Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan,Iran.
Cancer is one of the causes of death in the world. Several treatment methods exist against cancer cells such as radiotherapy and chemotherapy. Since traditional methods have side effects on normal cells and are expensive, identification and developing a new method to cancer therapy is very important. Antimicrobial peptides, present in a wide variety of organisms, such as plants, amphibians and mammals, are newly discovered agents. These peptides have various structures, sizes and molecular compositions; hence developing a computational method to predict these anticancer peptides is useful. In the present study, first, 2 databases with 138 and 206 anticancer and non-anticancer peptides were introduced, classified by TRAINER. TRAINER (http://www.baskent.edu.tr/~hogul/ TRAINER/) is a new online tool designed for classification of any alphabet of sequences. TRAINER allows users to select from among several feature representation schemes and supervised machine learning methods with relevant parameters. In this study, Naive Bayes and radial basis were used in a support vector machine. The accuracy and specificity in combination of features by Naive Bayes were 83% and by radial basis 87% and 92% respectively. The results demonstrate that two methods are useful for classification of these peptides; however, the accuracy of Radial Basis is higher than Naive Bayes.
1. Suarez-Jimenez GM, Burgos-Hernandez A, Ezquerra-Brauer JM. Bioactive peptides and depsipeptides with anticancer potential: Sources from marine animals. Mar Drugs 2012;10:963-986.
2. Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD. Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol 2002;3:991-998.
3. Mocellin S, Rossi CR, Nitti D. Cancer vaccine development: on the way to break immune tolerance to malignant cells. Exp Cell Res 2004;299:267-278.
4. Massodi I, Moktan S, Rawat A, Bidwell GL, Raucher D. Inhibition of ovarian cancer cell proliferation by a cell cycle inhibitory peptide fused to a thermally responsive polypeptide carrier. Int J Cancer 2010;126:533-544.
5. Shadidi M, Sioud M. Selective targeting of cancer cells using synthetic peptides. Drug Resist Update 2003;6:363-371.
6. Leuschner C, Hansel W. Membrane disrupting lytic peptides for cancer treatments. Curr Pharm Design 2004;10:2299-2310.
7. Papo N, Shahar M, Eisenbach L, Shai Y. A novel lytic peptide composed of D, L amino acids selectively kills cancer cells in culture and in mice. J Biol Chem 2003;278: 21018-21023.
8. Tossi A, Sandri L, Giangaspero A. Amphipathic, α-helical antimicrobial peptides. Pept Sci 2000;55:4-30.
9. Diamond G, Beckloff N, Weinberg A, Kisich KO. The roles of antimicrobial peptides in innate host defense. Curr Pharm Design 2009;15:2377.
10. Bals R. Epithelial antimicrobial peptides in host defense against infection. Respir Res 2000;1:141-150.
11. Papo N, Shai Y. Host defense peptides as new weapons in cancer treatment. Cell Mol Life Sci 2005;62:784-790.
12. Mai JC, Mi Z, Kim SH, Ng B, Robbins PD. A proapoptotic peptide for the treatment of solid tumors. Cancer Res 2001;61:7709-7712.
13. Ellerby HM, Arap W, Ellerby LM, Kain R, Andrusiak R, Rio GD, Krajewski S, Lombardo CR, Rao R, Ruoslahti E, Bredesen DE, Pasqualini R. Anti-cancer activity of targeted pro-apoptotic peptides. Nat Med 1999;5:1032-1038.
14. Shai Y. Mode of action of membrane active antimicrobial peptides. Biopolymers 2002; 66:236-248.
15. Hoffmann JA, Kafatos FC, Janeway CA, Ezekowitz RA. Phylogenetic perspectives in innate immunity. Science 1999;284:1313-1318.
16. Shai Y. Mechanism of the binding, insertion and destabilization of phospholipid bilayer membranes by α-helical antimicrobial and cell non-selective membrane-lytic peptides. Biochim Biophys Acta1999;1462:55-70.
17. Nijnik A, Hancock R. Host defense peptides: antimicrobial and immunomodulatory activity and potential applications for tackling antibiotic-resistant infections. Emerg Health Threats J 2009;2:e1.
18. Thayer AM. Improving peptides. Chem Eng News 2011;89:13-20.
19. Borghouts C, Kunz C, Groner B. Current strategies for the development of peptide-based anti-cancer therapeutics. J Pept Sci 2005;11:713-726.
20. Wang G, Li X, Wang Z. APD2: The updated antimicrobial peptide database and its application in peptide design. Nucleic Acids Res 2009;37:D933-D937.
21. Thomas S, Karnik S, Barai RS, Jayaraman VK, Idicula-Thomas S. CAMP: a useful resource for research on antimicrobial peptides. Nucleic acids Res 2010;38:D774-D780.
22. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 2006;22:1658-1659.
23. Moody J, Darken CJ. Fast learning in networks of locally-tuned processing units. Neural Comput 1989;1:281-294.
24. Park J, Sandberg IW. Universal approximation using radial basis-function networks. Neural Comput 1991;3:246-257.
25. Duda RO, Hart PE, Stork DG. Pattern Classification, 2nd edition. John Wiley and Sons, 2000.
26. Oqul H, Kalkan AT, Umu SU, Akkaya MS. Trainer: A general-purpose trainable short biosequence classifier. Protein Pept Lett 2013 [Epub ahead of print].
27. Sundelacruz S, Levin M, Kaplan D L. Role of membrane potential in the regulation of cell proliferation and differentiation. Stem Cell Rev Rep 2009;5:231-246.
28. Mohabatkar H. Prediction of cyclin proteins using Chou's pseudo amino acid composition. Protein Pept Lett 2010;17:1207-1214.
29. Esmaeili M, Mohabatkar H, Mohsenzadeh S. Using the concept of Chou's pseudo amino acid composition for risk type predictionof human papillomaviruses. J Theor Biol 2010;263:203-209.
30. Mohabatkar H, Mohammad-Beigi M, Esmaeili A. Prediction of GABAA receptor proteins using the concept of Chou's pseudoaminoacid composition and support vector machine. J Theor Biol 2011;281:18-23.
31. Mohammad-Beigi M, Behjati M, Mohabatkar H. Prediction of metalloproteinase family based on the concept of Chou's pseudoamino acid composition using a machine learning approach. J Struct Funct Genomics 2011;12:191-197.
33. Mohabatkar H, Mohammad-Beigi M, Abdolahi K, Mohsenzadeh S. Prediction of allergenic proteins by means of the concept of Chous pseudo amino acid composition and a machine learning approach. Med Chem 2013;9;133-137.