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Journal of Computer-Aided Molecular Design

, Volume 33, Issue 4, pp 447–459 | Cite as

Significantly different effects of tetrahydroberberrubine enantiomers on dopamine D1/D2 receptors revealed by experimental study and integrated in silico simulation

  • Haixia GeEmail author
  • Yuemin Bian
  • Xibing He
  • Xiang-Qun XieEmail author
  • Junmei WangEmail author
Article

Abstract

Tetrahydroberberrubine (TU), an active tetrahydroprotoberberines (THPBs), is gaining increasing popularity as a potential candidate for treatment of anxiety and depression. One of its two enantiomers, l-TU, has been reported to be an antagonist of both D1 and D2 receptors, but the functional activity of the other enantiomer, d-TU, is still unknown. In this study, experiments were combined with in silico molecular simulations to (1) confirm and discover the functional activities of l-TU and d-TU, and (2) systematically evaluate the molecular mechanisms beyond the experimental observations. l-TU proved to be an antagonist of both D1 and D2 receptors (IC50 = 385 nM and 985 nM, respectively), while d-TU shows no affinity against either D1 or D2 receptor, based on the cAMP assay (D1 receptor) and calcium flux assay (D2 receptor). Results from both flexible-ligand docking studies and molecular dynamic (MD) simulations provided insights at the atomic level. The l-TU-bound structures predicted by MD (1) undergo an outward rotation of the extracellular helical bundles; (2) have an enlarged orthosteric binding pocket; and (3) have a central toggle switch that is prevented from rotating freely. These features are unique to the l-TU enantiomer and provide an explanation for its antagonistic behavior toward both D1 and D2 receptors. The present study provides new sight on the structural and functional relationships of l-TU and d-TU binding to dopamine receptors, and provides guidance to the rational design of novel molecules targeting these two dopamine receptors in the future.

Keywords

Tetrahydroberberrubine Dopamine receptors Antagonistic activity Molecular dynamics simulation 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC NO. 21302052) and the National Institutes of Health of USA (R01-GM079383, R21-GM097617, P30-DA035778A1). Computational support from the Center for Research Computing of University of Pittsburgh, Pittsburgh Supercomputing Center (CHE180028P) and the Extreme Science and Engineering Discovery Environment (CHE090098, MCB170099 and MCB180045P), is acknowledged.

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interest.

Supplementary material

10822_2019_194_MOESM1_ESM.docx (2.7 mb)
Supplementary material 1 (DOCX 2728 KB)

