Advertisement

Antonie van Leeuwenhoek

, Volume 111, Issue 10, pp 1871–1882 | Cite as

In silico design of polycationic antimicrobial peptides active against Pseudomonas aeruginosa and Staphylococcus aureus

  • Oscar Hincapié
  • Paula Giraldo
  • Sergio Orduz
Original Paper

Abstract

Antimicrobial peptides (AMPs) have the potential to become valuable antimicrobial drugs in the coming years, since they offer wide spectrum of action, rapid bactericidal activity, and low probability for resistance development in comparison with traditional antibiotics. The search and improvement of methodologies for discovering new AMPs to treat resistant bacteria such as Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii and Pseudomonas aeruginosa are needed for further development of antimicrobial products. In this work, the software Peptide ID 1.0® was used to find new antimicrobial peptide candidates encrypted in proteins, considering the physicochemical parameters characteristics of AMPs such as positive net charge, hydrophobicity, and sequence length, among others. From the selected protein fragments, new AMPs were designed after conservative and semi-conservative modifications and amidation of the C-terminal region. In vitro studies of the antimicrobial activity of the newly designed peptides showed that two peptides, P3-B and P3-C, were active against P. aeruginosa Escherichia coli and A. baumannii with low minimum inhibitory concentrations. Peptide P3-C was also active against K. pneumoniae and S. aureus. Furthermore, bactericidal activity and information on the possible mechanisms of action are described according to the scanning electron microscopy studies.

Keywords

Cationic antimicrobial peptides Bioinformatics Peptide design Pseudomonas aeruginosa Staphylococcus aureus 

Notes

Acknowledgements

This research has been funded by projects 9727 and 35058 from Universidad Nacional de Colombia, sede Medellin.

