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Protein-protein interactions (PPI) are important in most biological processes and their study is crucial in many applications. Identification of types of protein complexes is a particular problem that has drawn the attention of the research community in the past few years. We focus on obligate and non-obligate complexes, their prediction and analysis. We propose a prediction model to distinguish between these two types of complexes, which uses desolvation energies of domain-domain interactions (DDI), pairs of atoms and amino acids present in the interfaces of such complexes. Principal components of the data were found and then the prediction is performed via linear dimensionality reduction (LDR) and support vector machines (SVM). Our results on a newly compiled dataset, namely binary-PPID, which is a joint and modified version of two well-known datasets consisting of 146 obligate and 169 non-obligate complexes, show that the best prediction is achieved with SVM (77.78%) when using desolvation energies of atom type features. Furthermore, a detailed analysis shows that different DDIs are present in obligate and non-obligate complexes, and that homo-DDIs are more likely to be present in obligate interactions.

Analysis of obligate and non-obligate complexes using desolvation energies in domain-domain interactions