Advertisement

Molecular docking and dynamic studies of crepiside E beta glucopyranoside as an inhibitor of snake venom PLA2

  • Mala S. KumarEmail author
  • Amjesh R.
  • Silpa Bhaskaran
  • Delphin R. D.
  • Achuthsankar S. Nair
  • Sudhakaran P. R.
Original Paper

Abstract

Alternative treatments from plant-derived small molecules for neutralizing the venom lethality in snake envenomation are prevalent now. Elephantopus scaber, a tropical plant species has been recognized for its various pharmacological activities and especially anti-snake venom property; however, the molecular basis for this property is not understood. It is reported that snake venom PLA2 is a toxic factor with pharmacological effects independent of their catalytic activity. Here we report the inhibition of catalytic property of Cobra and Viper (group I and group II) snake venom PLA2 by the phytocompounds from E. scaber through molecular docking and dynamics studies. Initially, Lipinski’s rule, ADMET, and molecular docking studies were carried out. Our results show that among 124 phytocompounds, crepiside E (deacylcynaropicrin-3′ beta-glucopyranoside) has shown interactions with the conserved catalytic active site residues, His 48 and Asp 49, in both the PLA2s. Further, molecular dynamic simulations for 60 ns confirmed the stability of crepiside E in the active site of PLA2s and were found to be stable throughout the simulation. In order to understand the drug-likeness of crepiside E, pIC50 and MMGBSA scores were correlated by performing a linear regression analysis. Crepiside E was found to have similar chemical features to that of doxycycline, a known PLA2 inhibitor as indicated by a similarity score of 64.15%. Hence, it is concluded that crepiside E beta glucopyranoside present in Elephantopus scaber contributes to neutralizing the snake venom.

Keywords

Elephantopus scaber Snake envenomation PLA2 Crepiside E MMGBSA-pIC50 correlation Molecular docking and dynamics 

Notes

Acknowledgements

This work was supported by the Centre for Excellence in Ayur-Informatics and Computer Aided Drug Design through FAST Scheme under MHRD (No. F. 5-6/2013-TS.VII) is gratefully acknowledged. The computational facilities were provided by the Department of Computational Biology and Bioinformatics, University of Kerala is also acknowledged. The authors would also like to sincerely thank Saraswathy V., Research Scholar, Department of Computational Biology and Bioinformatics, University of Kerala for her valuable suggestions and comments.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Supplementary material

894_2019_3954_MOESM1_ESM.pdf (788 kb)
ESM 1 (PDF 787 kb)
894_2019_3954_MOESM2_ESM.pdf (270 kb)
ESM 2 (PDF 269 kb)
894_2019_3954_MOESM3_ESM.pdf (308 kb)
ESM 3 (PDF 307 kb)

