Advertisement

Journal of Molecular Modeling

, 25:275 | Cite as

In silico evaluation of condensed and hydrolysable tannins as inhibitors of pancreatic α-amylase

  • Paulo Sérgio Alves Bueno
  • Camila Gabriel Kato-Schwartz
  • Diego de Souza Lima
  • Adelar Bracht
  • Rosane Marina Peralta
  • Flavio Augusto Vicente SeixasEmail author
Original Paper
  • 50 Downloads

Abstract

Amylases are interesting targets for antidiabetic drugs because their inhibition is able to lower glycaemia without the need of hormonal control, as promoted by insulin or glibenclamide. In this context, the comparison between the binding features of α-amylases with their substrate and known inhibitors may provide insights aiming at the discovery of new antidiabetic drugs. In this work, the structure of the porcine pancreatic α-amylase was modelled with the acarbose pentasaccharide inhibitor, and used in structure-based virtual screening simulations based on a library containing the structures of amylose (AMY), acarbose (ACA) and the more representative structures of condensed tannin (CTN) and hydrolysable tannin (HTN). After validation of the methodology by redocking (mean rmsd ~ 0.8 Å), the scores provided by programs AutoDock/Molegro were contradictory (− 1.5/− 23.3; − 3.5/− 24.6; − 4.3/− 14.6; −/− 19.5 for AMY, ACA, CTN and HTN respectively), indicating that a more sensitive methodology was necessary. The ΔGbinding was calculated by the molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) method, which indicated that the HTN, ACA and CTN had higher affinities for the enzyme regarding the AMY substrate, with values of − 350.0, − 346.2, − 320.5 and − 209.2 kJ mol−1, respectively. The predicted relative affinities of HTN and CTN are in agreement with those obtained experimentally. The results provided useful information for the characterization of tannin binding to α-amylase, which can be applied in future studies aiming at finding new hypoglycaemic molecules among natural products.

Keywords

Enzyme Diabetes Natural products MM-PBSA Docking 

Notes

Acknowledgements

The authors would like to acknowledge LNCC for computational facilities.

Funding information

This work was financially supported by Fundação Araucária (grant numbers 147/14 and 40/16), Coordination for the Improvement of Higher Education Personnel–Brazil (CAPES, cód 001) and National Council for Scientific and Technological Development–Brazil (CNPq grant number 305960/2015-6); CENAPAD/SP (Project number 520).

Supplementary material

894_2019_4176_MOESM1_ESM.docx (1.7 mb)
ESM 1 (DOCX 1716 kb)

