A novel method to predict protein-protein interactions based on the information of protein sequence

Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Unfortunately, the experimental methods for identifying PPIs are both time-consuming and expensive. Therefore, it is important to develop computational approaches for predicting PPIs. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on learning algorithm-Extreme Learning Machine (ELM) combined with a novel representation of local protein sequence descriptors. The local descriptors account for the interactions between residues in both continuous and discontinuous regions of a protein sequence, thus this method enables us to extract more PPI information from the protein sequences. ELM is a kind of accurate and fast-learning innovative classification method based on the random generation of the input-to-hidden-units weights followed by the resolution of the linear equations to obtain the hidden-to-output weights. When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 89.09% prediction accuracy with 89.25% sensitivity at the precision of 88.96%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Achieved results show that the proposed approach is very promising for predicting PPI, and it can be a helpful supplement for PPIs prediction.