Abstract
Protein-protein interactions (PPI) play an important role in the cell activities of organisms. The deep research about PPI can help humans understand the mechanism of life activities and apply protein functions better. Nowadays, PPI prediction algorithms based on amino acid sequences using the recurrent neural network (RNN) can overcome the disadvantages of traditional biological experimental methods and achieve high accuracy. However, these algorithms are usually time-consuming and cannot take full advantage of graphics processing units (GPU) with efficient computation performance to accelerate PPI prediction, because the RNN model considers the time series of sequences. In this paper, we propose an efficient algorithm based on the residual network (ResNet) model to predict PPI (ResPPI). Our algorithm uses the embedding method to represent amino acid sequences, combining the advantages of powerful feature extraction capabilities of the ResNet with deep layers and GPU performance. The experimental results show that the ResPPI algorithm can ensure high accuracy and reduce training time greatly. Based on the ordinary GPU device, compared with the state-of-the-art LSTM model, the speed of the ResPPI algorithm is five times faster than that of the LSTM, whereas the ResPPI algorithm can achieve similar accuracy to the LSTM. Besides, in the case of unbalanced datasets, the ResPPI algorithm can perform better.
Highlights
Protein-protein interactions (PPI) are the basis of many cellular biological processes [1] such as signal transduction, immune response, DNA replication and cell metabolism
This method cannot extract the deep features of proteins and achieve satisfying results [11]. For these defects of sequence models, in this paper, we propose an efficient algorithm based on the residual network (ResNet) to predict PPI (ResPPI)
In this paper, we propose an efficient algorithm to predict PPI based on amino acid sequences
Summary
Protein-protein interactions (PPI) are the basis of many cellular biological processes [1] such as signal transduction, immune response, DNA replication and cell metabolism. Chen et al proposed a method of hyperparameter estimation in the support vector machine (SVM) with GPU acceleration for PPI prediction This method cannot extract the deep features of proteins and achieve satisfying results [11]. For these defects of sequence models, in this paper, we propose an efficient algorithm based on the residual network (ResNet) to predict PPI (ResPPI). The ResPPI algorithm can ensure high accuracy and reduce the training time greatly, which improves the performance of PPI prediction It can provide a reference for the processing of other biological data such as drug-drug interactions [13] and protein-RNA interactions [14].
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