References

  1. 1.
    Mukherjee AK (2012) Green medicine as a harmonizing tool to antivenom therapy for the clinical management of snakebite: the road ahead. Indian J Med Res 136(1):10PubMedPubMedCentralGoogle Scholar
  2. 2.
    Sakthivel G, Dey A, Nongalleima K, Chavali M, Rimal Isaac RS, Singh NS, Deb L (2013) In vitro and in vivo evaluation of polyherbal formulation against Russell’s viper and cobra venom and screening of bioactive components by docking studies. Evid Based Complement Alternat Med 2013:781216CrossRefGoogle Scholar
  3. 3.
    Koh DCI, Armugam A, Jeyaseelan K (2006) Snake venom components and their applications in biomedicine. Cell Mol Life Sci 63(24):3030–3041CrossRefGoogle Scholar
  4. 4.
    de Lima DC, Alvarez Abreu P, de Freitas CC, Santos DO, Borges RO, dos Santos TC, Mendes Cabral L, Rodrigues CR, Castro HC (2005) Snake venom: any clue for antibiotics and CAM? Evid Based Complement Alternat Med 2(1):39–47CrossRefGoogle Scholar
  5. 5.
    Alam JM, Qasim RASHIDA, Alam SM (1996) Enzymatic activities of some snake venoms from families Elapidae and Viperidae. Pak J Pharm Sci 9(1):37–41PubMedGoogle Scholar
  6. 6.
    Bougis PE, Marchot P, Rochat H (1986) Characterization of Elapidae snake venom components using optimized reverse-phase high-performance liquid chromatographic conditions and screening assays for alpha-neurotoxin and phospholipase A2 activities. Biochemistry 25(22):7235–7243CrossRefGoogle Scholar
  7. 7.
    Six DA, Dennis EA (2000) The expanding superfamily of phospholipase A 2 enzymes: classification and characterization. Biochim Biophys Acta Mol Cell Biol Lipids 1488(1):1–19CrossRefGoogle Scholar
  8. 8.
    Faure G, Xu H, Saul F (2010) Anticoagulant phospholipases A2 which bind to the specific soluble receptor coagulation factor Xa. Toxins and hemostasis. Springer, Dordrecht, pp 201–217CrossRefGoogle Scholar
  9. 9.
    Schaloske RH, Dennis EA (2006) The phospholipase A 2 superfamily and its group numbering system. Biochim Biophys Acta Mol Cell Biol Lipids 1761(11):1246–1259CrossRefGoogle Scholar
  10. 10.
    Warrell DA (1989) Snake venoms in science and clinical medicine 1. Russell’s viper: biology, venom and treatment of bites. Trans R Soc Trop Med Hyg 83(6):732–740CrossRefGoogle Scholar
  11. 11.
    Dennis EA, Cao J, Hsu YH, Magrioti V, Kokotos G (2011) Phospholipase A2 enzymes: physical structure, biological function, disease implication, chemical inhibition, and therapeutic intervention. Chem Rev 111(10):6130–6185CrossRefGoogle Scholar
  12. 12.
    Kini RM, Clemetson KJ, Markland FS, McLane MA, Morita T (2010) Toxins and hemostasis: from bench to bedside. Springer, Dordrecht, p 600Google Scholar
  13. 13.
    Segelke BW, Nguyen D, Chee R, Xuong NH, Dennis EA (1998) Structures of two novel crystal forms of Naja naja naja phospholipase A 2 lacking Ca 2+ reveal trimeric packing. J Mol Biol 279(1):223–232CrossRefGoogle Scholar
  14. 14.
    XingDing Z, Kini RM, Doley R, Mackessy SP (2009) Snake venom phospholipase A2 enzymes. Handbook of venoms and toxins of reptiles. Taylor and Francis, Boca Raton, pp 173–215Google Scholar
  15. 15.
    Singh G, Gourinath S, Saravanan K, Sharma S, Bhanumathi S, Betzel C, Srinivasan A, Singh TP (2005) Sequence-induced trimerization of phospholipase A2: structure of a trimeric isoform of PLA2 from common krait (Bungarus caeruleus) at 2.5 Å resolution. Acta Crystallogr Sect F: Struct Biol Cryst Commun 61(1):8–13CrossRefGoogle Scholar
  16. 16.
    Kini RM (2005) Structure–function relationships and mechanism of anticoagulant phospholipase A 2 enzymes from snake venoms. Toxicon 45(8):1147–1161CrossRefGoogle Scholar
  17. 17.
    Burke JE, Dennis EA (2009) Phospholipase A2 structure/function, mechanism, and signaling. J Lipid Res 50(Supplement):S237–S242CrossRefGoogle Scholar
  18. 18.
    Pore SM, Ramanand SJ, Patil PT, Gore AD, Pawar MP, Gaidhankar SL, Ghanghas RR (2015) A retrospective study of use of polyvalent anti-snake venom and risk factors for mortality from snake bite in a tertiary care setting. Indian J Pharm 47(3):270CrossRefGoogle Scholar
  19. 19.
    Pereañez JA, Patiño AC, Núñez V, Osorio E (2014) The biflavonoid morelloflavone inhibits the enzymatic and biological activities of a snake venom phospholipase A 2. Chem Biol Interact 220:94–101CrossRefGoogle Scholar
  20. 20.
