Atomistic characterization of binding modes and affinity of peptide inhibitors to amyloid-β protein

Abstract

The aggregation of amyloid β-protein (Aβ) is tightly linked to the pathogenesis of Alzheimer’s disease. Previous studies have found that three peptide inhibitors (i.e., KLVFF, VVIA, and LPFFD) can inhibit Aβ aggregation and alleviate Aβ-induced neurotoxicity. However, atomic details of binding modes and binding affinities between these peptide inhibitors and Aβ have not been revealed. Here, using molecular dynamics simulations and molecular mechanics Poisson Boltzmann surface area (MM/PBSA) analysis, we examined the effect of three peptide inhibitors (KLVFF, VVIA, and LPFFD) on their sequence-specific interactions with Aβ and the molecular basis of their inhibition. All inhibitors exhibit varied binding affinity to Aβ, in which KLVFF has the highest binding affinity, whereas LPFFD has the least. MM/PBSA analysis further revealed that different peptide inhibitors have different modes of interaction with Aβ, consequently hotspot binding residues, and underlying driving forces. Specific residue-based interactions between inhibitors and Aβ were determined and compared for illustrating different binding and inhibition mechanisms. This work provides structure-based binding information for further modification and optimization of these three peptide inhibitors to enhance their binding and inhibitory abilities against Aβ aggregation.

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

References

  1. 1.

    Mattson M P. Pathways towards and away from Alzheimer’s disease. Nature, 2004, 430(7000): 631–639

    CAS  Article  Google Scholar 

  2. 2.

    Blennow K, de Leon M J, Zetterberg H. Alzheimer’s disease. Lancet, 2006, 368(9533): 387–403

    CAS  Article  Google Scholar 

  3. 3.

    Selkoe D J. The molecular pathology of Alzheimer’s disease. Neuron, 1991, 6(4): 487–498

    CAS  Article  Google Scholar 

  4. 4.

    Miller D L, Papayannopoulos I A, Styles J, Bobin S A, Lin Y Y, Biemann K, Iqbal K. Peptide compositions of the cerebrovascular and senile plaque core amyloid deposits of Alzheimer’s disease. Archives of Biochemistry and Biophysics, 1993, 301(1): 41–52

    CAS  Article  Google Scholar 

  5. 5.

    Kang J, Lemaire H G, Unterbeck A, Salbaum J M, Masters C L, Grzeschik K H, Multhaup G, Beyreuther K, Muller-Hill B. The precursor of Alzheimer’s disease amyloid A4 protein resembles a cell-surface receptor. Nature, 1987, 325(6106): 733–736

    CAS  Article  Google Scholar 

  6. 6.

    Mattson MP. Cellular actions of beta-amyloid precursor protein and its soluble and fibrillogenic derivatives. Physiological Reviews, 1997, 77: 1081–1132

    CAS  Google Scholar 

  7. 7.

    Li X, Mehler E. Simulation of molecular crowding effects on an Alzheimer’s α-amyloid peptide. Cell Biochemistry and Biophysics, 2006, 46(2): 123–141

    CAS  Article  Google Scholar 

  8. 8.

    Jarrett J T, Berger E P, Lansbury P T J. The carboxy terminus of the beta amyloid protein is critical for the seeding of amyloid formation: implications for the pathogenesis of Alzheimer’s disease. Biochemistry, 1993, 32(18): 4693–4697

    CAS  Article  Google Scholar 

  9. 9.

    Zhang Y, McLaughlin R, Goodyer C, LeBlanc A. Selective cytotoxicity of intracellular amyloid β-peptide(1–42) through p53 and Bax in cultured primary human neurons. Journal of Cell Biology, 2002, 156(3): 519–529

    CAS  Article  Google Scholar 

  10. 10.

    Simona F, Tiana G, Broglia R A, Colombo G. Modeling the alphahelix to beta-hairpin transition mechanism and the formation of oligomeric aggregates of the fibrillogenic peptide A beta(12–28): Insights from all-atom molecular dynamics simulations. Journal of Molecular Graphics & Modelling, 2004, 23(3): 263–273

    CAS  Article  Google Scholar 

  11. 11.

    Mager P P. Molecular simulation of the amyloid β-peptide Aβ-(1–40) of Alzheimer’s disease. Molecular Simulation, 1998, 20(4): 201–222

    CAS  Article  Google Scholar 

  12. 12.

    Anand P, Hansmann U H E. Internal and environmental effects on folding and dimerisation of Alzheimer’s β-amyloid peptide. Molecular Simulation, 2011, 37(06): 440–448

    CAS  Article  Google Scholar 

  13. 13.

