Computational Prediction of Protein-Protein Interactions in Plants Using Only Sequence Information

Protein-protein interactions (PPIs) in plants plays a significant role in plant biology and functional organization of cells. Although, a large amount of plant PPIs data have been generated by high-throughput techniques, but due to the complexity of plants cells, the PPIs pairs currently obtained by experimental methods cover only a small fraction of the complete plants PPIs network. In addition, the experimental approaches for identifying PPIs in plants are laborious, time-consuming, and costly. Hence, it is highly desirable to develop more efficient approaches to detect PPIs in plants. In this study, we present a novel computational method combining weighted sparse representation-based classifier (WSRC) with inverse fast Fourier transform (IFFT) representation scheme which was adopted in position specific scoring matrix (PSSM) to extract features from plant protein sequences. When performing the proposed method on the plant PPIs data set of Maize, we achieved excellent results with high accuracies of 89.12%. To further assess the prediction performance of the proposed approach, we compared it with the state-of-art support vector machine (SVM) classifier. Experimental results demonstrated that the proposed method has a great potential to become a powerful tool for exploring the plants cells function.