Advertisement

Alpha Helix Prediction Based on Evolutionary Computation

  • Alfonso E. Márquez Chamorro
  • Federico Divina
  • Jesús S. Aguilar Ruiz
  • Gualberto Asencio Cortés
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6282)

Abstract

Multiple approaches have been developed in order to predict the protein secondary structure. In this paper, we propose an approach to such a problem based on evolutionary computation. The proposed approach considers various amino acids properties in order to predict the secondary structure of a protein. In particular, we will consider the hydrophobicity, the polarity and the charge of amino acids. In this study, we focus on predicting a particular kind of secondary structure: α-helices. The results of our proposal will be a set of rules that will identify the beginning or the end of such a structure.

Keywords

Protein Secondary Structure Prediction α-helix Evolutionary Computation 

References

  1. 1.
    Gu, J., Bourne, P.E.: Structural Bioinformatics (Methods of Biochemical Analysis). Wiley-Blackwell, Chichester (2003)Google Scholar
  2. 2.
    Berg, J.M., Stryer, L.: Biochemistry. Reverte (2008)Google Scholar
  3. 3.
    Chou, P.Y., Fasman, G.D.: Prediction of protein conformation. Biochemistry 13(2), 222–245 (1974)CrossRefPubMedGoogle Scholar
  4. 4.
    Garnier, J., Osguthorpe, D.J., Robson, B.: Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J. Mol. Biol. 120, 97–120 (1978)CrossRefPubMedGoogle Scholar
  5. 5.
    Lim, V.I.: Algorithms for prediction of a-helical and b-structural regions in globular proteins. J. Mol. Biol. 88, 857–872 (1974)CrossRefPubMedGoogle Scholar
  6. 6.
    Qian, N., Sejnowski, T.J.: Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol. 202, 865–884 (1988)CrossRefPubMedGoogle Scholar
  7. 7.
    McGuffin, L.J., Bryson, K., Jones, D.T.: The psipred protein structure prediction server. Bioinformatics 16, 404–405 (2000)CrossRefPubMedGoogle Scholar
  8. 8.
    Fariselli, P., Casadio, R.: A neural network based predictor of residue contacts in proteins. Protein Engineering 12, 15–21 (1999)CrossRefPubMedGoogle Scholar
  9. 9.
    Unger, R., Moult, J.: Genetic algorithms for protein folding simulations. Biochim. Biophys. 231, 75–81 (1993)Google Scholar
  10. 10.
    Frishman, D., Argos, P.: Incorporation of non-local interactions in protein secondary structure prediction from the amino acid sequence. Protein Engineering 9, 133–142 (1996)CrossRefPubMedGoogle Scholar
  11. 11.
    Salamov, A.A., Solovyev, V.V.: Protein secondary structure prediction using local alignments. J. Mol. Biol. 268, 31–36 (1997)CrossRefPubMedGoogle Scholar
  12. 12.
    Ward, J.J., McGuffin, L.J., Buxton, B.F., Jone, D.T.: Secondary structure prediction with support vector machines. Bioinformatics 13, 1650–1655 (2003)CrossRefGoogle Scholar
  13. 13.
    Cheng, J., Baldi, P.: Improved residue contact prediction using support vector machines and a large feature set. Bioinformatics 8, 113 (2007)PubMedPubMedCentralGoogle Scholar
  14. 14.
    Wilson, C.L., Hubbard, S.J., Doig: A critical assessment of the secondary structure prediction of alpha-helices and their n-termini in proteins. Protein Eng. 15, 545–554 (2002)CrossRefPubMedGoogle Scholar
  15. 15.
    Cui, Y., Chen, R.S., Hung, W.: Protein folding simulation with genetic algorithm and supersecondary structure constraints. Proteins: Structure, Function and Genetics 31, 247–257 (1998)CrossRefGoogle Scholar
  16. 16.
    Ramakrishnan, C., Ramachandran, G.N.: Stereochemical criteria for polypeptide and protein chain conformation. Byophys Journal 5, 909–933 (1965)CrossRefGoogle Scholar
  17. 17.
    Protein data bank online repository, ftp://ftp.wwpdb.org
  18. 18.
    Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. J. Mol. Bio. 157, 105–132 (1982)CrossRefGoogle Scholar
  19. 19.
    Grantham, R.: Amino acid difference formula to help explain protein evolution. J. J. Mol. Bio. 185, 862–864 (1974)Google Scholar
  20. 20.
    Klein, P., Kanehisa, M., DeLisi, C.: Prediction of protein function from sequence properties: Discriminant analysis of a data base. Biochim. Biophys. 787, 221–226 (1984)CrossRefGoogle Scholar
  21. 21.
    Richardson, J.S., Richardson, D.C.: Amino acid preferences for specific locations at the ends of alpha helices. Science 240, 1648–1652 (1998)CrossRefGoogle Scholar
  22. 22.
    Doig, A.J.: Baldwin R.L. N- and c-capping preferences for all 20 amino acids in alpha-helical peptides. Protein Science 4(7), 1325–1336 (1995)CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Fonseca, N.A., Camacho, R., Magalhaes, A.L.: Amino acid pairing at the n- and c-termini of helical segments in proteins. Proteins 70, 188–196 (2007)CrossRefGoogle Scholar
  24. 24.
    Protein data bank web, http://www.wwpdb.org
  25. 25.
    Protein data bank advanced search, http://www.pdb.org/pdb/search/advSearch.do
  26. 26.
    Complete list of pdb protein identifiers used in this article, http://www.upo.es/eps/marquez/proteins.txt
  27. 27.
    Wilson, C.L., Boardman, P.E., Doig, A.J., Hubbard, S.J.: Improved prediction for n-termini of alpha-helices using empirical information. Proteins 57(2), 322–330 (2004)CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alfonso E. Márquez Chamorro
    • 1
  • Federico Divina
    • 1
  • Jesús S. Aguilar Ruiz
    • 1
  • Gualberto Asencio Cortés
    • 1
  1. 1.School of EngineeringPablo de Olavide University of SevillaSpain

Personalised recommendations