    Xu Y, Shen J, Luo X, Zhu W, Chen K, Ma J, Jiang H. Conformational transition of amyloid β-peptide. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(15): 5403–5407

    CAS  Article  Google Scholar 

  14. 14.

    Yang C, Zhu X L, Li J Y, Shi R W. Exploration of the mechanism for LPFFD inhibiting the formation of β-sheet conformation of Aβ (1–42) in water. Journal of Molecular Modeling, 2010, 16(4): 813–821

    CAS  Article  Google Scholar 

  15. 15.

    Naeem A, Fazili N. Defective protein folding and aggregation as the basis of neurodegenerative diseases: The darker aspect of proteins. Cell Biochemistry and Biophysics, 2011, 61(2): 237–250

    CAS  Article  Google Scholar 

  16. 16.

    Yu X, Wang J, Yang J C, Wang Q, Cheng S Z D, Nussinov R, Zheng J. Atomic-scale simulations confirm that soluble β-sheet-rich peptide self-assemblies provide amyloid mimics presenting similar conformational properties. Biophysical Journal, 2010, 98(1): 27–36

    CAS  Article  Google Scholar 

  17. 17.

    Hamley I W. The amyloid beta peptide: A chemist’s perspective. Role in Alzheimer’s and fibrillization. Chemical Reviews, 2012, 112(10): 5147–5192

    CAS  Article  Google Scholar 

  18. 18.

    Wang Q M, Yu X, Li L Y, Zheng J. Inhibition of amyloid-beta aggregation in Alzheimer’s disease. Current Pharmaceutical Design, 2014, 20(8): 1223–1243

    CAS  Article  Google Scholar 

  19. 19.

    Liu F F, Ji L, Dong X Y, Sun Y. Molecular insight into the inhibition effect of trehalose on the nucleation and elongation of amyloid betapeptide oligomers. Journal of Physical Chemistry B, 2009, 113(32): 11320–11329

    CAS  Article  Google Scholar 

  20. 20.

    Wang C, Yang A, Li X, Li D, Zhang M, Du H, Li C, Guo Y, Mao X, Dong M, Besenbacher F, Yang Y, Wang C. Observation of molecular inhibition and binding structures of amyloid peptides. Nanoscale, 2012, 4(6): 1895–1909

    CAS  Article  Google Scholar 

  21. 21.

    Soto C, Sigurdsson E M, Morelli L, Kumar R A, Castano E M, Frangione B. β-Sheet breaker peptides inhibit fibrillogenesis in a rat brain model of amyloidosis: Implications for Alzheimer’s therapy. Nature Medicine, 1998, 4(7): 822–826

    CAS  Article  Google Scholar 

  22. 22.

    Findeis M A, Musso G M, Arico-Muendel C C, Benjamin H W, Hundal A M, Lee J J, Chin J, Kelley M, Wakefield J, Hayward N J, Molineaux S M. Modified-peptide inhibitors of amyloid β-peptide polymerization. Biochemistry, 1999, 38(21): 6791–6800

    CAS  Article  Google Scholar 

  23. 23.

    Fradinger E A, Monien B H, Urbanc B, Lomakin A, Tan M, Li H, Spring S M, Condron M M, Cruz L, Xie C W, Benedek G B, Bitan G. C-terminal peptides coassemble into Aβ42 oligomers and protect neurons against Aβ42-induced neurotoxicity. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(37): 14175–14180

    CAS  Article  Google Scholar 

  24. 24.

    Tjernberg L O, Naslund J, Lindqvist F, Johansson J, Karlstrom A R, Thyberg J, Terenius L, Nordstedt C. Arrest of β-amyloid fibril formation by a pentapeptide ligand. Journal of Biological Chemistry, 1996, 271(15): 8545–8548

    CAS  Article  Google Scholar 

  25. 25.

    Singh P, Maji S. Amyloid-like fibril formation by Tachykinin neuropeptides and its relevance to amyloid β-protein aggregation and toxicity. Cell Biochemistry and Biophysics, 2012, 64(1): 29–44

    CAS  Article  Google Scholar 

  26. 26.

    Mager P. Backpropagation neural network analysis applied to β-sheet breakers used against Alzheimer’s amyloid aggregation. Molecular Simulation, 2002, 28(3): 239–247

    CAS  Article  Google Scholar 

  27. 27.

    Viet M H, Ngo S T, Lam N S, Li M S. Inhibition of aggregation of amyloid peptides by beta-sheet breaker peptides and their binding affinity. Journal of Physical Chemistry B, 2011, 115(22): 7433–7446

    CAS  Article  Google Scholar 

  28. 28.