    Sivaramakrishnan V, Ilamathi M, Ghosh KS, Sathish S, Gowda TV, Vishwanath BS, Rangappa KS, Dhananjaya BL (2016) Virtual analysis of structurally diverse synthetic analogs as inhibitors of snake venom secretory phospholipase A2. J Mol Recognit 29(1):22–32CrossRefGoogle Scholar
  21. 21.
    Sankar V, Kalirajan R, Sales FSV, Raghuraman S (2001) Anti inflammatory activity of Elephantopus scaber in albino rats. Indian J Pharm Sci 63(6):523Google Scholar
  22. 22.
    Sheeba KO, Wills PJ, Latha BK, Rajalekshmy R, Latha MS (2012) Antioxidant and antihepatotoxic efficacy of methanolic extract of Elephantopus scaber Linn in Wistar rats. Asian Pac J Trop Dis 2:S904–S908CrossRefGoogle Scholar
  23. 23.
    Geng HW, Zhang XL, Wang GC, Yang XX, Wu X, Wang YF, Ye WC, Li YL (2011) Antiviral dicaffeoyl derivatives from Elephantopus scaber. J Asian Nat Prod Res 13(7):665–669CrossRefGoogle Scholar
  24. 24.
    Bhusan SH, Ranjan SS, Subhangankar N, Rakesh S, Amrita B (2012) Nephroprotective activity of ethanolic extract of Elephantopus scaber leaves on albino rats. Int Res J Pharm 3:246–250Google Scholar
  25. 25.
    Rao G, Rao YV, Pavani S, Dasari VP (2012) Qualitative and quantitative phytochemical screening and in vitro anti oxidant and anti microbial activities of Elephantopus scaber Linn. Recent Res Sci Technol 4(4):15–20Google Scholar
  26. 26.
    Sagar R, Sahoo HB (2012) Evaluation of antiasthmatic activity of ethanolic extract of Elephantopus scaber L. leaves. Indian J Pharm 44(3):398CrossRefGoogle Scholar
  27. 27.
    Aabid K, Arun P, Mukesh P, Singh BP (2011) Evaluation of anthelmintic property of alcoholic and aqueous extract of leaves of a Elephantopus scaber Linn. Int J Pharm Life Sci 2(2):551–553Google Scholar
  28. 28.
    Yan GR, Tan Z, Wang Y, Xu ML, Yu G, Li Y, He QY (2013) Quantitative proteomics characterization on the antitumor effects of isodeoxyelephantopin against nasopharyngeal carcinoma. Proteomics 13(21):3222–3232CrossRefGoogle Scholar
  29. 29.
    Daisy P, Rayan NA, Rajathi D (2007) Hypoglycemic and other related effects of Elephantopus scaber extracts on alloxan induced diabetic rats. J Biol Sci 7(2):433–437CrossRefGoogle Scholar
  30. 30.
    Mors WB, Do Nascimento MC, Pereira BMR, Pereira NA (2000) Plant natural products active against snake bite—the molecular approach. Phytochemistry 55(6):627–642CrossRefGoogle Scholar
  31. 31.
    Sandeep VB, Dilip KJ (2012) Profile of medicinal plants with anti ophidian property. J Pharm Sci Innov 5:13–20Google Scholar
  32. 32.
    Ho WY, Ky H, Yeap SK, Rahim RA, Omar AR, Ho CL, Alitheen NB (2009) Traditional practice, bioactivities and commercialization potential of Elephantopus scaber Linn. J Med Plant Res 3(13):1212–1221Google Scholar
  33. 33.
    ACD/Structure Elucidator (2012) 2012 Free version. Advanced Chemistry Development, Inc., Toronto. www.acdlabs.com
  34. 34.
    Schrödinger Release 2015-3 (2015) LigPrep, version 3, vol 5. Schrödinger, LLC, New YorkGoogle Scholar
  35. 35.
    Small-Molecule Drug Discovery Suite 2016-3 (2016) QikProp, version 3.2. Schrödinger, LLC, New YorkGoogle Scholar
  36. 36.
    Accelrys Software Inc. (2007) Discovery studio modeling environment. Accelrys Software Inc., San DiegoGoogle Scholar
  37. 37.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242. URL: www.rcsb.org/pdb
  38. 38.
    Schrödinger L (2010) PyMOL: the PyMOL molecular graphics systemGoogle Scholar
  39. 39.
    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):221–234CrossRefGoogle Scholar
  40. 40.
    Banks JL, Beard HS, Cao Y, Cho AE, Damm W, Farid R, Felts AK, Halgren TA, Mainz DT, Maple JR, Murphy R, Philipp DM, Repasky MP, Zhang LY, Berne BJ, Friesner RA, Gallicchio E, Levy RM (2005) Integrated modeling program, applied chemical theory (IMPACT). J Comput Chem 26(16):1752–1780CrossRefGoogle Scholar
  41. 41.
    Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J Med Chem 49(21):6177–6196CrossRefGoogle Scholar
  42. 42.
    Sirin S, Kumar R, Martinez C, Karmilowicz MJ, Ghosh P, Abramov YA, Sherman W (2014) A computational approach to enzyme design: predicting ω-aminotransferase catalytic activity using docking and MM-GBSA scoring. J Chem Inf Model 54(8):2334–2346CrossRefGoogle Scholar
  43. 43.
    Berendsen HJ, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91(1-3):43–56CrossRefGoogle Scholar
  44. 44.
