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)

Abstract

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.

Keywords

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

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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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