Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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Application of Decision Tree for the Classification of Antimicrobial Peptide

  • Lee, Su Yeon (Interdisciplinary Program in Bioinformatics, Laboratory of Molecular Genetics, School of Biological Sciences, Institute of Molecular Biology and Genetics, and Seoul National University) ;
  • Kim, Sunkyu (Laboratory of Molecular Genetics, School of Biological Sciences, Institute of Molecular Biology and Genetics, and Seoul National University) ;
  • Kim, Sukwon S. (Laboratory of Molecular Genetics, School of Biological Sciences, Institute of Molecular Biology and Genetics, and Seoul National University) ;
  • Cha, Seon Jeong (Interdisciplinary Program in Bioinformatics, Laboratory of Molecular Genetics, School of Biological Sciences, Institute of Molecular Biology and Genetics, and Seoul National University) ;
  • Kwon, Young Keun (Optimization Laboratory, School of Computer Science and Engineering, Seoul National University) ;
  • Moon, Byung-Ro (Interdisciplinary Program in Bioinformatics, Optimization Laboratory, School of Computer Science and Engineering, Seoul National University) ;
  • Lee, Byeong Jae (Interdisciplinary Program in Bioinformatics, Laboratory of Molecular Genetics, School of Biological Sciences, Institute of Molecular Biology and Genetics, and Seoul National University)
  • Published : 2004.09.01
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Abstract

The purpose of this study was to investigate the use of decision tree for the classification of antimicrobial peptides. The classification was based on the activities of known antimicrobial peptides against common microbes including Escherichia coli and Staphylococcus aureus. A feature selection was employed to select an effective subset of features from available attribute sets. Sequential applications of decision tree with 17 nodes with 9 leaves and 13 nodes with 7 leaves provided the classification rates of $76.74\%$ and $74.66\%$ against E. coli and S. aureus, respectively. Angle subtended by positively charged face and the positive charge commonly gave higher accuracies in both E. coli and S. aureusdatasets. In this study, we describe a successful application of decision tree that provides the understanding of the effects of physicochemical characteristics of peptides on bacterial membrane.

