Some New Features for Protein Fold Prediction

In this paper we propose several sets of new features for protein fold prediction. The first feature set consisting of 47 features uses only the sequence information. We also define four different sets of features based on hydrophobicity of amino acids. Each such set has 400 features which are motivated by folding energy modeling. To define these features we have considered pair-wise amino acids (AA) interaction potential. The effectiveness of the proposed feature sets is tested using multilayer perceptron and radial basis function networks to solve the 4 class (level 1) and 27 class (level 2) prediction problems as defined in the context of SCOP classification. Our investigation shows that such features have good discriminating powers in predicting protein folds.