Predicting nucleic acid binding sites by attention map-guided graph convolutional network with protein language embeddings and physicochemical information.

ABSTRACT: Protein-nucleic acid binding sites play a crucial role in biological processes such as gene expression, signal transduction, replication, and transcription. In recent years, with the development of artificial intelligence, protein language models, graph neural networks, and transformer architectures have been adopted to develop both structure-based and sequence-based predictive models. Structure-based methods benefit from the spatial relationship between residues and have shown promising performance. However, structure-based information requires 3D protein structures, which is a challenge for large-scale protein sequence spaces. To address this limitation, researchers have attempted to use predicted protein structure information to guide binding site prediction. While this strategy has imp

SUBMITTER: Li X 

PROVIDER: S-EPMC12415854 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature