. Abascal JL, Vega C (2005) A general purpose model for the condensed phases of water: TIP4P/2005. J Chem Phys 123(23):234505

    Article  CAS  PubMed  Google Scholar 

  73. Nerenberg PS, Jo B, So C et al (2012) Optimizing solute-water van der Waals interactions to reproduce solvation free energies. J Phys Chem B 116(15):4524–4534

    Article  CAS  PubMed  Google Scholar 

  74. Best RB, Zheng W, Mittal J (2014) Balanced protein–water interactions improve properties of disordered proteins and non-specific protein association. J Chem Theory Comput 10(11):5113–5124

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Piana S, Donchev AG, Robustelli P et al (2015) Water dispersion interactions strongly influence simulated structural properties of disordered protein states. J Phys Chem B 119(16):5113–5123

    Article  CAS  PubMed  Google Scholar 

  76. Homeyer N, Horn AH, Lanig H et al (2006) AMBER force-field parameters for phosphorylated amino acids in different protonation states: phosphoserine, phosphothreonine, phosphotyrosine, and phosphohistidine. J Mol Model 12(3):281–289

    Article  CAS  PubMed  Google Scholar 

  77. Steinbrecher T, Latzer J, Case D (2012) Revised AMBER parameters for bioorganic phosphates. J Chem Theory Comput 8(11):4405–4412

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Khoury GA, Thompson JP, Smadbeck J et al (2013) Forcefield_PTM: ab initio charge and AMBER forcefield parameters for frequently occurring post-translational modifications. J Chem Theory Comput 9(12):5653–5674

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Miao Y, Feixas F, Eun C et al (2015) Accelerated molecular dynamics simulations of protein folding. J Comput Chem 36(20):1536–1549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Doshi U, Hamelberg D (2009) Reoptimization of the AMBER force field parameters for peptide bond (Omega) torsions using accelerated molecular dynamics. J Phys Chem B 113(52):16590–16595

    Article  CAS  PubMed  Google Scholar 

  81. De Groot B, Van Aalten D, Scheek R et al (1997) Prediction of protein conformational freedom from distance constraints. Proteins 29(2):240–251

    Article  PubMed  Google Scholar 

  82. David C, Jacobs D (2014) Principal component analysis: a method for determining the essential dynamics of proteins. In: Livesay DR (ed) Protein dynamics, vol 1084, methods in molecular biology. Humana, Louisville, KY, pp 193–226

    Google Scholar 

  83. Showalter SA (2007) Intrinsically disordered proteins: methods for structure and dynamics studies. eMagRes 3(2). John Wiley & Sons, Ltd

    Google Scholar 

  84. Tompa P (2011) Unstructural biology coming of age. Curr Opin Struct Biol 21(3):419–425

    Article  CAS  PubMed  Google Scholar 

  85. Jensen MR, Zweckstetter M, J-r H et al (2014) Exploring free-energy landscapes of intrinsically disordered proteins at atomic resolution using NMR spectroscopy. Chem Rev 114(13):6632–6660

    Article  CAS  PubMed  Google Scholar 

  86. Bastidas M, Gibbs EB, Sahu D et al (2015) A primer for carbon-detected NMR applications to intrinsically disordered proteins in solution. Concept Magnet Reson A 44(1):54–66

    Article  CAS  Google Scholar 

  87. Bienkiewicz E, Lumb K (1999) Random-coil chemical shifts of phosphorylated amino acids. J Biomol NMR 15(3):203–206

    Article  CAS  PubMed  Google Scholar 

  88. Wishart DS, Sykes BD (1994) Chemical shifts as a tool for structure determination. Methods Enzymol 239:363–392

    Article  CAS  PubMed  Google Scholar 

  89. Schwarzinger S, Kroon GJ, Foss TR et al (2001) Sequence-dependent correction of random coil NMR chemical shifts. J Am Chem Soc 123(13):2970–2978

    Article  CAS  PubMed  Google Scholar 

  90. Tamiola K, Acar B, Mulder FA (2010) Sequence-specific random coil chemical shifts of intrinsically disordered proteins. J Am Chem Soc 132(51):18000–18003

    Article  CAS  PubMed  Google Scholar 

  91. Shen Y, Lange O, Delaglio F et al (2008) Consistent blind protein structure generation from NMR chemical shift data. Proc Natl Acad Sci U S A 105(12):4685–4690

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Camilloni C, De Simone A, Vranken WF et al (2012) Determination of secondary structure populations in disordered states of proteins using nuclear magnetic resonance chemical shifts. Biochemistry 51(11):2224–2231

    Article  CAS  PubMed  Google Scholar 

  93. Moon S, Case DA (2007) A new model for chemical shifts of amide hydrogens in proteins. J Biomol NMR 38(2):139–150

    Article  CAS  PubMed  Google Scholar 

  94. Vila JA, Arnautova YA, Martin OA et al (2009) Quantum-mechanics-derived 13Cα chemical shift server (CheShift) for protein structure validation. Proc Natl Acad Sci U S A 106(40):16972–16977

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Kohlhoff KJ, Robustelli P, Cavalli A et al (2009) Fast and accurate predictions of protein NMR chemical shifts from interatomic distances. J Am Chem Soc 131(39):13894–13895

    Article  CAS  PubMed  Google Scholar 

  96. Lehtivarjo J, Hassinen T, Korhonen S-P et al (2009) 4D Prediction of protein 1H chemical shifts. J Biomol NMR 45(4):413–426

    Article  CAS  PubMed  Google Scholar 

  97. Lehtivarjo J, Tuppurainen K, Hassinen T et al (2012) Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction. J Biomol NMR 52(3):257–267

    Article  CAS  PubMed  Google Scholar 

  98. Kukic P, Farrell D, McIntosh LP et al (2013) Protein dielectric constants determined from NMR chemical shift perturbations. J Am Chem Soc 135(45):16968–16976

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgment

N.S.G. acknowledges the award of a Curtin Early Career Research Fellowship. This work was also supported by resources provided by the Pawsey Supercomputing Centre.

Author information

Authors and Affiliations

  1. School of Biomedical Sciences, CHIRI Biosciences and Curtin Institute for Computation, Curtin University, G.P.O. Box U1987, Perth, WA, 6845, Australia

    Neha S. Gandhi & Ricardo L. Mancera

  2. Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK

    Predrag Kukic

  3. Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés, Université de Toulouse, CNRS, INRA, INSA Toulouse, 135Avenue de Rangueil, 31077, Toulouse, France

    Guy Lippens

  4. Université de Lille, CNRS, UMR 8576, UGSF-Unité de Glycobiologie Structurale et Fonctionnelle, 59000 Lille, France

    Guy Lippens

Authors
  1. Neha S. Gandhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Predrag Kukic
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guy Lippens
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ricardo L. Mancera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Neha S. Gandhi or Ricardo L. Mancera .

Editor information

Editors and Affiliations

  1. UMR CNRS 8576, Villeneuve d’Ascq, France

    Caroline Smet-Nocca

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this protocol

Cite this protocol

Gandhi, N.S., Kukic, P., Lippens, G., Mancera, R.L. (2017). Molecular Dynamics Simulation of Tau Peptides for the Investigation of Conformational Changes Induced by Specific Phosphorylation Patterns. In: Smet-Nocca, C. (eds) Tau Protein. Methods in Molecular Biology, vol 1523. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6598-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6598-4_3

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6596-0

  • Online ISBN: 978-1-4939-6598-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation