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Computational modeling of curcumin-based fluorescent probe molecules

  • Vardhan Satalkar
  • Theo A. Rusmore
  • Elizabeth Phillips
  • Xiaoliang Pan
  • Enrico Benassi
  • Qin Wu
  • Chongzhao RanEmail author
  • Yihan ShaoEmail author
Regular Article

Abstract

In recent years, a series of curcumin analogs have been designed as fluorescent probes for detecting and imaging \({\text {A}}\beta\) peptide aggregates and reactive oxygen species (ROS) in Alzheimer’s disease (AD) brains. In order to gain a better understanding of the photophysical properties of these probe molecules, a systematical computational investigation was performed using the time-dependent density functional theory (TDDFT) calculations. Computed absorption and emission wavelengths well reproduced the spectral shifts among the curcumin analogs. In particular, for a recently proposed pair of probe molecules, CRANAD-5 and CRANAD-61, for sensing ROS in preclinical studies of AD brains, their emission wavelength difference was found to arise from a delocalization of the lowest unoccupied molecular orbital of CRANAD-61 from the curcuminoid backbone to the oxalate moiety. Overall, this study reaffirms the value of employing TDDFT calculations to assist the design of new curcumin-based fluorescence probes for AD research.

Keywords

Fluorescence Alzheimer’s disease TDDFT 

Notes

Acknowledgements

Y.S. acknowledges financial support from Department of Energy Grant No. DE-SC0011297 and from the University of Oklahoma start-up fund. C.R. is supported by NIH Grant No. R01AG055413. Y.S. greatly appreciates insightful comments from Dr. Roland Lindh. Computational resources and services used in this work were provided by the OU Supercomputing Center for Education and Research (OSCER) and the Center of Functional Nanomaterials (CFN). CFN is a U.S. DOE Office of Science Facility, at the Brookhaven National Laboratory under Contract No. DE-SC0012704.

Supplementary material

214_2019_2415_MOESM1_ESM.pdf (10.1 mb)
Supplementary material 1 (pdf 10,336 KB)

