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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.

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