Advertisement

Models for the Prediction of Antimicrobial Peptides Activity

  • Rosaura Parisi
  • Ida Moccia
  • Lucia Sessa
  • Luigi Di Biasi
  • Simona Concilio
  • Stefano PiottoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 587)

Abstract

Antimicrobial peptides AMP are small proteins produced by the innate immune system in multicellular microorganisms. The mechanism of action of AMP on target membranes can be divided in two main categories: pore forming and non-pore forming mechanisms. We applied a computational approach to design novel linear peptides having high specificity and low toxicity against common pathogens. We built up QSAR models using the data present in a database of antimicrobial peptides. Here, we present new models of activities obtained by the use of evolutionary methods and the relative statistical validation.

Keywords

Genetic Algorithm Artificial Neural Network Antimicrobial Peptide Artificial Neural Network Model Methicillin Resistant Staphylococcus Aureus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Liu, C., et al.: Clinical practice guidelines by the Infectious Diseases Society of America for the treatment of methicillin-resistant Staphylococcus aureus infections in adults and children. Clin. Infect. Dis. 52(3), e18–55 (2011) (ciq146)CrossRefGoogle Scholar
  2. 2.
    Cruz, J., et al.: Antimicrobial peptides: promising compounds against pathogenic microorganisms. Curr. Med. Chem. 21(20), 2299–2321 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cirac, A.D., et al.: The molecular basis for antimicrobial activity of pore-forming cyclic peptides. Biophys. J. 100(10), 2422–2431 (2011)CrossRefGoogle Scholar
  4. 4.
    Török, Z., et al.: Plasma membranes as heat stress sensors: from lipid-controlled molecular switches to therapeutic applications. Biochim. Biophys. Acta (BBA)-Biomembr. 1838(6), 1594–1618 (2014)CrossRefGoogle Scholar
  5. 5.
    Scrima, M., et al.: Structural features of the C8 antiviral peptide in a membrane-mimicking environment. Biochim. Biophys. (BBA)-Biomembr. 1838(3), 1010–1018 (2014)CrossRefGoogle Scholar
  6. 6.
    Marr, A.K., Gooderham, W.J., Hancock, R.E.: Antibacterial peptides for therapeutic use: obstacles and realistic outlook. Curr. Opin. Pharmacol. 6(5), 468–472 (2006)CrossRefGoogle Scholar
  7. 7.
    Wang, G.: Human antimicrobial peptides and proteins. Pharmaceuticals 7(5), 545–594 (2014)CrossRefGoogle Scholar
  8. 8.
    Scheetz, T., et al.: Genomics-based approaches to gene discovery in innate immunity. Immunol. Rev. 190(1), 137–145 (2002)CrossRefGoogle Scholar
  9. 9.
    Hancock, R.E., Chapple, D.S.: Peptide antibiotics. Antimicrob. Agents Chemother. 43(6), 1317–1323 (1999)Google Scholar
  10. 10.
    Lata, S., Sharma, B., Raghava, G.: Analysis and prediction of antibacterial peptides. BMC Bioinform. 8(1), 263 (2007)CrossRefGoogle Scholar
  11. 11.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)Google Scholar
  12. 12.
    Piotto, S.P., et al.: YADAMP: yet another database of antimicrobial peptides. Int. J. Antimicrob. Agents 39(4), 346–351 (2012)CrossRefGoogle Scholar
  13. 13.
    Accelrys, Accelrys Materials Studio. Accelrys Inc., San Diego, California (2014)Google Scholar
  14. 14.
    MATLAB, R.: Version 8.1. 0.604 (R2013a). The MathWorks Inc., Natrick, Massachusetts (2013)Google Scholar
  15. 15.
    Chen, L., et al.: How the antimicrobial peptides kill bacteria: computational physics insights. Commun. Comput. Phys. 11(3), 709 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Rosaura Parisi
    • 1
  • Ida Moccia
    • 1
  • Lucia Sessa
    • 1
  • Luigi Di Biasi
    • 1
    • 2
  • Simona Concilio
    • 3
  • Stefano Piotto
    • 1
    Email author
  1. 1.Department of PharmacyUniversity of SalernoFiscianoItaly
  2. 2.Department of InformaticsUniversity of SalernoFiscianoItaly
  3. 3.Department of Industrial EngineeringUniversity of SalernoFiscianoItaly

Personalised recommendations