Advertisement

Journal of Molecular Modeling

, 25:260 | Cite as

Molecular modeling of four Dermaseptin-related peptides of the gliding tree frog Agalychnis spurrelli

  • Sebastián Cuesta
  • Felipe Gallegos
  • Josefa Arias
  • Fernanda Pilaquinga
  • Ailín Blasco-Zúñiga
  • Carolina Proaño-Bolaños
  • Miryan Rivera
  • Lorena MenesesEmail author
Original Paper
Part of the following topical collections:
  1. QUITEL 2018 (44th Congress of Theoretical Chemists of Latin Expression)

Abstract

In this research, we present a preliminary computational study of four Dermaseptin-related peptides from the skin exudate of the gliding tree frog Agalychnis spurrelli. Experimentally, the amino acid sequence of these peptides was elucidated through molecular cloning and tandem mass spectrometry and synthetic peptides were assayed against E. coli, S. aureus, and C. albicans to determine their antimicrobial properties. With the sequences on hand, a computational study of the structures was carried out, obtaining their physicochemical properties, secondary structure, and their similarity to other known peptides. A molecular docking study of these peptides was also performed against cell membrane and several enzymes are known to be vital for the organisms. Results showed that Dermaseptin-related peptides are α-helical cationic peptides with an isoelectric point above 9.70 and a positive charge of physiological pH. Introducing theses peptides in a database, it was determined that their identity compared with known peptides range from 36 to 82% meaning these four Dermaseptins are novel peptides. This preliminary study of molecular docking suggests the mechanism of action of this peptide is not given by the inhibition of essential enzymatic pathways, but by cell lysis.

Graphical abstract

Keywords

Agalychnis spurrelli Antimicrobial peptides Dermaseptins Molecular docking 

Notes

Acknowledgments

CPB obtained the peptides sequences in Queen’s University Belfast funded by the Natural drug discovery group and the Ecuadorian Secretariat of Science and Technology (SENESCYT) through a scholarship. Collection and rearing of frogs in Ecuador were done under permits of the Ecuadorian Ministerio de Ambiente (MAE): 001-13 IC-FAU-DNB/MA, 003-11 IC-FAU-DNB/MA, 005-15 IC-FAU-DNB/MA (Issued to the Centro Jambatu). This research is part of the project “Conservation of Ecuadorian amphibian diversity and sustainable use of its genetic resources”, which involves MAE, Ikiam-Universidad Regional Amazónica, Queen’s University Belfast, and Centro Jambatu, and help of the Global Environmental Facility (GEF) and “Programa de las Naciones Unidas para el Desarrollo” (PNUD).

Funding information

This research was funded by two research grants to L.M and M.R. of the Dirección General Académica of the Pontificia Universidad Católica del Ecuador, projects: QINV0035-IINV529010100 and QIV0046-IINV529010100.

Supplementary material

894_2019_4141_MOESM1_ESM.docx (14 kb)
ESM 1 (DOCX 14 kb)

