Multiple Tree Alignment with Weights Applied to Carbohydrates to Extract Binding Recognition Patterns

  • Masae Hosoda
  • Yukie Akune
  • Kiyoko F. Aoki-Kinoshita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7632)

Abstract

The purpose of our research is the elucidation of glycan recognition patterns. Glycans are composed of monosaccharides and have complex structures with branches due to the fact that monosaccharides have multiple potential binding positions compared to amino acids. Each monosaccharide can potentially be bound by up to five other monosaccharides, compared to two for any amino acid. Glycans are often bound to proteins and lipids on the cell surface and play important roles in biological processes. Lectins in particular are proteins that recognize and bind to glycans. In general, lectins bind to the terminal monosaccharides of glycans on glycoconjugates. However, it is suggested that some lectins recognize not only terminal monosaccharides, but also internal monosaccharides, possibly influencing the binding affinity. Such analyses are difficult without novel bioinformatics techniques. Thus, in order to better understand the glycan recognition mechanism of such biomolecules, we have implemented a novel algorithm for aligning glycan tree structures, which we provide as a web tool called MCAW (Multiple Carbohydrate Alignment with Weights). From our web tool, we have analyzed several different lectins, and our results could confirm the existence of well-known glycan motifs. Our work can now be used in several other analyses of glycan structures, such as in the development of glycan score matrices as well as in state model determination of probabilistic tree models. Therefore, this work is a fundamental step in glycan pattern analysis to progress glycobiology research.

Keywords

glycomics glycans bioinformatics multiple tree alignment algorithm 

References

  1. 1.
    Akune, Y., Hosoda, M., Kaiya, S., Shinmachi, D., Aoki-Kinoshita, K.F.: The RINGS resource for glycome informatics analysis and data mining on the Web. OMICS 14(4), 475–486 (2010)CrossRefGoogle Scholar
  2. 2.
    Alvarez, R.A., Blixt, O.: Identification of ligand specificities for glycan-binding proteins using glycan arrays. Methods Enzymol. 415, 292–310 (2006)CrossRefGoogle Scholar
  3. 3.
    Aoki, K.F., Mamitsuka, H., Akutsu, T., Kanehisa, M.: A score matrix to reveal the hidden links in glycans. Bioinformatics 21(8), 1457–1463 (2005)CrossRefGoogle Scholar
  4. 4.
    Aoki-Kinoshita, K.F., Ueda, N., Mamitsuka, H., Kanehisa, M.: ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains. Bioinformatics 22, e25–e34 (2006)Google Scholar
  5. 5.
    Aoki-Kinoshita, K.F.: Glycome Informatics: Methods and Applications. CRC Press (2009)Google Scholar
  6. 6.
    Aoki, K.F., Yamaguchi, A., Ueda, N., Akutsu, T., Mamitsuka, H., Goto, S., Kanehisa, M.: KCaM (KEGG carbohydrate matcher): a software tool for analyzing the structures of carbohydrate glycans. Nucleic Acids Research 32, 267–272 (2004)CrossRefGoogle Scholar
  7. 7.
    Eddy, S.R.: Profile hidden Markov models. Bioinformatics 14, 755–763 (1998)CrossRefGoogle Scholar
  8. 8.
    Fitch, W.M., Margoliash, E.: Construction of phylogenetic trees. Science 155, 279–284 (1967)CrossRefGoogle Scholar
  9. 9.
    Fukui, S., Feizi, T., Galustian, C., Lawson, A.M., Chai, W.: Oligosaccharide microarrays for high-throughput detection and specificity assignments of carbohydrate-protein interactions. Nat. Biotechnology 20(10), 1011–1017 (2002)CrossRefGoogle Scholar
  10. 10.
    Hashimoto, K., Aoki-Kinoshita, K.F., et al.: A new efficient probabilistic model for mining labeled ordered tree. In: Proc. KDD, pp. 177–186 (2006)Google Scholar
  11. 11.
    Ohtsubo, K., Marth, J.: Glycosylation in cellular mechanisms of health and disease. Cell 126(5), 85–867 (2006)CrossRefGoogle Scholar
  12. 12.
    Ramakrishnan, S., Lang, W., Raguram, S., Raman, R., Venkataraman, M., Sasisekharan, R.: Advancing glycomics: Implementation strategies at the Consortium for Functional Glycomics. Glycobiology 16, 82–90 (2006)CrossRefGoogle Scholar
  13. 13.
    Thompson, J.D., Higgins, D.G., Gibson, T.J.: Clustal W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research 22(22), 4673–4680 (1994)CrossRefGoogle Scholar
  14. 14.
    Ueda, N., Aoki-Kinoshita, K.F., Yamaguchi, A., Akutsu, T., Mamitsuka, H.: A probabilistic model for mining labeled ordered trees: capturing patterns in carbohydrate sugar chains. IEEE Transactions on Knowledge and Data Engineering 17(8), 1051–1064 (2005)CrossRefGoogle Scholar
  15. 15.
    Varki, A., et al. (eds.): Essentials of Glycobiology second edition. Cold Spring Harbor Laboratory Press (2009)Google Scholar
  16. 16.
    Bille, P.: A survey on tree edit distance and related problems. Theoretical Computer Science 337(1-3), 217–239 (2005)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Shatsky, M., Nussinov, R., Wolfson, H.J.: A method for simultaneous alignment of multiple protein structures. Proteins: Structure, Function, and Bioinformatics 56(1), 143-156, 1097-0134(2004)Google Scholar
  18. 18.
  19. 19.
    Consortium for Functional Glycomics, http://www.functionalglycomics.org

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Masae Hosoda
    • 1
  • Yukie Akune
    • 1
  • Kiyoko F. Aoki-Kinoshita
    • 1
  1. 1.Dept. of Bioinformatics, Faculty of EngineeringSoka UniversityHachiojiJapan

Personalised recommendations