References

  1. 1.
    Hardy J, Selkoe DJ (2002) The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 297:353–356CrossRefGoogle Scholar
  2. 2.
    Selkoe DJ (2011) Resolving controversies on the path to Alzheimer’s therapeutics. Nat Med 17(9):1060–1065CrossRefGoogle Scholar
  3. 3.
    Lue L, Kuo Y, Roher AE, Brachova L, Shen Y, Sue L, Beach T, Kurth JH, Rydel RE, Rogers J (1999) Soluble amyloid beta peptide concentration as a predictor of synaptic change in Alzheimer’s disease. Am J Pathol 155:853–862CrossRefGoogle Scholar
  4. 4.
    Mclean CA, Cherny RA, Fraser FW, Fuller SJ, Smith MJ, Beyreuther K, Bush AI, Masters CL (1999) Soluble pool of abeta amyloid as a determinant of severity of neurodegeneration in Alzheimer’s disease. Ann Neurol 46:860–866CrossRefGoogle Scholar
  5. 5.
    Giacobini E, Gold G (2013) Alzheimer disease therapy moving from amyloid beta to tau. Nat Rev Neurol 9:677–686CrossRefGoogle Scholar
  6. 6.
    Pratico D (2008) Oxidative stress hypothesis in Alzheimer’s disease: a reappraisal. Trends Pharmacol Sci 29:609–615CrossRefGoogle Scholar
  7. 7.
    Xie H, Hou S, Jiang J, Sekutowicz M, Kelly J, Bacskai BJ (2013) Rapid cell death is preceded by amyloid plaque-mediated oxidative stress. Proc Natl Acad Sci USA 110:7904–7909CrossRefGoogle Scholar
  8. 8.
    Wang X, Wang W, Li L, Perry G, Hg Lee, Zhu X (2014) Oxidative stress and mitochondrial dysfunction in Alzheimer’s disease. Biochim Biophys Acta 1842:1240–1247CrossRefGoogle Scholar
  9. 9.
    Kim GH, Kim JE, Rhie SJ, Yoon S (2015) The role of oxidative stress in neurodegenerative diseases. Exp Neurobiol 24:325–340CrossRefGoogle Scholar
  10. 10.
    Huang Y, Erdmann N, Peng H, Zhao Y, Zheng J (2005) The role of TNF related apoptosis-inducing ligand in neurodegenerative diseases. Cell Mol Immunol 2:113–122PubMedGoogle Scholar
  11. 11.
    Markesbery WR (1997) Oxidative stress hypothesis in Alzheimer’s disease. Free Radic Biol Med 23:134–147CrossRefGoogle Scholar
  12. 12.
    Yang JJ, Zhang X, Yuan P, Yang JJ, Xu Y, Grutzendler J, Shao Y, Moore A, Ran C (2017) Oxalate-curcumin based probe for micro- and macro-imaging of reactive oxygen species in Alzheimer’s disease. Proc Natl Acad Sci USA 114:12384–12389CrossRefGoogle Scholar
  13. 13.
    Patel V, Zhang X, Tautiva NA, Nyabera AN, Owa OO, Baidya M, Sung HC, Taunk PS, Abdollahi S, Charles S, Gonnella RA, Gadi N, Duong KT, Fawver JN, Ran C, Jalonen TO, Murray IVJ (2015) Small molecules and Alzheimer’s disease: misfolding, metabolism and imaging. Curr Alzheimer Res 12:000CrossRefGoogle Scholar
  14. 14.
    Amdursky N, Erez Y, Huppert D (2012) Molecular rotors: what lies behind the high sensitivity of the thioflavin-T fluorescent. Acc Chem Res 45(9):1548–1557CrossRefGoogle Scholar
  15. 15.
    Nesterov EE, Skoch J, Hyman BT, Klunk WE, Bacskai BJ, Swager TM (2005) In vivo optical imaging of amyloid aggregates in brain: design of fluorescent markers. Angew Chem Int Ed 44:5452–5456CrossRefGoogle Scholar
  16. 16.
    Ono M, Watanabe H, Kimura H, Saji H (2012) BODIPY-based molecular probe for imaging of cerebral beta-amyloid plaques. ACS Chem Neurosci 3:319–324CrossRefGoogle Scholar
  17. 17.
    Fu H, Cui M, Tu P, Pan Z, Liu B (2014) Evaluation of molecules based on the electron donor–acceptor architecture as near-infrared beta-amyloidal-targeting probes. Chem Commun 50:11875–11878CrossRefGoogle Scholar
  18. 18.
    Fu H, Cui M, Zhao L, Tu P, Zhou K, Dai J, Liu B (2015) Highly sensitive near-infrared fluorophores for in vivo detection of amyloid-beta plaques in Alzheimer’s disease. J Med Chem 58:6972–6983CrossRefGoogle Scholar
  19. 19.
    Ran C, Xu X, Raymond SB, Ferrara BJ, Neal K, Brian J, Medarova Z, Moore A (2009) Design, synthesis, and testing of difluoroboron derivatized curcumins as near infrared probes for in vivo detection of amyloid-beta deposits. J Am Chem Soc 131:15257–15261CrossRefGoogle Scholar
