Machine learning based identification of protein-protein interactions using derived features of physiochemical properties and evolutionary profiles

Proteins are the central constitute of a cell or biological system. Proteins execute their functions by interacting with other molecules such as RNA, DNA and other proteins. The major functionality of protein-protein interactions (PPIs) is the execution of biochemical activities in living species. Therefore, an accurate identification of PPIs becomes a challenging and demanding task for investigators from last few decades. Various traditional and computational methods have been applied but they have not achieved quite encouraging results. In order to extend the concept of computational model by incorporating intelligent, contemporary machine learning algorithms have been utilized for identification of PPIs. In this prediction model, protein sequences are expressed by using two distinct feature extraction methods namely: physiochemical properties of amino acids and evolutionary profiles method position specific scoring matrix (PSSM). Jackknife test and numerous performance parameters namely: specificity, recall, accuracy, MCC, precision, and F-measure were employed to compute the predictive quality of proposed model. After empirical analysis, it is determined that the proposed prediction model yielded encouraging predictive outcomes compared to existing state-of-the-art models. This achievement is ascribed with PSSM because it has clearly discerned a motif of PPIs. It is realized that the proposed prediction model will lead to be a practical and very useful tool for research community.