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Aliste MP, MacCallum JL, Tieleman DP (2003) Molecular dynamics simulations of pentapeptides at interfaces: salt bridge and cation-pi interactions. Biochemistry 42:8976–8987.  https://doi.org/10.1021/bi027001j CrossRefGoogle Scholar
  2. Amaral AC, Silva ON, Mundim NCCC, de Carvalho MJ, Migliolo L, Leite JR, Prates MV, Bocca AL, Franco OL, Felipe MS (2012) Predicting antimicrobial peptides from eukaryotic genomes: in silico strategies to develop antibiotics. Peptides 37:301–308.  https://doi.org/10.1016/j.peptides.2012.07.021 CrossRefPubMedGoogle Scholar
  3. Arias CA, Murray BE (2009) Antibiotic-resistant bugs in the 21st century—a clinical super-challenge. N Engl J Med 360:439–443.  https://doi.org/10.1056/NEJMp0804651 CrossRefPubMedGoogle Scholar
  4. Berditsch M, Jäger T, Strempel N, Schwartz T, Overhage J, Ulrich AS (2015) Synergistic effect of membrane-active peptides polymyxin B and gramicidin S on multidrug-resistant strains and biofilms of Pseudomonas aeruginosa. Antimicrob Agents Chemother 59:5288–5296.  https://doi.org/10.1128/AAC.00682-15 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Bhullar K, Waglechner N, Pawlowski A, Koteva K, Banks ED, Johnston MD, Barton HA, Wright GD (2012) Antibiotic resistance is prevalent in an isolated cave microbiome. PLoS ONE 7:e34953.  https://doi.org/10.1371/journal.pone.0034953 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Brand GD, Magalhães MT, Tinoco ML, Aragão FJ, Nicoli J, Kelly SM, Cooper A, Bloch C Jr (2012) Probing protein sequences as sources for encrypted antimicrobial peptides. PLoS ONE 7(9):e45848.  https://doi.org/10.1371/journal.pone.0045848 CrossRefPubMedPubMedCentralGoogle Scholar
  7. D’Costa VM, King CE, Kalan L, Morar M, Sung WW, Schwarz C, Froese D, Zazula G, Calmels F, Debruyne R, Golding GB, Poinar HN, Wright GD (2011) Antibiotic resistance is ancient. Nature 477:457–461.  https://doi.org/10.1038/nature10388 CrossRefPubMedGoogle Scholar
  8. Fauchere J-L, Pliska V (1983) Hydrophobic parameters pi of amino-acid side chains from the partitioning of N-acetyl-amino-acid amides. Eur J Med Chem Chim Ther 18:369–375Google Scholar
  9. Gallivan JP, Dougherty DA (1999) Cation-π interactions in structural biology. Proc Natl Acad Sci USA 96:9459–9464CrossRefGoogle Scholar
  10. Gautier R, Douguet D, Antonny B, Drin G (2008) HELIQUEST: a web server to screen sequences with specific α-helical properties. Bioinformatics 24:2101–2102.  https://doi.org/10.1093/bioinformatics/btn392 CrossRefPubMedGoogle Scholar
  11. Gómez EA, Giraldo P, Orduz S (2017) InverPep: a database of invertebrate antimicrobial peptides. J Glob Antimicrob Resist 8:13–17.  https://doi.org/10.1016/j.jgar.2016.10.003 CrossRefPubMedGoogle Scholar
  12. Ibrahim HR, Thomas U, Pellegrini A (2001) A helix-loop-helix peptide at the upper lip of the active site cleft of lysozyme confers potent antimicrobial activity with membrane permeabilization action. J Biol Chem 276:43767–43774.  https://doi.org/10.1074/jbc.M106317200 CrossRefPubMedGoogle Scholar
  13. Malagoli D (2007) A full-length protocol to test hemolytic activity of palytoxin on human erythrocytes. Invertebr Surv J 4:92–94Google Scholar
  14. Maria-Neto S, de Almeida KC, Macedo M (2015) Understanding bacterial resistance to antimicrobial peptides: from the surface to deep inside. Biochim Biophys Acta 1848:3078–3088.  https://doi.org/10.1016/j.bbamem.2015.02.017 CrossRefPubMedGoogle Scholar
  15. Mishra B, Wang G (2012) The importance of amino acid composition in natural AMPs: an evolutional, structural, and functional perspective. Front Immunol 3:221.  https://doi.org/10.3389/fimmu.2012.00221 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Mooney C, Haslam N, Holton T, Pollastri G, Shields D (2013) PEPTIDELOCATOR: prediction of bioactive peptides in protein sequences. Method Biochem Anal 29:1120–1126.  https://doi.org/10.1093/bioinformatics/btt103 CrossRefGoogle Scholar
  17. O’Driscoll NH, Labovitiadi O, Cushnie T (2013) Production and evaluation of an antimicrobial peptide-containing wafer formulation for topical application. Curr Microbiol 66:271–278.  https://doi.org/10.1007/s00284-012-0268-3 CrossRefGoogle Scholar
  18. Rieg S, Huth A, Kalbacher H, Kern WV (2009) Resistance against antimicrobial peptides is independent of Escherichia coli AcrAB, Pseudomonas aeruginosa MexAB and Staphylococcus aureus NorA efflux pumps. Int J Antimicrob Agents 33:174–176.  https://doi.org/10.1016/j.ijantimicag.2008.07.032 CrossRefPubMedGoogle Scholar
