Prediction of biological protein–protein interactions using atom‐type and amino acid properties

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.