PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees

Abstract

Protein-RNA interactions play essential roles in many biological aspects. Quantifying the binding affinity of protein-RNA complexes is helpful to the understanding of protein-RNA recognition mechanism and identification of strong binding partners. Due to experimentally measured protein-RNA binding affinity data available is still limited to date, there is a pressing demand for accurate and reliable computational approaches.In this paper, we propose a computational approach, PredPRBA, which can effectively predict protein-RNA binding affinity using gradient boosted regression trees. We build a dataset of protein-RNA binding affinity that includes 103 protein-RNA complex structures manually collected from related literature. Then, we generate 37 kinds of sequence and structural features and explore the relationship between the features and protein-RNA binding affinity. We find that the binding affinity mainly depends on the structure of RNA molecules. According to the type of RNA associated with proteins composed of the protein-RNA complex, we split the 103 protein-RNA complexes into six categories. For each category, we build a gradient boosted regression tree (GBRT) model based on the generated features. We perform comprehensive evaluation for the proposed method on the binding affinity dataset using leave-one-out cross-validations. We show that PredPRBA achieves correlations ranging from 0.723 to 0.897 among six categories, which is significantly better than other machine learning methods and the pioneer protein-RNA binding affinity predictor SPOT-Seq-RNA. The PredPRBA webserver is freely available at http://PredPRBA.denglab.org.