Prediction of crystal packing and biological protein-protein interactions

Prediction of protein-protein interactions are important to understand any biological processes. The structural models of the complexes resulting from these interactions are necessary to understand those processes at the molecular level. X-ray crystallography is the most popular method to determine the three dimensional structures of protein complexes. However, some of the observed interactions in the structures of protein complexes determined by X-ray crystallography are crystal packing contacts and are not biologically relevant. Thus, it is important to discriminate between biologically relevant interactions and crystal packing contacts. We propose a classification approach to predict these two types of complexes. Our approach has two main features. Firstly, we have calculated various interface property features from the quaternary structures of these interactions. Various features are extracted for each complex, namely number-based and area-based amino acid compositions. Secondly, these features are treated as the input features of the classifiers. The classification is performed with support vector machines (SVM) and linear dimensionality reduction (LDR) coupled with Bayesian classifiers. The results on a standard benchmark dataset of crystal packing and biological protein complexes show increasing prediction accuracy when compared.