  20. 20.
    Zhang X, Tian Y, Li Z, Tian X, Sun H, Liu H, Moore A, Ran C (2013) Design and synthesis of curcumin analogues for in vivo fluorescence imaging and inhibiting copper-induced cross-linking of amyloid beta species in Alzheimer’s disease. J Am Chem Soc 135:16397–16409CrossRefGoogle Scholar
  21. 21.
    Zhang X, Tian Y, Zhang C, Tian X, Ross AW, Moir RD, Sun H, Tanzi RE, Moore A, Ran C (2015) Near-infrared fluorescence molecular imaging of amyloid beta species and monitoring therapy in animal models of Alzheimer’s disease. Proc Natl Acad Sci USA 112:9734–9739CrossRefGoogle Scholar
  22. 22.
    Yang J, Yang J, Liang SH, Xu Y, Moore A, Ran C (2016) Imaging hydrogen peroxide in Alzheimer’s disease via cascade signal amplification. Sci Rep 6:35613CrossRefGoogle Scholar
  23. 23.
    Martínez-Cifuentes M, Weiss-López B, Araya-Maturana R (2016) A computational study of structure and reactivity of N-substitued-4-piperidones curcumin analogues and their radical anions. Molecules 21:1658CrossRefGoogle Scholar
  24. 24.
    Ferrari E, Benassi R, Saladini M, Orteca G, Gazova Z, Siposova K (2017) In vitro study on potential pharmacological activity of curcumin analogues and their copper complexes. Chem Biol Drug Des 89:411–419CrossRefGoogle Scholar
  25. 25.
    Xu G, Wei D, Wang J, Jiang B, Wang M, Xue X, Zhou S, Wu B, Jiang M (2014) Dyes and pigments crystal structure, optical properties and biological imaging of two curcumin derivatives. Dye Pigment 101:312–317CrossRefGoogle Scholar
  26. 26.
    Sabate R, Rodriguez-santiago L, Sodupe M, Saupe SJ, Ventura S (2013) Thioflavin-T excimer formation upon interaction with amyloid fibers. Chem Commun 49:5745–5747CrossRefGoogle Scholar
  27. 27.
    Peccati F, Hernando J, Blancafort L, Solans-Monfort X, Sodupe M (2015) Disaggregation-induced fluorescence enhancement of NIAD-4 for the optical imaging of amyloid-beta fibrils. Phys Chem Chem Phys 17(30):19718–19725CrossRefGoogle Scholar
  28. 28.
    Peccati F, Widniewska M, Solans-Monfort X, Sodupe M (2016) Computational study on donor–acceptor optical markers for Alzheimer’s disease: a game of charge transfer and electron delocalization. Phys Chem Chem Phys 18:11634–11643CrossRefGoogle Scholar
  29. 29.
    Murugan NA, Zalesny R, Kongsted J, Nordberg A, Ågren H (2014) Promising two-photon probes for in vivo detection of beta amyloid deposits. Chem Commun 50:11694–11697CrossRefGoogle Scholar
  30. 30.
    Canard G, Ponce-Vargas M, Jacquemin D, Le Guennic B, Felouat A, Rivoal M, Zaborova E, D’Aléo A, Fages F (2017) Influence of the electron donor groups on the optical and electrochemical properties of borondifluoride complexes of curcuminoid derivatives: a joint theoretical and experimental study. RSC Adv 7:10132–10142CrossRefGoogle Scholar
  31. 31.
    Zhang X, Tian Y, Yuan P, Li Y, Yaseen MA, Grutzendler J, Moore A, Ran C (2014) A bifunctional curcumin analogue for two-photon imaging and inhibiting crosslinking of amyloid beta in Alzheimer’s disease. Chem Commun 50:11550–11553CrossRefGoogle Scholar
  32. 32.
    Parr RG, Yang W (1989) Density-functional theory of atoms and molecules. Oxford University Press, New YorkGoogle Scholar
  33. 33.
    Becke AD (2014) Perspective: fifty years of density-functional theory in chemical physics. J Chem Phys 140(18):18A301CrossRefGoogle Scholar
  34. 34.
    Burke K, Werschnik J, Gross EKU (2005) Time-dependent density functional theory: past, present, and future. J Chem Phys 123:062206CrossRefGoogle Scholar
  35. 35.
    Casida ME, Huix-Rotllant M (2012) Progress in time-dependent density-functional theory. Ann Rev Phys Chem 63:287–323CrossRefGoogle Scholar
  36. 36.
