DeepPPI: Boosting Prediction of Protein–Protein Interactions with Deep Neural Networks

TLDR

The complex language of eukaryotic gene expression is incompletely understood, and many disease‑associated protein variants have unknown mechanisms such as protein–protein interactions. The study aims to improve PPI prediction by developing DeepPPI, a deep neural network that learns protein representations from common descriptors. DeepPPI uses deep neural networks to learn protein representations from common descriptors through layer‑wise abstraction. DeepPPI achieved superior performance on the test set, with accuracy 92.50 %, precision 94.38 %, recall 90.56 %, specificity 94.49 %, MCC 85.08 %, and AUC 97.43 %, outperforming existing methods. Source code is available at http://ailab.ahu.edu.cn:8087/DeepPPI/index.html.

Abstract

The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many proteins variants statistically associated with human disease, nearly all such variants have unknown mechanisms, for example, protein–protein interactions (PPIs). In this study, we address this challenge using a recent machine learning advance-deep neural networks (DNNs). We aim at improving the performance of PPIs prediction and propose a method called DeepPPI (Deep neural networks for Protein–Protein Interactions prediction), which employs deep neural networks to learn effectively the representations of proteins from common protein descriptors. The experimental results indicate that DeepPPI achieves superior performance on the test data set with an Accuracy of 92.50%, Precision of 94.38%, Recall of 90.56%, Specificity of 94.49%, Matthews Correlation Coefficient of 85.08% and Area Under the Curve of 97.43%, respectively. Extensive experiments show that DeepPPI can learn useful features of proteins pairs by a layer-wise abstraction, and thus achieves better prediction performance than existing methods. The source code of our approach can be available via http://ailab.ahu.edu.cn:8087/DeepPPI/index.html.