References

  1. 1.
    Boehlke C, Zierau O, Hannig C (2015) Salivary amylase - the enzyme of unspecialized euryphagous animals. Arch Oral Biol 60(8):1162–1176.  https://doi.org/10.1016/j.archoralbio.2015.05.008 CrossRefPubMedGoogle Scholar
  2. 2.
    Lynge Pedersen AM, Belstrom D (2019) The role of natural salivary defences in maintaining a healthy oral microbiota. J Dent 80(Suppl 1):S3–S12.  https://doi.org/10.1016/j.jdent.2018.08.010 CrossRefPubMedGoogle Scholar
  3. 3.
    Saeedi M, Hadjiakhondi A, Nabavi SM, Manayi A (2017) Heterocyclic compounds: effective alpha-amylase and alpha-glucosidase inhibitors. Curr Top Med Chem 17(4):428–440.  https://doi.org/10.2174/1568026616666160824104655 CrossRefPubMedGoogle Scholar
  4. 4.
    IDF Diabetes Atlas (2017) 8th ed. International Diabetes Federation, Brussels. http://www.diabetesatlas.org. Accessed 04/23/2019
  5. 5.
    Forouhi NG, Wareham NJ (2019) Epidemiology of diabetes. Medicine 41(1):22–27CrossRefGoogle Scholar
  6. 6.
    Teng H, Chen L (2017) Alpha-glucosidase and alpha-amylase inhibitors from seed oil: a review of liposoluble substance to treat diabetes. Crit Rev Food Sci Nutr 57(16):3438–3448.  https://doi.org/10.1080/10408398.2015.1129309 CrossRefPubMedGoogle Scholar
  7. 7.
    Taylor R (2012) Insulin resistance and type 2 diabetes. Diabetes 61(4):778–779.  https://doi.org/10.2337/db12-0073 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Fischer S, Patzak A, Rietzsch H, Schwanebeck U, Kohler C, Wildbrett J, Fuecker K, Temelkova-Kurktschiev T, Hanefeld M (2003) Influence of treatment with acarbose or glibenclamide on insulin sensitivity in type 2 diabetic patients. Diabetes Obes Metab 5(1):38–44.  https://doi.org/10.1046/j.1463-1326.2003.00239.x CrossRefPubMedGoogle Scholar
  9. 9.
    Ueno H, Tsuchimochi W, Wang HW, Yamashita E, Tsubouchi C, Nagamine K, Sakoda H, Nakazato M (2015) Effects of miglitol, acarbose, and sitagliptin on plasma insulin and gut peptides in type 2 diabetes mellitus: a crossover study. Diabetes Ther 6(2):187–196.  https://doi.org/10.1007/s13300-015-0113-3 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Hanefeld M, Cagatay M, Petrowitsch T, Neuser D, Petzinna D, Rupp M (2004) Acarbose reduces the risk for myocardial infarction in type 2 diabetic patients: meta-analysis of seven long-term studies. Eur Heart J 25(1):10–16.  https://doi.org/10.1016/S0195-668X(03)00468-8 CrossRefPubMedGoogle Scholar
  11. 11.
    Tundis R, Loizzo MR, Menichini F (2010) Natural products as alpha-amylase and alpha-glucosidase inhibitors and their hypoglycaemic potential in the treatment of diabetes: an update. Mini-Rev Med Chem 10(4):315–331.  https://doi.org/10.2174/138955710791331007 CrossRefPubMedGoogle Scholar
  12. 12.
    Funke I, Melzig M (2005) Effect of different phenolic compounds on alpha-amylase activity: screening by microplate-reader based kinetic assay. Die Pharmazie 60(10):796–797 ingentaconnect.com/contentone/govi/pharmaz/2005/00000060/00000010/art00017 PubMedGoogle Scholar
  13. 13.
    Zhang B, Wang L, Luo L, King MW (2014) Natural dye extracted from Chinese gall–the application of color and antibacterial activity to wool fabric. J Clean Prod 80:204–210.  https://doi.org/10.1016/j.jclepro.2014.05.100 CrossRefGoogle Scholar
  14. 14.
    Kusano R, Ogawa S, Matsuo Y, Tanaka T, Yazaki Y, Kouno I (2010) α-Amylase and lipase inhibitory activity and structural characterization of acacia bark proanthocyanidins. J Nat Prod 74(2):119–128.  https://doi.org/10.1021/np100372t CrossRefPubMedGoogle Scholar
  15. 15.
    Kato CG, Goncalves GA, Peralta RA, Seixas FAV, de Sa-Nakanishi AB, Bracht L, Comar JF, Bracht A, Peralta RM (2017) Inhibition of alpha-amylases by condensed and hydrolysable tannins: focus on kinetics and hypoglycemic actions. Enzyme Res 2017:5724902.  https://doi.org/10.1155/2017/5724902 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Larson SB, Day JS, McPherson A (2010) X-ray crystallographic analyses of pig pancreatic alpha-amylase with limit dextrin, oligosaccharide, and alpha-cyclodextrin. Biochemistry 49(14):3101–3115.  https://doi.org/10.1021/bi902183w CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Li C, Begum A, Numao S, Park KH, Withers SG, Brayer GD (2005) Acarbose rearrangement mechanism implied by the kinetic and structural analysis of human pancreatic alpha-amylase in complex with analogues and their elongated counterparts. Biochemistry 44(9):3347–3357.  https://doi.org/10.1021/bi048334e CrossRefPubMedGoogle Scholar
  18. 18.
    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26(16):1781–1802.  https://doi.org/10.1002/jcc.20289 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Mackerell Jr AD, Feig M, Brooks 3rd CL (2004) Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J Comput Chem 25(11):1400–1415.  https://doi.org/10.1002/jcc.20065 CrossRefPubMedGoogle Scholar
  20. 20.
    Zoete V, Cuendet MA, Grosdidier A, Michielin O (2011) SwissParam: a fast force field generation tool for small organic molecules. J Comput Chem 32(11):2359–2368.  https://doi.org/10.1002/jcc.21816 CrossRefPubMedGoogle Scholar
  21. 21.
    Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49(11):3315–3321.  https://doi.org/10.1021/jm051197e CrossRefPubMedGoogle Scholar
  22. 22.
    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791.  https://doi.org/10.1002/jcc.21256 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Dallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx. Chemical biology. Humana Press, New York, pp 243–250.  https://doi.org/10.1007/978-1-4939-2269-7_19 CrossRefGoogle Scholar
  24. 24.
    Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4(3):435–447.  https://doi.org/10.1021/Ct700301q CrossRefGoogle Scholar
  25. 25.
    Scott WRP, Hunenberger PH, Tironi IG, Mark AE, Billeter SR, Fennen J, Torda AE, Huber T, Kruger P, van Gunsteren WF (1999) The GROMOS biomolecular simulation program package. J Phys Chem A 103(19):3596–3607.  https://doi.org/10.1021/Jp984217f CrossRefGoogle Scholar
  26. 26.
    Schuttelkopf AW, van Aalten DMF (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr Sect D 60:1355–1363.  https://doi.org/10.1107/S0907444904011679 CrossRefGoogle Scholar
  27. 27.
    Baker NA, Sept D, Joseph S, Holst MJ, McCammon JA (2001) Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci USA 98(18):10037–10041.  https://doi.org/10.1073/pnas.181342398 CrossRefPubMedGoogle Scholar
  28. 28.
    Kumari R, Kumar R, Lynn A, Consort OSDD (2014) g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 54(7):1951–1962.  https://doi.org/10.1021/Ci500020m CrossRefGoogle Scholar
  29. 29.
    da Silva SM, Koehnlein EA, Bracht A, Castoldi R, de Morais GR, Baesso ML, Peralta RA, de Souza CGM, de Sá-Nakanishi AB, Peralta RM (2014) Inhibition of salivary and pancreatic α-amylases by a pinhão coat (Araucaria angustifolia) extract rich in condensed tannin. Food Res Int 56:1–8.  https://doi.org/10.1016/j.foodres.2013.12.004 CrossRefGoogle Scholar
  30. 30.
    Ambrose GO, Afees OJ, Nwamaka NC, Simon N, Oluwaseun AA, Soyinka T, Oluwaseun AS, Bankole S (2018) Selection of luteolin as a potential antagonist from molecular docking analysis of EGFR mutant. Bioinformation 14(5):241–247.  https://doi.org/10.6026/97320630014241 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Shakeel E, Akhtar S, Khan MKA, Lohani M, Arif JM, Siddiqui MH (2017) Molecular docking analysis of aplysin analogs targeting survivin protein. Bioinformation 13(9):293–300.  https://doi.org/10.6026/97320630013293 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Gutierrez-de-Teran H, Aqvist J (2012) Linear interaction energy: method and applications in drug design. Methods Mol Biol 819:305–323.  https://doi.org/10.1007/978-1-61779-465-0_20 CrossRefPubMedGoogle Scholar
  33. 33.
    Grossfield A, Patrone PN, Roe DR, Schultz AJ, Siderius DW, Zuckerman DM (2018) Best practices for quantification of uncertainty and sampling quality in molecular simulations [Article v1. 0]. Living J Comput Mol Sci 1(1):5067.  https://doi.org/10.33011/livecoms.1.1.5067 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Kato-Schwartz CG, Bracht F, Gonçalves GA, Soares AA, Vieira TF, Brugnari T, Bracht A, Peralta RM (2018) Inhibition of α-amylases by pentagalloil glucose: kinetics, molecular dynamics and consequences for starch absorption. J Funct Foods 44:265–273.  https://doi.org/10.1016/j.jff.2018.03.025 CrossRefGoogle Scholar
  35. 35.
    g_mmpbsa Forum (2014) https://groups.google.com/forum/#!topic/g_mmpbsa/YR2fuPMifjM. Accessed 07/10/2018
  36. 36.
    Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discovery 10(5):449–461.  https://doi.org/10.1517/17460441.2015.1032936 CrossRefGoogle Scholar
  37. 37.
    Souza AH, Correa RC, Barros L, Calhelha RC, Santos-Buelga C, Peralta RM, Adelar B, Matsushita M, Ferreira IC (2015) Phytochemicals and bioactive properties of Ilex paraguariensis: an in-vitro comparative study between the whole plant, leaves and stems. Food Res Int 78:286286–286294.  https://doi.org/10.1016/j.foodres.2015.09.032 CrossRefGoogle Scholar
  38. 38.
    Gonçalves GA, de Sá-Nakanishi AB, Comar JF, Bracht L, Dias MI, Barros L, Peralta RM, Ferreira ICFR, Bracht A (2018) Water soluble compounds of rosmarinus officinalis l. improve the oxidative and inflammatory states of rats with adjuvant-induced arthritis. Food Funct 9(4):2328–2340.  https://doi.org/10.1039/c7fo01928a CrossRefGoogle Scholar
  39. 39.
    Correa RC, Peralta RM, Haminiuk CWI, Maciel GM, Bracht A, Ferreira ICF (2018) New phytochemicals as potential human anti-aging compounds: reality, promise, and challenges. Crit Rev Food Sci Nutr 58(6):942–957.  https://doi.org/10.1080/10408398.2016.1233860 CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Paulo Sérgio Alves Bueno
    • 1
  • Camila Gabriel Kato-Schwartz
    • 1
    • 2
  • Diego de Souza Lima
    • 3
  • Adelar Bracht
    • 1
    • 2
  • Rosane Marina Peralta
    • 1
    • 2
  • Flavio Augusto Vicente Seixas
    • 3
    Email author
  1. 1.Department of BiochemistryUniversidade Estadual de MaringáMaringáBrazil
  2. 2.Post-Graduate Program of Food ScienceUniversidade Estadual de MaringáMaringáBrazil
  3. 3.Department of TechnologyUniversidade Estadual de Maringá, UEMUmuaramaBrazil

Personalised recommendations