References

  1. 1.
    Katritch V, Cherezov V, Stevens RC (2013) Structure-function of the G protein-coupled receptor superfamily. Annu Rev Pharmacol Toxicol 53:531–556.  https://doi.org/10.1146/annurev-pharmtox-032112-135923 Google Scholar
  2. 2.
    Hauser AS, Attwood MM, Rask-Andersen M, Schioth HB, Gloriam DE (2017) Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev Drug Discov 16(12):829–842.  https://doi.org/10.1038/nrd.2017.178 Google Scholar
  3. 3.
    Isberg V, Mordalski S, Munk C, Rataj K, Harpsoe K, Hauser AS, Vroling B, Bojarski AJ, Vriend G, Gloriam DE (2016) GPCRdb: an information system for G protein-coupled receptors. Nucleic Acids Res 44(D1):D356–D364.  https://doi.org/10.1093/nar/gkv1178 Google Scholar
  4. 4.
    Cooke RM, Brown AJ, Marshall FH, Mason JS (2015) Structures of G protein-coupled receptors reveal new opportunities for drug discovery. Drug Discov Today 20(11):1355–1364.  https://doi.org/10.1016/j.drudis.2015.08.003 Google Scholar
  5. 5.
    Beaulieu JM, Gainetdinov RR (2011) The physiology, signaling, and pharmacology of dopamine receptors. Pharmacol Rev 63(1):182–217.  https://doi.org/10.1124/pr.110.002642 Google Scholar
  6. 6.
    Jin G (1987) l(−)Tetrahydropalmatine and its analogues as new dopamine receptor antagonists. Trends Pharmacol Sci 8(3):81–82Google Scholar
  7. 7.
    Yang K, Jin G, Wu J (2007) The neuropharmacology of (−)-stepholidine and its potential applications. Curr Neuropharmacol 5(4):289–294Google Scholar
  8. 8.
    Ellenbroek BA, Zhang XX, Jin GZ (2006) Effects of (−)stepholidine in animal models for schizophrenia. Acta Pharmacol Sin 27(9):1111–1118.  https://doi.org/10.1111/j.1745-7254.2006.00365.x Google Scholar
  9. 9.
    Ge HX, Zhang J, Chen L, Kou JP, Yu BY (2013) Chemical and microbial semi-synthesis of tetrahydroprotoberberines as inhibitors on tissue factor procoagulant activity. Bioorganic Med Chem 21(1):62–69.  https://doi.org/10.1016/j.bmc.2012.11.002 Google Scholar
  10. 10.
    Yu X, Yu S, Chen L, Liu H, Zhang J, Ge H, Zhang Y, Yu B, Kou J (2016) Tetrahydroberberrubine attenuates lipopolysaccharide-induced acute lung injury by down-regulating MAPK, AKT, and NF-kappaB signaling pathways. Biomed Pharmacother 82:489–497.  https://doi.org/10.1016/j.biopha.2016.05.025 Google Scholar
  11. 11.
    Zhao W, Ge H, Liu K, Chen X, Zhang J, Liu B (2017) Nandinine, a derivative of berberine, inhibits inflammation and reduces insulin resistance in adipocytes via regulation of AMP-kinase activity. Planta Med 83(3–04):203–209.  https://doi.org/10.1055/s-0042-110576 Google Scholar
  12. 12.
    Mi GY, Liu S, Zhang J, Liang H, Gao Y, Li N, Yu B, Yang H, Yang Z (2017) Levo-tetrahydroberberrubine produces anxiolytic-like effects in mice through the 5-HT1A Receptor. PLoS ONE 12(1):1–13.  https://doi.org/10.1371/journal.pone.0168964.g001 Google Scholar
  13. 13.
    Yang Z, Yu BY, Zhang J, Li N, Ge H, Fang T, Jin P (2011) Application of tetrahydroberberrubine in preparing antianxiety agents and antidepressants. CN 101972252 AGoogle Scholar
  14. 14.
    Mo JG, Yang YS, Shen JS, Jin GZ, Zhen XC (2007) Recent developments in studies of l-stepholidine and its analogs: chemistry, pharmacology and clinical implications. Curr Med Chem 14(28):2996–3002Google Scholar
  15. 15.
    Zhang H, Xue L, Tong J, Zhang C (2010) Study on the chemical resolution of tetrahydroberberrubine. Yaoxue Jinzhan 34(10):459–462Google Scholar
  16. 16.
    Wang S, Che T, Levit A, Shoichet BK, Wacker D, Roth BL (2018) Structure of the D2 dopamine receptor bound to the atypical antipsychotic drug risperidone. Nature 555(7695):269Google Scholar
  17. 17.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242Google Scholar
  18. 18.
    Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234.  https://doi.org/10.1007/s10822-013-9644-8 Google Scholar
  19. 19.
    Consortium TU (2017) UniProt: the universal protein knowledgebase. Nucleic Acids Res 45:D158–D169Google Scholar
  20. 20.
