Functional Protein Prediction Using HMM Based Feature Representation and Relevance Analysis

  • Diego Fabian Collazos-Huertas
  • Andres Felipe Giraldo-Forero
  • David Cárdenas-Peña
  • Andres Marino Álvarez-Meza
  • Germán Castellanos-Domínguez
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 232)

Abstract

The prediction of subcellular location aims to understand the biological processes being carried out within the cell. Here, a feature representation methodology is proposed to identify subcellular locations in gram-positive bacteria. Regarding this, each considered class is employed to train a hidden Markov model, and the probability of a sequence of amino acids, being generated by each of the trained models is employed as a feature in further classification stage. Our proposal is tested on a well known database, containing amino acids sequences of bacteria. For concrete testing, a percentage of less than 80% identity is studied, using a multi-label Support Vector Machines with soft margin classifier. Attained results show that our approach improves issues raised in PfamFeat. Moreover, it seems to be an appropriate tool for predicting subcellular location proteins.

Keywords

HMM Multiclass SVM Protein Subcellular Localization 

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References

  1. 1.
    Gardy, J.L., Brinkman, F.S.L.: Methods for predicting bacterial protein subcellular localization. Nature Reviews Microbiology 4(10), 741–751 (2006)CrossRefGoogle Scholar
  2. 2.
    Gardy, J.L., Spencer, C., Wang, K., Ester, M., Tusnady, G.E., Simon, I., Hua, S., Lambert, C., Nakai, K., Brinkman, F.S., et al.: Psort-b: Improving protein subcellular localization prediction for gram-negative bacteria. Nucleic Acids Research 31(13), 3613–3617 (2003)CrossRefGoogle Scholar
  3. 3.
    Yu, C.S., Lin, C.J., Hwang, J.K.: Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Science 13(5), 1402–1406 (2004)CrossRefGoogle Scholar
  4. 4.
    Lu, Z., Szafron, D., Greiner, R., Lu, P., Wishart, D., Poulin, B., Anvik, J., Macdonell, C., Eisner, R.: Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics 20(4), 547–556 (2004)CrossRefGoogle Scholar
  5. 5.
    Punta, M., Coggill, P.C., Eberhardt, R.Y., Mistry, J., Tate, J., Boursnell, C., Pang, N., Forslund, K., Ceric, G., Clements, J., Heger, A., Holm, L., Sonnhammer, E.L.L., Eddy, S.R., Bateman, A., Finn, R.D.: The Pfam protein families database. Nucleic Acids Research 40(Database issue), D290–D301 (2012)Google Scholar
  6. 6.
    Crammer, K.: On the algorithmic implementation of multiclass kernel-based vector machines. The Journal of Machine Learning Research 2, 265–292 (2002)MATHGoogle Scholar
  7. 7.
    Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  8. 8.
    Scholkopg, B., Smola, A.J.: Learning with Kernels. The MIT Press, Cambridge (2002)Google Scholar
  9. 9.
    Rey, S., Acab, M., Gardy, J.L., Laird, M.R., Lambert, C., Brinkman, F.S., et al.: Psortdb: a protein subcellular localization database for bacteria. Nucleic Acids Research 33(suppl. 1), D164–D168 (2005)Google Scholar
  10. 10.
    Li, W., Godzik, A.: Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics (Oxford, England) 22(13), 1658–1659 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Diego Fabian Collazos-Huertas
    • 1
  • Andres Felipe Giraldo-Forero
    • 1
  • David Cárdenas-Peña
    • 1
  • Andres Marino Álvarez-Meza
    • 1
  • Germán Castellanos-Domínguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizales - CaldasColombia

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