A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites - Wang, Pan; Zhang, Guiyang; Yu, Zu-Guo; Huang, Guohua (Frontiers in Genetics, 2021)
The knowledge about protein-protein interactions is beneficial to understand cellular mechanism. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to limitation of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a deep learning and XGBoost-based method (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as feature extractor to remove redundant information of the protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved area under the receiver operating characteristic curve of 0.681, a recall of 0.624 and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validate the positive role of global features in predicting protein-protein interaction sites.