Feature selection and classification of protein protein complexes based on their binding affinities using machine learning approaches

Protein protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protien- protien complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein protein complexes with their binding affinities using machine learning approaches.we set up a database of 185 protein Protein complexes for which the interacting pairs are lieteroditners and their experimental biinding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein protein complexes into high combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for indentifying the interacting partners in protein protein Interaction networks and human pathogen interactions based on the strength of interactions.