  19. Roca I, Akova M, Baquero F, Carlet J, Cavaleri M, Coenen S, Cohen J, Findlay D, Gyssens I, Heure OE, Kahlmeter G, Kruse H, Laxminarayan R, Liébana E, López-Cerero L, MacGowan A, Martins M, Rodríguez-Baño J, Rolain JM, Segovia C, Sigauque B, Taconelli E, Wellington E, Vila J (2015) The global threat of antimicrobial resistance: science for intervention. New Microbes New Infect 6:22–29.  https://doi.org/10.1016/j.nmni.2015.02.007 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Sánchez Y, Betancur A, Agudelo M, Orduz S (2015) Peptide ID 1.0. Un programa para buscar potenciales péptidos bioactivos en secuencias de proteínas. Dirección Nacional de Derechos de Autor. Ministerio del Interior. Registro 13-50-213 del 12 de Noviembre de 2015Google Scholar
  21. Strandberg E, Tiltak D, Ieronimo M (2007) Influence of C-terminal amidation on the antimicrobial and hemolytic activities of cationic α-helical peptides. Pure Appl Chem 79:717–728.  https://doi.org/10.1351/pac200779040717 CrossRefGoogle Scholar
  22. Tazi A, Chapron J, Touak G, Longo M (2013) Rapid emergence of resistance to linezolid and mutator phenotypes in Staphylococcus aureus isolates from an adult cystic fibrosis patient. Antimicrob Agents Chemother 57:5182–5188.  https://doi.org/10.1128/AAC.01392-13 CrossRefGoogle Scholar
  23. The UniProt Consortium (2015) UniProt: a hub for protein information. Nucl Acids Res 43:204–212.  https://doi.org/10.1093/nar/gku989 CrossRefGoogle Scholar
  24. Torrent M, Andreu D, Nogués VM, Boix E (2011) Connecting peptide physicochemical and antimicrobial properties by a rational prediction model. PLoS ONE 6(2):e16968.  https://doi.org/10.1371/journal.pone.0016968 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Torrent M, Di Tommaso P, Pulido D, Nogués MV, Notredame C, Boix E, Andreu D (2012) AMPA: an automated web server for prediction of protein antimicrobial regions. Bioinformatics 28:130–131.  https://doi.org/10.1093/bioinformatics/btr604 CrossRefPubMedGoogle Scholar
  26. Tran TT, Panesso D, Mishra NN, Mileykovskaya E (2013) Daptomycin-resistant Enterococcus faecalis diverts the antibiotic molecule from the division septum and remodels cell membrane phospholipids. MBio 4(4):e00281.  https://doi.org/10.1128/mBio.00281-13 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Uematsu N, Matsuzaki K (2000) Polar angle as a determinant of amphipathic α-helix-lipid interactions: a model peptide study. Biophys J 79:2075–2083.  https://doi.org/10.1016/S0006-3495(00)76455-1 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Waghu FH, Barai RS, Gurung P (2016) CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides. Nucl Acids Res 44:D1094–D1097.  https://doi.org/10.1093/nar/gkv1051 CrossRefPubMedGoogle Scholar
  29. Wang G (2015) Improved methods for classification, prediction, and design of antimicrobial peptides. Methods Mol Biol 1268:43–66.  https://doi.org/10.1007/978-1-4939-2285-7_3 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Wang G, Li X, Wang Z (2016) APD3: the antimicrobial peptide database as a tool for research and education. Nucl Acids Res 44(D1):D1087–D1093.  https://doi.org/10.1093/nar/gkv1278 CrossRefPubMedGoogle Scholar
  31. Watkins RR, Bonomo RA (2016) Overview: global and local impact of antibiotic resistance. Infect Dis Clin North Am 30:313–322.  https://doi.org/10.1016/j.idc.2016.02.001 CrossRefPubMedGoogle Scholar
  32. Wiegand I, Hilpert K, Hancock R (2008) Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat Protoc 3:163–175.  https://doi.org/10.1038/nprot.2007.521 CrossRefGoogle Scholar
  33. World Health Organization (2012) The evolving threat of antimicrobial resistance: options for action 2012. ISBN 978 92 4 150318 1Google Scholar
  34. World Health Organization (2014) Antimicrobial resistance: global report on surveillance 2014. ISBN: 978 92 4 156474 8Google Scholar
  35. Xiang N, Lyu Y, Zhu X, Bhunia AK, Narsimhan G (2016) Methodology for identification of pore forming antimicrobial peptides from soy protein subunits β-conglycinin and glycinin. Peptides 85:27–40.  https://doi.org/10.1016/j.peptides.2016.09.004 CrossRefPubMedGoogle Scholar
  36. Yeaman MR, Yount NY (2003) Mechanisms of antimicrobial peptide action and resistance. Pharmacol Rev 55:27–55.  https://doi.org/10.1124/pr.55.1.2 CrossRefGoogle Scholar
  37. Yin LM, Edwards MA, Li J, Yip CM, Deber CM (2012) Roles of hydrophobicity and charge distribution of cationic antimicrobial peptides in peptide-membrane interactions. J Biol Chem 287:7738–7745.  https://doi.org/10.1074/jbc.M111.303602 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Yun RH, Anderson A, Hermans J (1991) Proline in α-helix: stability and conformation studied by dynamics simulation. Proteins 10(3):219–228.  https://doi.org/10.1002/prot.340100306 CrossRefPubMedGoogle Scholar
  39. Zasloff M (2002) Antimicrobial peptides in health and disease. N Engl J Med 347:1199–2000.  https://doi.org/10.1056/NEJMe020106 CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Grupo de Investigación de Biología Funcional, Escuela de Biociencias - Facultad de CienciasUniversidad Nacional de ColombiaMedellínColombia

Personalised recommendations