Feature Selection for Data Driven Prediction of Protein Model Quality

Features selection to assess the accuracy of a protein three-dimensional model, when only the protein sequence is known, is a challenging task because it is not clear which features are most important and how they should best be combined. We present the results of an information theory-based approach to select an optimal subset of features for the prediction of protein model quality. The optimal subset of features was calculated by means of a backward selection procedure, starting from a set of structural features belonging to the following three categories: atomic interactions, solvent accessibility, and secondary structure. Three statistical-learning approaches were evaluated to predict the quality of a protein model starting from an optimum subset of features. The performances of a probabilistic classifier modeled by means of a kernel probability density estimation method (KPDE) were compared with those of a feed-forward artificial neural network (ANN) and a support vector machine (SVM).