    Boratyn GM, Camacho C, Cooper PS, Coulouris G, Fong A, Ma N, Madden TL, Matten WT, McGinnis SD, Merezhuk Y, Raytselis Y, Sayers EW, Tao T, Ye J, Zaretskaya I (2013) BLAST: a more efficient report with usability improvements. Nucleic Acids Res 41:W29–W33Google Scholar
  21. 21.
    Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen MY, Pieper U, Sali A (2014) Comparative protein structure modeling using MODELLER. Curr Protoc Bioinform 47:5.6.1–5.6.32Google Scholar
  22. 22.
    Webb B, Sali A (2014) Protein structure modeling with MODELLER. Methods Mol Biol 1137:1–15Google Scholar
  23. 23.
    Shen MY, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15(11):2507–2524Google Scholar
  24. 24.
    Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7(1):95–99Google Scholar
  25. 25.
    Laskowski RA, Macarthur MW, Moss DS, Thornton J (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291Google Scholar
  26. 26.
    Bian Y-m, He X-b, Jing Y-k, Wang L-r, Wang J-m, Xie X-Q (2018) Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics-knowledgebase network analysis and integrated in silico modeling and simulation. Acta Pharmacologica Sinica 40:374Google Scholar
  27. 27.
    Lovell SC, Davis IW, Arendall WB, de Bakker PI, Word JM, Prisant MG, Richardson JS, Richardson DC (2003) Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins 50(3):437–450Google Scholar
  28. 28.
    Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inform Model 45(1):177–182Google Scholar
  29. 29.
    Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, Kruger FA, Light Y, Mak L, McGlinchey S, Nowotka M, Papadatos G, Santos R, Overington JP (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42 (Database issue):D1083–D1090.  https://doi.org/10.1093/nar/gkt1031 Google Scholar
  30. 30.
    Bian Y, Feng Z, Yang P, Xie XQ (2017) Integrated in silico fragment-based drug design: case study with allosteric modulators on metabotropic glutamate receptor 5. AAPS J 19(4):1235–1248.  https://doi.org/10.1208/s12248-017-0093-5 Google Scholar
  31. 31.
    Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49(21):6177–6196Google Scholar
  32. 32.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoil EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749Google Scholar
  33. 33.
    Halgen TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750–1759Google Scholar
  34. 34.
    Schrödinger (2018) Release 2018-1: LigPrep. Schrödinger, LLC, New York, NYGoogle Scholar
  35. 35.
    Harder E, Damm W, Maple J, Wu C, Reboul M, Xiang JY, Wang L, Lupyan D, Dahlgren MK, Knight JL, Kaus JW, Cerutti DS, Krilov G, Jorgensen WL, Abel R, Friesner RA (2016) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12(1):281–296.  https://doi.org/10.1021/acs.jctc.5b00864 Google Scholar
  36. 36.
    Jo S, Kim T, Iyer VG, Im W (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29(11):1859–1865.  https://doi.org/10.1002/jcc.20945 Google Scholar
  37. 37.
    Jo S, Lim JB, Klauda JB, Im W (2009) CHARMM-GUI membrane builder for mixed bilayers and its application to yeast membranes. Biophys J 97(1):50–58.  https://doi.org/10.1016/j.bpj.2009.04.013 Google Scholar
  38. 38.
    Wang JM, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25(2):247–260Google Scholar
  39. 39.
    Wang JM, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174Google Scholar
  40. 40.
    Jakalian ABB, Jack DB, Bayly CI (2000) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method. J Comput Chem 21:132–146Google Scholar
  41. 41.
    Bayly CI, Cieplak P, Cornell W, Kollman PA (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J Phys Chem 97(40):10269–10280Google Scholar
  42. 42.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE et al (2016) Gaussian 16, Revision A, 03. Gaussian, Inc., WallingfordGoogle Scholar
  43. 43.
    James A. Maier CM, Koushik K, Lauren W, Kevin EH, Carlos S (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11(8):3696–3713Google Scholar
  44. 44.
    Dickson CJ, Madej BD, Skjevik AA, Betz RM, Teigen K, Gould IR, Walker RC (2014) Lipid14: the amber lipid force field. J Chem Theory Comput 10(2):865–879.  https://doi.org/10.1021/ct4010307 Google Scholar