    Liu R, Yuan B, Emadi S, Zameer A, Schulz P, McAllister C, Lyubchenko Y, Goud G, Sierks M R. Single chain variable fragments against β-amyloid (Aβ) can inhibit Aβ aggregation and prevent Aβ-induced neurotoxicity. Biochemistry, 2004, 43(22): 6959–6967

    CAS  Article  Google Scholar 

  29. 29.

    Manoutcharian K, Acero G, Munguia M E, Becerril B, Massieu L, Govezensky T, Ortiz E, Marks J D, Cao C, Ugen K, Gevorkian G. Human single chain Fv antibodies and a complementarity determining region-derived peptide binding to amyloid-β 1–42. Neurobiology of Disease, 2004, 17(1): 114–121

    CAS  Article  Google Scholar 

  30. 30.

    Cabaleiro-Lago C, Quinlan-Pluck F, Lynch I, Lindman S, Minogue A M, Thulin E, Walsh D M, Dawson K A, Linse S. Inhibition of amyloid β protein fibrillation by polymeric nanoparticles. Journal of the American Chemical Society, 2008, 130(46): 15437–15443

    CAS  Article  Google Scholar 

  31. 31.

    Takahashi T, Mihara H. Peptide and protein mimetics inhibiting amyloid β-peptide aggregation. Accounts of Chemical Research, 2008, 41(10): 1309–1318

    CAS  Article  Google Scholar 

  32. 32.

    Soto C, Kindy M S, Baumann M, Frangione B. Inhibition of Alzheimer’s amyloidosis by peptides that prevent β-sheet conformation. Biochemical and Biophysical Research Communications, 1996, 226(3): 672–680

    CAS  Article  Google Scholar 

  33. 33.

    Li H Y, Monien B H, Lomakin A, Zemel R, Fradinger E A, Tan M A, Spring S M, Urbanc B, Xie C W, Benedek G B, Bitan G. Mechanistic investigation of the inhibition of A beta 42 assembly and neurotoxicity by A beta 42 C-terminal fragments. Biochemistry, 2010, 49(30): 6358–6364

    CAS  Article  Google Scholar 

  34. 34.

    Dong X Y, Du W J, Liu F F. Molecular dynamics simulation and binding free energy calculation of the conformational transition of amyloid peptide 42 inhibited by peptide inhibitors. Acta Physico-Chimica Sinica, 2012, 28: 2735–2744

    CAS  Google Scholar 

  35. 35.

    Hou T J, Wang JM, Li Y Y, Wang W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. Journal of Chemical Information and Modeling, 2011, 51(1): 69–82

    CAS  Article  Google Scholar 

  36. 36.

    Hou T J, Wang J M, Li Y Y, Wang W. Assessing the performance of the molecular mechanics/poisson boltzmann surface area and molecular mechanics/generalized born surface area methods. II. The accuracy of ranking poses generated from docking. Journal of Computational Chemistry, 2011, 32(5): 866–877

    CAS  Article  Google Scholar 

  37. 37.

    Xu L, Sun H Y, Li Y Y, Wang J M, Hou T J. Assessing the performance of MM/PBSA and MM/GBSA methods. 3. The impact of force fields and ligand charge models. Journal of Physical Chemistry B, 2013, 117(28): 8408–8421

    CAS  Article  Google Scholar 

  38. 38.

    Wang J M, Hou T J, Xu X J. Recent advances in free energy calculations with a combination of molecular mechanics and continuum models. Current Computer-aided Drug Design, 2006, 2(3): 287–306

    CAS  Article  Google Scholar 

  39. 39.

    Crescenzi O, Tomaselli S, Guerrini R, Salvadori S, D’Ursi A M, Temussi P A, Picone D. Solution structure of the Alzheimer amyloid β-peptide (1–42) in an apolar microenvironment. European Journal of Biochemistry, 2002, 269(22): 5642–5648

    CAS  Article  Google Scholar 

  40. 40.

    van der Spoel D, Lindahl E, Hess B, Groenhof G, Mark A E, Berendsen H J C. GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 2005, 26(16): 1701–1718

    Article  Google Scholar 

  41. 41.

    van Gunsteren W F, Billeter S R, Eising A A, Hünenberger P H, Krüger P, Mark A E, Scott W R P, Tironi I G. Biomolecular Simulation: The GROMOS96 Manual and Userguide. Zürich, Switzerland, Groningen, Holland, 1996

    Google Scholar 

  42. 42.