References

  1. 1.
    Conlon JM (2012) The potential of frog skin antimicrobial peptides for development into therapeutically valuable anti-infective agents, Chapter 3 In Small Wonders. Peptides for Disease Control; ACS Symposium Series; American Chemical Society, WashingtonGoogle Scholar
  2. 2.
    Lacombe C, Piesse C, Sagan S, Combadière C, Rosenstein Y, Auvynet C (2015) Pachymodulin, a new functional formyl peptide receptor 2 peptidic ligand isolated from frog skin has Janus-like immunomodulatory capacities. J Med Chem 58:1089–1099.  https://doi.org/10.1021/jm501018q CrossRefPubMedGoogle Scholar
  3. 3.
    Holthausen D, Lee S, Kumar V, Bouvier N, Krammer F, Ellebedy A, Wrammert J, Lowen A, George S, Pillai M, Jacob J (2017) An amphibian host defense peptide is virucidal for human H1 hemagglutinin-bearing influenza viruses. Immunity 46:587–595.  https://doi.org/10.1016/j.immuni.2017.03.018 CrossRefPubMedGoogle Scholar
  4. 4.
    Scorciapino M, Manzo G, Rinaldi A, Sanna R, Casu M, Pantic J, Lukic M, Conlon J (2013) Conformational analysis of the frog skin peptide, plasticin-L1 and its effects on the production of proinflammatory cytokines by macrophages. Biochemistry 52(41):7231–7241.  https://doi.org/10.1021/bi4008287 CrossRefPubMedGoogle Scholar
  5. 5.
    Manzo G, Casu M, Rinaldi M, Montaldo N, Luganini A, Gribaudo G, Scorciapino M (2014) Folded structure and insertion depth of the frog-skin antimicrobial peptide esculentin-1b (1–18) in the presence of differently charged membrane-mimicking micelles. J Nat Prod 77(11):2410–2417.  https://doi.org/10.1021/np5004406 CrossRefPubMedGoogle Scholar
  6. 6.
    Samgina T, Vorontsov E, Gorshkov V, Hakalehto E, Hanninen O, Zubarev R, Lebedevl A (2012) Composition and antimicrobial activity of the skin peptidome of Russian Brown Frog Rana temporaria. J Proteome Res 11(12):6213–6222.  https://doi.org/10.1021/pr300890m CrossRefPubMedGoogle Scholar
  7. 7.
    Marani M, Dourado F, Quelemes P, Rodrigues A, Gomes M, Alves E, Costa L, Rodrigues A, Barroso E, Eaton P, Figueiró J, Bentes R, Delerue-Matos C, Leite J (2015) Characterization and biological activities of ocellatin peptides from the skin secretion of the frog Leptodactylus pustulatus. J Nat Prod 78(7):1495–1504.  https://doi.org/10.1021/np500907t CrossRefPubMedGoogle Scholar
  8. 8.
    Stutz K, Muller A, Hiss J, Schneider P, Blatter M, Pfeiffer B, Posselt G, Kanfer G, Kornmann B, Wrede P, Altmann K, Wessler S, Schneider G (2017) Peptide-membrane interaction between targeting and lysis. ACS Chem Biol 12(9):2254–2259.  https://doi.org/10.1021/acschembio.7b00504 CrossRefPubMedGoogle Scholar
  9. 9.
    Ortega-Andrade HM (2008) Agalychnis spurrelli Boulenger (Anura: Hylidae): variación, distribución y sinonimia. Papéis Avulsos de Zoologia 48:103–1117CrossRefGoogle Scholar
  10. 10.
    Duellman WE (2001) Hylid Frogs of Middle America. Society for the Study of Amphibians and Reptiles, IthacaGoogle Scholar
  11. 11.
    Gray AR (1997) Observations on the biology of Agalychnis spurrelli from the Caribbean lowlands of Costa Rica. Observaciones sobre la biología de Agalychnis spurrelli de las tierras bajas del caribe de Costa Rica. Journal of the International Herpetological Society 22:61–70Google Scholar
  12. 12.
    MECN (2010) Serie Herpetofauna del Ecuador: El Choco Esmeraldeño. Monografía. Museo Ecuatoriano de Ciencias Naturales. Quito-Ecuador 5:1–232Google Scholar
  13. 13.
    Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A (2005) Protein Identification and Analysis Tools on the ExPASy Server. In: En Walker J (ed) The Proteomics Protocols Handbook. Springer, LondonGoogle Scholar
  14. 14.
    Innovagen (2015) Peptide property calculator, Innovagen AB, https://pepcalc.com/
  15. 15.
    Bio-Synthesis. (2018). Peptide Property Calculator, Bio-Synthesis Inc. https://www.biosyn.com/peptidepropertycalculatorlanding.aspx
  16. 16.
    Drozdetskiy A, Cole C, Procter J, Barton GJ (2015) JPred4: a protein secondary structure prediction server. Nucleic Acids Res 43:389–394CrossRefGoogle Scholar
