An accurate classification of native and non-native protein-protein interactions using supervised and semi-supervised learning approaches

The progress in experimental and computational structural biology has led to a rapid growth of experimentally resolved structures and computational models of proteinprotein interactions. However, distinguishing between the physiological and non-physiological interactions remains a challenging problem. In this work, two related problems of interface classification have been addressed. The first problem is concerned with classification of the physiological and crystal-packing interactions. The second problem deals with the classification of the physiological interactions, or their accurate models, and decoys obtained from the inaccurate docking models. We have defined a universal set of interface features and employed supervised and semi-supervised learning approaches to accurately classify the interactions in both problems. Furthermore, we formulated the second problem as a semi-supervised learning problem and employed a transductive SVM to improve the accuracy of classification. Finally, we showed that using the scoring functions from the obtained classifiers, one can improve the accuracy of the docking methods.