Keywords

References

  1. Dathe, M., Wieprecht, T., Nikolenko, H., Handel, L., Maloy, W. L. , MacDonald, D.L., Beyermanna, M., and Bienerta M. (1997). Hydrophobicity, hydrophobic moment and angle subtended by charged residues modulate antibacterial and haemolytic activity of amphipathic helical peptides. FEBS Lett. 403, 208-212 https://doi.org/10.1016/S0014-5793(97)00055-0
  2. Dathe, M. and Wieprecht, T. (1999). Structural features of helical antimicrobial peptides: their potential to modulate activity on model membranes and biological cells. Biochim. Biophys. Acta. 1462, 71-87 https://doi.org/10.1016/S0005-2736(99)00201-1
  3. Dathe, M, Nikolenko, H., Meyer, J., Beyermann, M. and Bienert, M (2001). Optimization of the antimicrobial activity of magainin peptides by modification of charge. FEBS Lett. 501, 146-150 https://doi.org/10.1016/S0014-5793(01)02648-5
  4. Dathe, M., Meyer, J., Beyermann, M., Maul, B., Hoischen, C., and Bienert, M. (2002). General aspects of peptide selectivity towards lipid bilayers and cell membranes studied by variation of the structural parameters of amphipathic helical model peptides. Biochim. Biophys. Acta. 1558, 171-186 https://doi.org/10.1016/S0005-2736(01)00429-1
  5. Hong, S.Y., Park, T.G., and Lee, K.H.. (2001). The effect of charge increase on the specificity and activity of a short antimicrobial peptide. Peptides 22, 1669-1674 https://doi.org/10.1016/S0196-9781(01)00502-2
  6. Kim, S., Kim, S.S., Bang, Y.J., Kim, S.J. and Lee B.J. (2003). In vitro activities of native and designed peptide antibiotics against drug sensitive and resistant tumor cell lines. Peptides 24, 945-953 https://doi.org/10.1016/S0196-9781(03)00194-3
  7. Kneller, D.G,, Cohen, F.E, and Langridge, R. (1990). Improvements in protein secondary structure prediction by an enhanced neural network. J. Mol. BioI. 214, 171-182 https://doi.org/10.1016/0022-2836(90)90154-E
  8. Kyte, J. and Doolittle, R.F. (1982). A simple method for displaying the hydropathic character of a protein. J. Mol. BioI. 157, 105-132 https://doi.org/10.1016/0022-2836(82)90515-0
  9. Mitchell, T.M. (1997). Machine Learning (The McGrow-Hill Companies Inc.)
  10. Park, J.M., Jung, J.E., and Lee, B.J. (1994). Antimicrobial peptides from the skin of a Korean frog, Rana rugosa. Biochern. Biophys, Res. Comrn. 205, 948-954 https://doi.org/10.1006/bbrc.1994.2757
  11. Pathak, N., Salas-Auvert, R., Ruche, G., Janna, M.H., McCarthy, D., and Harrison, R.G. (1995). Comparison of the effects of hydrophobicity, amphiphilicity, and alphahelicity on the activities of antimicrobial peptides. Proteins 22, 182-186 https://doi.org/10.1002/prot.340220210
  12. Quinlan, J.R. (1993). C4.5:Programs for Machine Learning (Morgan Kaufmann Publisher Inc.)
  13. Rocca, P.L., Biggin, P.C., Tieleman, D.P., and Sansom MSP. (1999a). Simulation studies of the interaction of antimicrobial peptides and lipid bilayers. Biochim. Biophys. Acta. 1462, 185-200 https://doi.org/10.1016/S0005-2736(99)00206-0
  14. Rocca, P.L., Shai, Y., and Sansom, M.S.P. (1999b). Peptide -bilayer interactions: simulations of dermaseptin B, an antimicrobial peptide. Biophys. Chem. 76, 145-159 https://doi.org/10.1016/S0301-4622(98)00232-4
  15. Salzberg, S., Deicher, A.L., Fasman K.H., and Henderson L. (1998). A decision tree system for finding genes in DNA. J. Comput. BioI. 5, 667-680 https://doi.org/10.1089/cmb.1998.5.667
  16. Selbig, J., Mevissen, T., and Lenauer, T. (1999). Decision tree-based formation of consensus protein secondary structure prediction. Bioinformatics 15, 1039-1046 https://doi.org/10.1093/bioinformatics/15.12.1039
  17. Stark, M., Liu, L.P., and Deber, C.M. (2002). Cationic hydrophobic peptides with antimicrobial activity. Antimicrob. Agents Chemother. 46, 3585-3590 https://doi.org/10.1128/AAC.46.11.3585-3590.2002
  18. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions (with discussion). J. Roy. Stat. Soc. B. 36,111-147
  19. Tachi, T., Epand, R.F., Epand, R.M., and Matsuzaki, M. (2002). Position-dependent hydrophobicity of the antimicrobial magainin peptide affects the mode of peptide-lipid interactions and selective toxicity. Biochemistry 41, 10723-10731 https://doi.org/10.1021/bi0256983
  20. Vapnik, V.N. (1995). The Nature of Statistical Learning Theory (New York: Springer Verlag)
  21. Weiss, S.M. and kulikowski, C.A. (1991). Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems. (San Mateo: Morgan Kaufmann Publisher Inc.)
  22. Xia, X., Maliski, E., Cheetham, J., and Poppe, L. (2003). Solubility prediction by recursive partitioning. Pharm. Res. 20,1634-1640 https://doi.org/10.1023/A:1026195503465
  23. Zasloff, M. (2002). Antimicrobial peptides of multicellular organisms. Nature 415, 389-395 https://doi.org/10.1038/415389a