  45. 45.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935.  https://doi.org/10.1063/1.445869 Google Scholar
  46. 46.
    Gotz AW, Williamson MJ, Xu D, Poole D, Grand SL, Walker RC (2012) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. J Chem Theory Comput 8(5):1542–1555.  https://doi.org/10.1021/ct200909j Google Scholar
  47. 47.
    Salomon-Ferrer R, Gotz AW, Poole D, Grand SL, Walker RC (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh ewald. J Chem Theory Comput 9(9): 3878–3888.  https://doi.org/10.1021/ct400314y Google Scholar
  48. 48.
    Case DA, Betz RM, Cerutti DS et al (2016) AMBER. University of California, San FranciscoGoogle Scholar
  49. 49.
    Darden TY, Pedersen DL (1993) Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. J Chem Phys 98(12):10089–10092.  https://doi.org/10.1063/1.464397 Google Scholar
  50. 50.
    Essmann UP, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103(19):8577–8593Google Scholar
  51. 51.
    Jean-Paul R, Ciccotti G, Herman JCB (1977) Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341Google Scholar
  52. 52.
    Jayashree ST, Cheatham TE, Piotr C, Peter AK, David AC (1998) Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate-DNA helices. J Am Chem Soc 120(37):9401–9409Google Scholar
  53. 53.
    Hou TJ, Wang JM, Li YY, Wang W (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51(1):69–82Google Scholar
  54. 54.
    Rocchia W, ALEXOV E, Honig B (2001) Extending the applicability of the nonlinear Poisson-Boltzmann equation: multiple dielectric constants and multivalent ions. J Phys Chem B 105(28):6507–6514Google Scholar
  55. 55.
    Ge HX, Zhang J, Dong Y, Cui K, Yu BY (2012) Unique biocatalytic resolution of racemic tetrahydroberberrubine via kinetic glycosylation and enantio-selective sulfation. Chem Commun 48(49):6127.  https://doi.org/10.1039/c2cc32175k Google Scholar
  56. 56.
    Iwasa K, Cui W, Takahashi T, Nishiyama Y, Kamigauchi M, Koyama J, Takeuchi A, Moriyasu M, Takeda K (2010) Biotransformation of phenolic tetrahydroprotoberberines in plant cell cultures followed by LC–NMR, LC–MS, and LC–CD. J Nat Product 73(2):115–122Google Scholar
  57. 57.
    Andra FA, Sali A (2003) Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol 374:461–491Google Scholar
  58. 58.
    Shen MY, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein 15(11):2507–2524.  https://doi.org/10.1110/ps.062416606 Google Scholar
  59. 59.
    Ramachandran GN, Ramakrishman C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99Google Scholar
  60. 60.
    Jain AN (2007) Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J Comput Aided Mol Des 21(5):281–306.  https://doi.org/10.1007/s10822-007-9114-2 Google Scholar
  61. 61.
    Wang JM, Ge YB, Xie XQ (2019) Development and testing of druglike screening libraries. J Chem Inf Model 59(1):53–65Google Scholar
  62. 62.
    Trzaskowski B, Latek D, Yuan S, Ghoshdastider U, Debinski A, Filipek S (2012) Action of molecular switches in GPCRs—theoretical and experimental studies. Curr Med Chem 19(8):1090–1109Google Scholar
  63. 63.
    Hua T, Vemuri K, Nikas SP, Laprairie RB, Wu Y, Qu L, Pu M, Korde A, Jiang S, Ho JH, Han GW, Ding K, Li X, Liu H, Hanson MA, Zhao S, Bohn LM, Makriyannis A, Stevens RC, Liu ZJ (2017) Crystal structures of agonist-bound human cannabinoid receptor CB1. Nature 547(7664):468–471.  https://doi.org/10.1038/nature23272 Google Scholar

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Authors and Affiliations

  1. 1.School of Life SciencesHuzhou UniversityHuzhouChina
  2. 2.Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of PharmacyUniversity of PittsburghPittsburghUSA
  3. 3.NIDA National Center of Excellence for Computational Drug Abuse Research, Drug Discovery InstituteUniversity of PittsburghPittsburghUSA
  4. 4.Department of Computational Biology and Structural Biology, School of MedicineUniversity of PittsburghPittsburghUSA

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