Using Short-Range Interactions and Simulated Genetic Strategy to Improve the Protein Contact Map Prediction

  • Cosme E. Santiesteban Toca
  • Milton García-Borroto
  • Jesus S. Aguilar Ruiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7329)


Protein contact map prediction is one of the most important intermediate steps of the protein folding prediction problem. In this research we want to know how a decision tree predictor based on short-range interactions can learn the correlation among the covalent structures of a protein residues. The proposed solution predicts protein contact maps by the combination of a forest of 400 decision trees with an input codification for short-range interactions and a genetic-based edition method. The method’s performance was satisfactory, improving the accuracy instead using all information of the protein sequence. For a globulin data set the method can predict contacts with a maximal accuracy of 43%. The presented predictive model illustrates that short-range interactions play a predominant role in determining protein structure.


protein structure prediction protein contact map prediction short-range interactions decision trees edition method 


  1. 1.
    Ouzounis, C.A., Valencia, A.: Early bioinformatics: the birth of a discipline – a personal view. Bioinformatics 19(17), 2176–2190 (2003)CrossRefGoogle Scholar
  2. 2.
    Quan, Z.H., Zhang, G.-Z., Huang, D.S.: Combining a binary input encoding scheme with RBFNN for globulin protein inter-residue contact map prediction. Pattern Recognition Letters 26, 1543–1553 (2005)CrossRefGoogle Scholar
  3. 3.
    Glasgow, J., Kuo, T., Davies, J.: Protein structure from contact maps: A case-based reasoning approach. Inf. Sys. Front. 8, 29–36 (2006)CrossRefGoogle Scholar
  4. 4.
    Ramanathan, A.: Using Tensor Analysis to characterize Contact-map Dynamics of Proteins. PhD thesis, Carnegie Mellon University Pittsburgh, PA (2008)Google Scholar
  5. 5.
    Zhou, J., Arndt, D., Wishart, D.S., Lin, G., Shi, Y., Zhou, J., Arndt, D., Wishart, D.S., Lin, G.: Protein contact order prediction from primari sequences. BMC Bioinformatics 9(255), 1–21 (2008)zbMATHGoogle Scholar
  6. 6.
    Fariselli, P., Olmea, O., Valencia, A., Casadio, R.: Prediction of contact maps with neural networks and correlated mutations. Protein Engineering 14(11), 835–843 (2001)CrossRefGoogle Scholar
  7. 7.
    Pollastri, G., Baldi, P.: Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners. Bioinformatics 18, 1–9 (2002)CrossRefGoogle Scholar
  8. 8.
    Kim, H.: Computational analysis of hydrogen bonds in protein-RNA complexes for interaction patterns. FEBS Letters 552, 231–239 (2003)CrossRefGoogle Scholar
  9. 9.
    Martin, A.J.M., Walsh, I., Bau, D.: Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks. BMC Structural Biology 9(5), 1–38 (2009)Google Scholar
  10. 10.
    Ahmad, M., Mathkour, H.: An integrated approach for protein structure prediction using artificial neural network. In: 2010 Second International Conference on Computer Engineering and Applications, pp. 484–488. IEEE (2010)Google Scholar
  11. 11.
    Sinha, S., Durga Bhavani, S., Suvarnavani, K.: Mining of protein contact maps for protein fold prediction. WIREs Data Mining Knowl. Discov. 1(4), 362–368 (2011)CrossRefGoogle Scholar
  12. 12.
    Saraee, M., Korbekandi, H., Habibi, N.: Protein contact map prediction using committee machine approach. International Journal of Data Mining and Bioinformatics 2, 205–209 (2011)Google Scholar
  13. 13.
    Saraee, M.H., Habibi, N.K.: Protein contact map prediction based on an ensemble learning method. In: 2009 International Conference on Computer Engineering and Technology, vol. 2, pp. 205–209. IEEE (2009)Google Scholar
  14. 14.
    Kim, J., Kim, H., Min, H., Yoon, S.: Constructing accurate contact maps for hydroxyl-radical-cleavage-based high-throughput rna structure inference. IEEE Transactions on Biomedical Engineering 58(5), 1347–1355 (2011)CrossRefGoogle Scholar
  15. 15.
    Toca, C.E.S., Márquez Chamorro, A.E., Asencio Cortes, G., Aguilar Ruiz, J.S.: A Decision Tree-Based Method for Protein Contact Map Prediction. In: Giacobini, M. (ed.) EvoBIO 2011. LNCS, vol. 6623, pp. 153–158. Springer, Heidelberg (2011)Google Scholar
  16. 16.
    Santiesteban-Toca, C.E., Aguilar-Ruiz, J.S.: DTP: Decision Tree-Based Predictor of Protein Contact Map. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part II. LNCS, vol. 6704, pp. 367–375. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Valencia, A., Olmea, O.: Improving contact predictions by the combination of correlated mutations and other sources of sequence information. Protein Engineering 2, S25–S32 (1997)Google Scholar
  18. 18.
    Casadio, R., Fariselli, P.: A neural network based predictor of residue contacts in proteins. Protein Engineering 12(1), 15–21 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cosme E. Santiesteban Toca
    • 1
  • Milton García-Borroto
    • 1
  • Jesus S. Aguilar Ruiz
    • 2
  1. 1.Centro de BioplantasUniversity of Ciego de ÁvilaCuba
  2. 2.University of Pablo de OlavideSevillaSpain

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