    Berendsen H J C, Postma J P M, van Gunsteren W F, Hermans J. In: Intermolecular Forces. Pullman B, ed. Reidel: Dordecht, Holland, 1981

  43. 43.

    Bahrami H, Zahedi M, Moosavi-Movahedi A, Azizian H, Amanlou M. Theoretical investigation of interaction of sorbitol molecules with alcohol dehydrogenase in aqueous solution using molecular dynamics simulation. Cell Biochemistry and Biophysics, 2011, 59(2): 79–88

    CAS  Article  Google Scholar 

  44. 44.

    Bussi G, Donadio D, Parrinello M. Canonical sampling through velocity rescaling. Journal of Chemical Physics, 2007, 126(1): 014101

    Article  Google Scholar 

  45. 45.

    Berendsen H J C, Postma J P M, Gunsteren W F V, DiNola A, Haak J R. Molecular dynamics with coupling to an external bath. Journal of Chemical Physics, 1984, 81(8): 3684–3690

    CAS  Article  Google Scholar 

  46. 46.

    Hess B, Bekker H, Berendsen H J C, Fraaije J G E M. LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry, 1997, 18(12): 1463–1472

    CAS  Article  Google Scholar 

  47. 47.

    Verlet L. Computer “experiments” on classical fluids. I. Thermodynamical properties of Lennard-Jones molecules. Physical Review, 1967, 159(1): 98–103

    CAS  Article  Google Scholar 

  48. 48.

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

    CAS  Article  Google Scholar 

  49. 49.

    Zhou X Y, Xi W H, Luo Y, Cao S Q, Wei G H. Interactions of a water-soluble fullerene derivative with amyloid-beta protofibrils: Dynamics, binding mechanism, and the resulting salt bridge disruption. Journal of Physical Chemistry B, 2014, 118(24): 6733–6741

    CAS  Article  Google Scholar 

  50. 50.

    Zoete V, Meuwly M, Karplus M. Study of the insulin dimerization: Binding free energy calculations and per-residue free energy decomposition. Proteins. Structure, Function, and Bioinformatics, 2005, 61(1): 79–93

    CAS  Article  Google Scholar 

  51. 51.

    Milev S, Gorfe A A, Karshikoff A, Clubb R T, Bosshard H R, Jelesarov I. Energetics of sequence-specific protein-DNA association: Conformational stability of the DNA binding domain of integrase Tn916 and its cognate DNA duplex. Biochemistry, 2003, 42(12): 3492–3502

    CAS  Article  Google Scholar 

  52. 52.

    Lafont V, Schaefer M, Stote R H, Altschuh D, Dejaegere A. Proteinprotein recognition and interaction hot spots in an antigen-antibody complex: Free energy decomposition identifies “efficient amino acids”. Proteins. Structure, Function, and Bioinformatics, 2007, 67(2): 418–434

    CAS  Article  Google Scholar 

  53. 53.

    Yan C L, Kaoud T, Lee S B, Dalby K N, Ren P Y. Understanding the specificity of a docking interaction between JNK1 and the scaffolding protein JIP1. Journal of Physical Chemistry B, 2011, 115(6): 1491–1502

    CAS  Article  Google Scholar 

  54. 54.

    Huang B, Liu F F, Dong X Y, Sun Y. Molecular mechanism of the affinity interactions between protein A and human immunoglobulin G1 revealed by molecular simulations. Journal of Physical Chemistry B, 2011, 115(14): 4168–4176

    CAS  Article  Google Scholar 

  55. 55.

    Huang B, Liu F F, Dong X Y, Sun Y. Molecular mechanism of the effects of salt and pH on the affinity between protein A and human immunoglobulin G1 revealed by molecular simulations. Journal of Physical Chemistry B, 2012, 116(1): 424–433

    CAS  Article  Google Scholar 

  56. 56.

    Zheng J, Yu X, Wang J D, Yang J C, Wang Q M. Molecular modeling of two distinct triangular oligomers in amyloid betaprotein. Journal of Physical Chemistry B, 2010, 114(1): 463–470

    CAS  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Jie Zheng or Xiaoyan Dong.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liu, F., Du, W., Sun, Y. et al. Atomistic characterization of binding modes and affinity of peptide inhibitors to amyloid-β protein. Front. Chem. Sci. Eng. 8, 433–444 (2014). https://doi.org/10.1007/s11705-014-1454-6

Download citation

Keywords

  • Alzheimer’s disease
  • amyloid β-protein
  • peptide inhibitors
  • protein-protein interaction
  • molecular dynamics simulation