GATBind: Accurate protein-RNA binding sites prediction via graph attention networks with pre-trained language model

Protein-RNA interactions play a pivotal role in various biological processes, making them essential for discovering novel therapeutic targets. Understanding these interactions is crucial for identifying potential drug targets and designing effective therapeutics. However, traditional experimental approaches are costly and time-consuming, highlighting the need for accurate and efficient computational methods to predict RNA-binding sites. In this study, we propose a novel method called GATBind, designed to predict RNA-binding sites on proteins. GATBind introduces a local environment-aware module that captures the local structural information surrounding target residues. Additionally, we incorporate sequence and structural features, including PSSM, HMM, DSSP, and sequence embeddings generated by the pre-trained protein language model ESMFold-2. These features are integrated and processed through a Graph Attention Network (GAT) module to predict RNA-binding sites accurately. Experimental results demonstrate that GATBind outperforms existing state-of-the-art methods, and case studies further validate its effectiveness in predicting RNA-binding sites.