DGSite: A Well-Calibrated Deep Learning Framework for Protein-RNA Binding Site Prediction

Protein-RNA interactions play pivotal roles in biological processes. Despite the development of numerous computational models for protein-RNA binding site prediction, they still face critical challenges, including insufficient integration of global and local features, class imbalance undermining precision-recall, and poorly calibrated predictions. To address these issues, we first constructed a larger and more comprehensive data set (Train-1086 and Test-107). Specifically, we developed DGSite, a novel deep learning framework that captures long-range dependencies via the simplified Deformable Attention Transformer and extracts multiscale local contextual features using Graph Attention Networks. To mitigate class imbalance and reduce model uncertainty, we introduced Adaptive Bias Loss (ABL), based on optimal transport theory. On this foundation, we proposed a hybrid loss function, ABL+FL, that adaptively combines the complementary strengths of ABL and Focal Loss (FL) to achieve a superior balance between precision, recall, and calibration. Extensive experiments demonstrated that DGSite achieves high performance on both Test-107 and established classic data sets. The ABL+FL loss not only balanced precision and recall but also significantly reduced model uncertainty, yielding well-calibrated and more trustworthy predictions. Moreover, case-study visualizations confirmed the model's ability to minimize false positives and provide interpretable insights. Collectively, DGSite serves as a robust, high-performance, and well-calibrated computational tool for pinpointing protein-RNA binding sites.