Protein–protein interactions prediction based on ensemble deep neural networks

Abstract

Abstract Protein–protein interactions (PPIs) are of vital importance to most biological processes. Plenty of PPIs have been identified by wet-lab experiments in the past decades, but there are still abundant uncovered PPIs. Furthermore, wet-lab experiments are expensive and limited by the adopted experimental protocols. Although various computational models have been proposed to automatically predict PPIs and provided reliable interactions for experimental verification, the problem is still far from being solved. Novel and competent models are still anticipated. In this study, a neural network based approach called EnsDNN (Ensemble Deep Neural Networks) is proposed to predict PPIs based on different representations of amino acid sequences. Particularly, EnsDNN separately uses auto covariance descriptor, local descriptor, and multi-scale continuous and discontinuous local descriptor, to represent and explore the pattern of interactions between sequentially distant and spatially close amino acid residues. It then trains deep neural networks (DNNs) with different configurations based on each descriptor. Next, EnsDNN integrates these DNNs into an ensemble predictor to leverage complimentary information of these descriptors and of DNNs, and to predict potential PPIs. EnsDNN achieves superior performance with accuracy of 95.29%, sensitivity of 95.12%, and precision of 95.45% on predicting PPIs of Saccharomyces cerevisiae. Results on other five independent PPI datasets also demonstrate that EnsDNN gets better prediction performance than other related comparing methods.