Protein-protein Interaction Prediction using Desolvation Energies and Interface Properties

An important aspect in understanding and classifying protein-protein interactions (PPI) is to analyze their interfaces in order to distinguish between transient and obligate complexes. We propose a classification approach to discriminate between these two types of complexes. Our approach has two important aspects. First, we have used desolvation energies -amino acid and atom type -of the residues present in the interface, which are the input features of the classifiers. Principal components of the data were found and then the classification is performed via linear dimensionality reduction (LDR) methods. Second, we have investigated various interface properties of these interactions. From the analysis of protein quaternary structures, physicochemical properties are treated as the input features of the classifiers. Various features are extracted from each complex, and the classification is performed via different linear dimensionality reduction (LDR) methods. The results on standard benchmarks of transient and obligate protein complexes show that (i) desolvation energies are better discriminants than solvent accessibility and conservation properties, among others, and (ii) the proposed approach outperforms previous solvent accessible area based approaches using support vector machines.