Attention-based Convolutional Neural Networks for Protein-Protein Interaction Site Prediction

Protein-protein interactions are of great importance in the life cycles of living cells. Accurate prediction of the proteinprotein interaction site (PPIs) from protein sequence improves our understanding of protein-protein interaction, contributes to the protein-protein docking and is crucial for drug design. However, practical experimental methods are costly and time-consuming so that many sequence-based computational methods have been developed. Most of those methods employ a sliding window approach, which utilize local neighbor information within a window size. However, they don’t distinguish and use the effect of each individual neighboring residue at different position. We propose a novel sequence-based deep learning method consisting of convolutional neural networks (CNNs) and attention mechanism to improve the performance of PPIs prediction. Our attention-based CNNs captures the different effect of each neighboring residue within a sliding window, and therefore making a better understanding of the local environment of target residue. We employ experiments on several public benchmark datasets. The experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art techniques. The source code can be obtained from https://github.com/biolushuai/attention-based-CNNsfor-PPIs-prediction.