    Shao Y, Gan Z, Epifanovsky E, Gilbert AT, Wormit M, Kussmann J, Lange AW, Behn A, Deng J, Feng X, Ghosh D, Goldey M, Horn PR, Jacobson LD, Kaliman I, Khaliullin RZ, Kuś T, Landau A, Liu J, Proynov EI, Rhee YM, Richard RM, Rohrdanz MA, Steele RP, Sundstrom EJ, Woodcock HL, Zimmerman PM, Zuev D, Albrecht B, Alguire E, Austin B, Beran GJO, Bernard YA, Berquist E, Brandhorst K, Bravaya KB, Brown ST, Casanova D, Chang CM, Chen Y, Chien SH, Closser KD, Crittenden DL, Diedenhofen M, DiStasio R, Do H, Dutoi AD, Edgar RG, Fatehi S, Fusti-Molnar L, Ghysels A, Golubeva-Zadorozhnaya A, Gomes J, Hanson-Heine MW, Harbach PH, Hauser AW, Hohenstein EG, Holden ZC, Jagau TC, Ji H, Kaduk B, Khistyaev K, Kim J, Kim J, King RA, Klunzinger P, Kosenkov D, Kowalczyk T, Krauter CM, Lao KU, Laurent A, Lawler KV, Levchenko SV, Lin CY, Liu F, Livshits E, Lochan RC, Luenser A, Manohar P, Manzer SF, Mao SP, Mardirossian N, Marenich AV, Maurer SA, Mayhall NJ, Neuscamman E, Oana CM, Olivares-Amaya R, O’Neill DP, Parkhill JA, Perrine TM, Peverati R, Prociuk A, Rehn DR, Rosta E, Russ NJ, Sharada SM, Sharma S, Small DW, Sodt A, Stein T, Stück D, Su YC, Thom AJ, Tsuchimochi T, Vanovschi V, Vogt L, Vydrov O, Wang T, Watson MA, Wenzel J, White A, Williams CF, Yang J, Yeganeh S, Yost SR, You ZQ, Zhang IY, Zhang X, Zhao Y, Brooks BR, Chan GK, Chipman DM, Cramer CJ, Goddard WA, Gordon MS, Hehre WJ, Klamt A, Schaefer HF, Schmidt MW, Sherrill CD, Truhlar DG, Warshel A, Xu X, Aspuru-Guzik A, Baer R, Bell AT, Besley NA, Chai JD, Dreuw A, Dunietz BD, Furlani TR, Gwaltney SR, Hsu CP, Jung Y, Kong J, Lambrecht DS, Liang W, Ochsenfeld C, Rassolov VA, Slipchenko LV, Subotnik JE, Van Voorhis T, Herbert JM, Krylov AI, Gill PM, Head-Gordon M (2015) Advances in molecular quantum chemistry contained in the Q-Chem 4 Program Package. Mol Phys 113:184–215CrossRefGoogle Scholar
  37. 37.
    Becke AD (1988) Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A 38:3098–3100CrossRefGoogle Scholar
  38. 38.
    Becke AD (1993) A new mixing of Hartree–Fock and local density functional theories. J Chem Phys 98:1372CrossRefGoogle Scholar
  39. 39.
    Lee C, Yang W, Parr RG (1988) Development of the Colle–Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37:785–789CrossRefGoogle Scholar
  40. 40.
    Adamo C, Barone V (1999) Toward reliable density functional methods without adjustable parameters: the PBE0 model. J Chem Phys 110:6158CrossRefGoogle Scholar
  41. 41.
    Zhao Y, Truhlar DG (2007) The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functions. Theor Chem Acc 120:215–241CrossRefGoogle Scholar
  42. 42.
    Chai JD, Head-Gordon M (2008) Systematic optimization of long-range corrected hybrid density functionals. J Chem Phys 128:084106CrossRefGoogle Scholar
  43. 43.
    Caricato M, Trucks GW, Frisch MJ, Wiberg KB (2010) Electronic transition energies: a study of the performance of a large range of single reference density functional and wave function methods on Valence and Rydberg states compared to experiment. J Chem Theory Comput 6:370–383CrossRefGoogle Scholar
  44. 44.
    Leang SS, Zahariev F, Gordon MS (2012) Benchmarking the performance of time-dependent density functional methods. J Chem Phys 136:104101CrossRefGoogle Scholar
  45. 45.
    Isegawa M, Peverati R, Truhlar DG (2012) Performance of recent and high-performance approximate density functionals for time-dependent density functional theory calculations of Valence and Rydberg electronic transition energies. J Chem Phys 137:244104CrossRefGoogle Scholar
  46. 46.
    Laurent AD, Jacquemin D (2013) TD-DFT benchmarks: a review. Int J Quantum Chem 113(17):2019–2039CrossRefGoogle Scholar
  47. 47.
    Charaf-Eddin A, Planchat A, Mennucci B, Adamo C, Jacquemin D (2013) Choosing a functional for computing absorption and fluorescence band shapes with TD-DFT. J Chem Theory Comput 9:2749CrossRefGoogle Scholar
  48. 48.
