SCIA 2017: Image Analysis pp 285-296 | Cite as

Supervised Approaches for Function Prediction of Proteins Contact Networks from Topological Structure Information

  • Alessio Martino
  • Enrico Maiorino
  • Alessandro Giuliani
  • Mauro Giampieri
  • Antonello Rizzi
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10269)

Abstract

The role performed by a protein is directly connected to its physico-chemical structure. How the latter affects the behaviour of these molecules is still an open research topic. In this paper we consider a subset of the Escherichia Coli proteome where each protein is represented through the spectral characteristics of its residue contact network and its physiological function is encoded by a suitable class label. By casting this problem as a machine learning task, we aim at assessing whether a relation exists between such spectral properties and the protein’s function. To this end we adopted a set of supervised learning techniques, possibly optimised by means of genetic algorithms. First results are promising and they show that such high-level spectral representation contains enough information in order to discriminate among functional classes. Our experiments pave the way for further research and analysis.

Keywords

Pattern recognition Supervised learning Support Vector Machines Protein contact networks Normalised Laplacian matrix 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Di Paola, L., De Ruvo, M., Paci, P., Santoni, D., Giuliani, A.: Protein contact networks: an emerging paradigm in chemistry. Chem. Rev. 113, 1598–1613 (2013)CrossRefGoogle Scholar
  2. 2.
    Niwa, T., Ying, B.W., Saito, K., Jin, W., Takada, S., Ueda, T., Taguchi, H.: Bimodal protein solubility distribution revealed by an aggregation analysis of the entire ensemble of Escherichia coli proteins. Proc. Natl. Acad. Sci. USA 106, 4201–4206 (2009)CrossRefGoogle Scholar
  3. 3.
    Webb, E.C.: Enzyme Nomenclature. Academic Press, San Diego (1992)Google Scholar
  4. 4.
    Jurman, G., Visintainer, R., Furlanello, C.: An introduction to spectral distances in networks. Front. Artif. Intell. Appl. 226, 227–234 (2011)Google Scholar
  5. 5.
    Livi, L., Maiorino, E., Giuliani, A., Rizzi, A., Sadeghian, A.: A generative model for protein contact networks. J. Biomol. Struct. Dyn. 34, 1441–54 (2016)CrossRefGoogle Scholar
  6. 6.
    Maiorino, E., Rizzi, A., Sadeghian, A., Giuliani, A.: Spectral reconstruction of protein contact networks. Phys. A 471, 804–817 (2017)CrossRefGoogle Scholar
  7. 7.
    Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33, 1065–1076 (1962)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)MATHGoogle Scholar
  9. 9.
    Mitchell, T.: Machine Learning. McGraw-Hill, Boston (1997)MATHGoogle Scholar
  10. 10.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2007)MATHGoogle Scholar
  11. 11.
    Livi, L., Giuliani, A., Sadeghian, A.: Characterization of graphs for protein structure modeling and recognition of solubility. Curr. Bioinform. 11, 106–114 (2016)CrossRefGoogle Scholar
  12. 12.
    Livi, L., Giuliani, A., Rizzi, A.: Toward a multilevel representation of protein molecules: Comparative approaches to the aggregation/folding propensity problem. Inf. Sci. 326, 134–145 (2016)CrossRefGoogle Scholar
  13. 13.
    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000). http://www.rcsb.org/pdb/home/home.do
  14. 14.
    Scott, D.: On optimal and data-based histograms. Biometrika 66, 605–610 (1979)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Giuliani, A., Benigni, R., Zbilut, J.P., Webber, C.L., Sirabella, P., Colosimo, A.: Nonlinear signal analysis methods in the elucidation of protein sequence-structure relationships. Chem. Rev. 102(5), 1471–1492 (2002)CrossRefGoogle Scholar
  16. 16.
    Changeux, J.P., Edelstein, S.J.: Allosteric mechanisms of signal transduction. Science 308(5727), 1424–1428 (2005)CrossRefGoogle Scholar
  17. 17.
    Di Paola, L., Giuliani, A.: Protein contact network topology: a natural language for allostery. Curr. Opin. Struct. Biol. 31, 43–48 (2015)CrossRefGoogle Scholar
  18. 18.
    Tsai, C.J., Del Sol, A., Nussinov, R.: Allostery: absence of a change in shape does not imply that allostery is not at play. J. Mol. Biol. 378(1), 1–11 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alessio Martino
    • 1
  • Enrico Maiorino
    • 1
  • Alessandro Giuliani
    • 2
  • Mauro Giampieri
    • 1
  • Antonello Rizzi
    • 1
  1. 1.Department of Information Engineering, Electronics and TelecommunicationsUniversity of Rome La SapienzaRomeItaly
  2. 2.Department of Environment and Health, Istituto Superiore di SanitáRomeItaly

Personalised recommendations