  17. 17.
    Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195–202CrossRefGoogle Scholar
  18. 18.
    The PyMOL Molecular Graphics System (2017) Version 2.0 Schrödinger, LLC. https://pymol.org/2/support.html?#citing
  19. 19.
  20. 20.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Fox DJ (2009) Gaussian 09. Revision A.01. Gaussian, Inc, Wallingford https://doi.org/111\ Google Scholar
  21. 21.
    Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov I, Bourne P (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242CrossRefGoogle Scholar
  22. 22.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem 31(2):455–461PubMedPubMedCentralGoogle Scholar
  23. 23.
    Gill SC, von Hippel PH (1989) Calculation of protein extinction coefficients from amino acid sequence data. Anal Biochem 182(2):319–326CrossRefGoogle Scholar
  24. 24.
    Cuesta S, Arias J, Gallegos F, Proaño C, Blasco-Zúñiga A, Rivera M, Meneses L (2019) Molecular modeling of Dermaseptine SP2 extracted from Agalychnis spurrelli. InfoANALÍTICA 7(1):41–56.  https://doi.org/10.26807/ia.v7i1.95 CrossRefGoogle Scholar
  25. 25.
    Guruprasad K, Reddy BVB, Pandit MW (1990) Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng 4:155–161CrossRefGoogle Scholar
  26. 26.
    Li RF, Lu ZF, Sun YN, Chen SH, Yi YJ, Zhang HR, Yang SY, Yu GH, Huang L, Li CN (2016) Molecular design, structural analysis and antifungal activity of derivatives of peptide CGA-N46. Interdisciplinary sciences, computational life sciences 8(3):319–326CrossRefGoogle Scholar
  27. 27.
    Fieser TM, Tainer JA, Geysen HM, Houghten RA, Lerner RA (1987) Influence of protein flexibility and peptide conformation on reactivity of monoclonal anti-peptide antibodies with a protein alpha-helix. Proc Natl Acad Sci U S A 84(23):8568–8572CrossRefGoogle Scholar
  28. 28.
    Karplus PA, Schulz GE (1985) Prediction of chain flexibility in proteins: a tool for the selection of peptide antigens. Naturwissenschaften 72:212–213CrossRefGoogle Scholar
  29. 29.
    Huang F, Nau WM (2003) A conformational flexibility scale for amino acids in peptides. Angew Chem Int Ed Eng 42(20):2269–2272CrossRefGoogle Scholar
  30. 30.
    Melchiorri P, Negri L (2009) Amphibian peptides, Encyclopedia of Neuroscience. Elsevier Academic Press, San DiegoGoogle Scholar
  31. 31.
    Park MS, Gao C, Stern HA (2011) Estimating binding affinities by docking/scoring methods using variable protonation states. Proteins 79(1):304–314.  https://doi.org/10.1002/prot.22883 CrossRefPubMedGoogle Scholar
  32. 32.
    Pantsar T, Poso A (2018) Binding affinity via docking: fact and fiction. Molecules 23(8):1899CrossRefGoogle Scholar
  33. 33.
    Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M (2017) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:1074–1082CrossRefGoogle Scholar
  34. 34.
    Wang G (2014) Human antimicrobial peptides and proteins. Pharmaceuticals 7(5):545–594CrossRefGoogle Scholar
  35. 35.
    Bechinger B, Gorr SU (2017) Antimicrobial peptides: mechanisms of action and resistance. J Dent Res 96(3):254–260CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Sebastián Cuesta
    • 1
  • Felipe Gallegos
    • 2
  • Josefa Arias
    • 2
  • Fernanda Pilaquinga
    • 2
  • Ailín Blasco-Zúñiga
    • 3
  • Carolina Proaño-Bolaños
    • 4
  • Miryan Rivera
    • 3
  • Lorena Meneses
    • 2
    Email author
  1. 1.Laboratorios LIFEQuitoEcuador
  2. 2.Laboratorio de Química Computacional, Escuela de Ciencias QuímicasPontificia Universidad Católica del EcuadorQuitoEcuador
  3. 3.Laboratorio de Investigación en Citogenética y Biomoléculas de Anfibios-LICBA, Centro de Investigación para la Salud en América Latina-CISeAL, Facultad de Ciencias Exactas y NaturalesPontificia Universidad Católica del EcuadorQuitoEcuador
  4. 4.Laboratorio de Biología Molecular y Bioquímica, Universidad Regional Amazónica IkiamTenaEcuador

Personalised recommendations