    Ebrahimi, Mohsen, et al. (2018) Structural Insight into Binding Mode of 9-Hydroxy Aristolochic Acid, Diclofenac and Indomethacin to PLA 2. Interdisciplinary Sciences: Computational Life Sciences 10(2):400–410Google Scholar
  45. 45.
    Hess B, Bekker H, Berendsen HJ, Fraaije JG (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18(12):1463–1472CrossRefGoogle Scholar
  46. 46.
    Maffucci I, Contini A (2013) Explicit ligand hydration shells improve the correlation between MM-PB/GBSA binding energies and experimental activities. J Chem Theory Comput 9(6):2706–2717CrossRefGoogle Scholar
  47. 47.
    Srivastava HK, Sastry GN (2012) Molecular dynamics investigation on a series of HIV protease inhibitors: assessing the performance of MM-PBSA and MM-GBSA approaches. J Chem Inf Model 52(11):3088–3098CrossRefGoogle Scholar
  48. 48.
    Incerti M, Tognolini M, Russo S, Pala D, Giorgio C, Hassan-Mohamed I, Noberini R, Pasquale EB, Vicini P, Piersanti S, Rivara S, Barocelli E, Mor M, Lodola A (2013) Amino acid conjugates of lithocholic acid as antagonists of the EphA2 receptor. J Med Chem 56(7):2936–2947CrossRefGoogle Scholar
  49. 49.
    Durante F (2014) The R book, second edition by Michael J. Crawley. Int Stat Rev 82:145–146.  https://doi.org/10.1111/insr.12051_5 CrossRefGoogle Scholar
  50. 50.
    Placzek S, Schomburg I, Chang A, Jeske L, Ulbrich M, Tillack J, Schomburg D (2017) BRENDA in 2017: new perspectives and new tools in BRENDA. Nucleic Acids Res 45(D1):D380–D388CrossRefGoogle Scholar
  51. 51.
    Selvaraj, Chandrabose, et al (2011) Tool development for Prediction of pIC 50 values from the IC 50 values-A pIC 50 value calculator. Current Trends in Biotechnology & Pharmacy 5(2):1104–1109Google Scholar
  52. 52.
    Yang LC, Wang F, Liu M (1998) A study of an endothelin antagonist from a Chinese anti-snake venom medicinal herb. J Cardiovasc Pharmacol 31:S249–S250CrossRefGoogle Scholar
  53. 53.
    Halgren T (2007) New method for fast and accurate binding site identification and analysis. Chem Biol Drug Des 69(2):146–148CrossRefGoogle Scholar
  54. 54.
    Marcussi S, Sant’Ana CD, Oliveira CZ, Quintero Rueda A, Menaldo DL, Beleboni RO, Stabeli RG, Giglio JR, Fontes M, Marcos R, Soares AM (2007) Snake venom phospholipase A2 inhibitors: medicinal chemistry and therapeutic potential. Curr Top Med Chem 7(8):743–756CrossRefGoogle Scholar
  55. 55.
    Pruzanski W, Greenwald RA, Street LP, Lauberte F, Stefanski E, Vadas P (1992) Inhibition of enzymatic activity of phospholipases A2 by minocycline and doxycycline. Biochem Pharmacol 44(6):1165–1170CrossRefGoogle Scholar
  56. 56.
    Golbraikh A, Shen M, Xiao Z, Xiao YD, Lee KH, Tropsha A (2003) Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 17(2-4):241–253CrossRefGoogle Scholar
  57. 57.
    Kuca K, Musilek K, Jun D, Zdarova-Karasova J, Nepovimova E, Soukup O, Hrabinova M, Mikler J, TCC F, EFF DC, De Castro AA, Valis M, Ramalho TC (2018) A newly developed oxime K203 is the most effective reactivator of tabun-inhibited acetylcholinesterase. BMC Pharmacol Toxicol 19(1):8CrossRefGoogle Scholar
  58. 58.
    de Lima WEA, Pereira AF, de Castro AA, da Cunha EFF, Ramalho TC (2016) Flexibility in the molecular design of acetylcholinesterase reactivators: probing representative conformations by chemometric techniques and docking/QM calculations. Lett Drug Des Discov 13(5):360–371CrossRefGoogle Scholar
  59. 59.
    Ojeda P, Ramírez D, Alzate-Morales J, Caballero J, Kaas Q, González W (2018) Computational studies of snake venom toxins. Toxins 10(1):8CrossRefGoogle Scholar
  60. 60.
    da Silva Gonçalves A, França TCC, Caetano MS, Ramalho TC (2014) Reactivation steps by 2-PAM of tabun-inhibited human acetylcholinesterase: reducing the computational cost in hybrid QM/MM methods. J Biomol Struct Dyn 32(2):301–307CrossRefGoogle Scholar
  61. 61.
    Sales TA, Marcussi S, da Cunha EF, Kuca K, Ramalho TC (2017) Can inhibitors of snake venom phospholipases A2 lead to new insights into anti-inflammatory therapy in humans? A theoretical study. Toxins 9(11):341CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Mala S. Kumar
    • 1
    Email author
  • Amjesh R.
    • 1
  • Silpa Bhaskaran
    • 1
  • Delphin R. D.
    • 1
  • Achuthsankar S. Nair
    • 1
  • Sudhakaran P. R.
    • 1
  1. 1.Department of Computational Biology and Bioinformatics, KaryavattomUniversity of KeralaThiruvananthapuramIndia

Personalised recommendations