Identification and analysis of types of biological protein-protein interactions and their interfaces to predict obligate and non-obligate complexes is a problem that has drawn the attention of the research community in the past few years. In this paper, we propose a prediction approach to predict these two types of complexes. We use desolvation energies - amino acid and atom type - of the residues present in the interface. The prediction is performed via two state-of-the-art classification techniques, namely linear dimensionality reduction (LDR) and support vector machines (SVM). The results on a newly compiled data set, namely BPPI, which is a joint and modified version of two well-known data sets consisting of 213 obligate and 303 non-obligate complexes, show that the best prediction is achieved with SVM (76.94% accuracy) when using desolvation energies of atom-type features. Also, the proposed approach outperforms the previous solvent accessible area-based approaches using SVM (75% accuracy) and LDR (73.06% accuracy). Moreover, a visual analysis of desolvation energies in obligate and non-obligate complexes shows that a few atom-type pairs are good descriptors for these types of complexes.

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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 nonobligate 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 homoDDIs are more likely to be present in obligate interactions.

An important issue in understanding and classifying proteinprotein interactions (PPI) is to characterize their interfaces in order to discriminate between transient and obligate complexes. We propose a classification approach to discriminate between these two types of complexes. Our approach uses contact and binding free energies of the residues present in the interaction, which are the input features for the classifiers. A total of 282 features are extracted for each complex, and the classification is performed via recently proposed dimensionality reduction (LDR) methods, including the well-know Fisher's discriminant analysis and two heteroscedastic approaches. The results on a standard benchmark of transient and obligate protein complexes show that LDR approaches achieve a very high classification accuracy (over 78%), outperforming various support vector machines and nearest-neighbor classifiers. An additional insight on the proposed approach and experiments on different subsets of features shows that solvation energies can be used in the classification, leading to a performance comparable to using the full binding free energies of the interaction.