    Krishnan R, Binkley JS, Seeger R, Pople JA (1980) Self-consistent molecular orbital methods. XX. A basis set for correlated wave functions. J Chem Phys 72:650–654CrossRefGoogle Scholar
  49. 49.
    Gill PMW, Johnson BG, Pople JA (1993) A standard grid for density functional calculations. Chem Phys Lett 209:506–512CrossRefGoogle Scholar
  50. 50.
    Becke AD (1988) A multicenter numerical integration scheme for polyatomic molecules. J Chem Phys 88:2547CrossRefGoogle Scholar
  51. 51.
    Tomasi J, Mennucci B, Cammi R (2005) Quantum mechanical continuum solvation models. Chem Rev 105:2999–3093CrossRefGoogle Scholar
  52. 52.
    Truong TN, Stefanovich EV (1995) A new method for incorporating solvent effect into the classical, ab initio molecular orbital and density functional theory frameworks for arbitrary shape cavity. Chem Phys Lett 240:253–260CrossRefGoogle Scholar
  53. 53.
    Barone V, Cossi M (1998) Quantum calculation of molecular energies and energy gradients in solution by a conductor solvent model. J Phys Chem A 102:1995–2001CrossRefGoogle Scholar
  54. 54.
    Lange AW, Herbert JM (2010) Polarizable continuum reaction-field solvation models affording smooth potential energy surfaces. J Phys Chem Lett 1:556–561CrossRefGoogle Scholar
  55. 55.
    Tomasi J, Mennucci B, Cancès E (1999) The IEF version of the PCM solvation method: an overview of a new method addressed to study molecular solutes at the QM ab initio level. J Mol Struct Theochem 464:211–226CrossRefGoogle Scholar
  56. 56.
    Cammi R, Mennucci B (1999) Linear response theory for the polarizable continuum model. J Chem Phys 110:9877–9886CrossRefGoogle Scholar
  57. 57.
    Cossi M, Barone V (2001) Time-dependent density functional theory for molecules in liquid solutions. J Chem Phys 115:4708–4717CrossRefGoogle Scholar
  58. 58.
    You ZQ, Mewes JM, Dreuw A, Herbert JM (2015) Comparison of the Marcus and Pekar partitions in the context of non-equilibrium, polarizable-continuum solvation models. J Chem Phys 143:1–14CrossRefGoogle Scholar
  59. 59.
    Segado M, Benassi E, Barone V (2015) A twist on the interpretation of the multifluorescence patterns of DASPMI. J Chem Theory Comput 11:4803–4813CrossRefGoogle Scholar
  60. 60.
    Hansch C, Leo A, Taft RW (1991) A survey of Hammett substituent constants and resonance and field parameters. Chem Rev 91:165–195CrossRefGoogle Scholar
  61. 61.
    Parimita SP, Ramshankar YV, Suresh S, Guru Row TN (2007) Redetermination of curcumin: (1E,4Z,6E)-5-hydroxy-1,7-bis(4-hydroxy-3-methoxyphenyl)hepta-1,4,6-trien-3-one. Acta Cryst E63:o860–o862Google Scholar
  62. 62.
    Chignell CF, Bilski P, Reszka KJ, Motten AG, Sik RH, Dahl TA (1994) Spectral and photochemical properties of curcumin. Photochem Photobiol 59:295–302CrossRefGoogle Scholar
  63. 63.
    Kawano S, Inohana Y, Hashi Y, Lin JM (2013) Analysis of keto–enol tautomers of curcumin by liquid chromatography/mass spectrometry. Chin Chem Lett 24:685–687CrossRefGoogle Scholar
  64. 64.
    Shen L, Ji HF (2007) Theoretical study on physicochemical properties of curcumin. Spectrochim Acta A 67:619–623CrossRefGoogle Scholar
  65. 65.
    Anjomshoa S, Namazian M, Noorbala MR (2017) Is curcumin a good scavenger of reactive oxygen species? A computational investigation. Theor Chem Acc 136:1–6CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Chemistry and BiochemistryUniversity of OklahomaNormanUSA
  2. 2.Stephenson School of Biomedical EngineeringUniversity of OklahomaNormanUSA
  3. 3.School of Science and TechnologyNazarbayev UniversityAstanaKazakhstan
  4. 4.Novosibirsk State UniversityNovosibirskRussia
  5. 5.Center for Functional NanomaterialsBrookhaven National LaboratoryUptonUSA
  6. 6.Molecular Imaging Laboratory, Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolBostonUSA

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