XGBPRH: Prediction of Binding Hot Spots at Protein–RNA Interfaces Utilizing Extreme Gradient Boosting
AbstractHot spot residues at protein–RNA complexes are vitally important for investigating the underlying molecular recognition mechanism. Accurately identifying protein–RNA binding hot spots is critical for drug designing and protein engineering. Although some progress has been made by utilizing various available features and a series of machine learning approaches, these methods are still in the infant stage. In this paper, we present a new computational method named XGBPRH, which is based on an eXtreme Gradient Boosting (XGBoost) algorithm and can effectively predict hot spot residues in protein–RNA interfaces utilizing an optimal set of properties. Firstly, we download 47 protein–RNA complexes and calculate a total of 156 sequence, structure, exposure, and network features. Next, we adopt a two-step feature selection algorithm to extract a combination of 6 optimal features from the combination of these 156 features. Compared with the state-of-the-art approaches, XGBPRH achieves better performances with an area under the ROC curve (AUC) score of 0.817 and an F1-score of 0.802 on the independent test set. Meanwhile, we also apply XGBPRH to two case studies. The results demonstrate that the method can effectively identify novel energy hotspots. View Full-Text
Share & Cite This Article
Deng, L.; Sui, Y.; Zhang, J. XGBPRH: Prediction of Binding Hot Spots at Protein–RNA Interfaces Utilizing Extreme Gradient Boosting. Genes 2019, 10, 242.
Deng L, Sui Y, Zhang J. XGBPRH: Prediction of Binding Hot Spots at Protein–RNA Interfaces Utilizing Extreme Gradient Boosting. Genes. 2019; 10(3):242.Chicago/Turabian Style
Deng, Lei; Sui, Yuanchao; Zhang, Jingpu. 2019. "XGBPRH: Prediction of Binding Hot Spots at Protein–RNA Interfaces Utilizing Extreme Gradient Boosting." Genes 10, no. 3: 242.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.