Advertisement

Prediction of Protein Distance Maps by Assembling Fragments According to Physicochemical Similarities

  • Gualberto Asencio Cortés
  • Jesús S. Aguilar-Ruiz
  • Alfonso E. Márquez Chamorro
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 93)

Abstract

The prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein consists of determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Many approaches cited in the literature use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, implemented with parallel multithreading, a set of 30 physicochemical amino acid properties selected from the AAindex database were used. Several protein tertiary structure prediction methods produce a contact map. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. The results of experiments with several non-homologous protein sets demonstrate the generality of this method and its prediction quality using the amino acid properties considered.

Keywords

Protein Structure Prediction Amino Acid Property Amino Acid Index Structural Bioinformatics Prediction Vector 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rohl, C.A., Strauss, C.E.M., Misura, K.M.S., Baker, D.: Protein structure prediction using rosetta. In: Brand, L., Johnson, M.L. (eds.) Numerical Computer Methods, Part D Methods in Enzymology, vol. 383, pp. 66–93. Academic Press, London (2004)CrossRefGoogle Scholar
  2. 2.
    Hoque, T., Chetty, M., Sattar, A.: Extended hp model for protein structure prediction. Journal of computational biology: a journal of computational molecular cell biology 16(1), 85–103 (2009)MathSciNetGoogle Scholar
  3. 3.
    Kawashima, S., Pokarowski, P., Pokarowska, M., Kolinski, A., Katayama, T., Kanehisa, M.: Aaindex: amino acid index database, progress report 2008. Nucleic Acids Res. 36(Database issue), D202–D205 (2008)Google Scholar
  4. 4.
    Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I., Bourne, P.: The protein data bank. Nucl. Acids Res. 28(1), 235–242 (2000)CrossRefGoogle Scholar
  5. 5.
    Wang, G., Dunbrack, R.: Pisces: a protein sequence culling server. Bioinformatics 19(12), 1589–1591 (2003)CrossRefGoogle Scholar
  6. 6.
    Griep, S., Hobohm, U.: Pdbselect 1992-2009 and pdbfilter-select. Nucl. Acids Res. 38(Suppl. 1), D318–D319 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gualberto Asencio Cortés
    • 1
  • Jesús S. Aguilar-Ruiz
    • 1
  • Alfonso E. Márquez Chamorro
    • 1
  1. 1.School of EngineeringPablo de Olavide UniversitySpain

Personalised recommendations