RPI-PLMGNN: Enhancing RNA-Protein Interaction Prediction with the Pretrained Large Language Models and Graph Neural...

RNA-protein interactions play key roles in many life processes, and their study is significant for understanding gene regulation, revealing disease pathogenesis, and developing novel RNA-targeted drugs. However, traditional RPI prediction methods are time-consuming and difficult to satisfy the needs of high-throughput studies. Additionally, existing methods rely solely on a manual feature extraction approach and fail to fully leverage the advantages of the pretrained large language models. In this paper, we propose RPI-PLMGNN, an innovative RPI prediction method that integrates multimodal feature fusion with a Graph Neural Networks framework. First, we adopt linear graph topology to characterize the RNA-protein interaction network. Second, RNAErnie and ESM2 are employed to extract sequence features for RNA and proteins, respectively. Structural features of RNA and proteins are then extracted from RNAFold and SOPMA, respectively, and concatenated with their corresponding sequence features to construct node representations for each modality. Finally, the graph topology features and node features are jointly processed by a hybrid Graph Neural Network architecture that integrates both Graph Attention Network and Gated Graph Convolutional Network modules to generate the final interaction predictions. Experimental results show that RPI-PLMGNN exhibits superior prediction performance on multiple benchmark data sets. Particularly noteworthy is that in cross-species validation, RPI-PLMGNN achieves 94.2, 92.8, 94.5, 97.5, 98.2, and 97.1% accuracy on six species test sets (RPI_C, RPI_D, RPI_E, RPI_H, RPI_M, and RPI_S), demonstrating excellent generalization ability. Extensive experiments show that RPI-PLMGNN is an efficient and accurate method for RPI prediction, offering a valuable tool for studying RNA-protein interaction mechanisms and related drug development.