MegSite: an accurate nucleic acid-binding residue prediction method based on multimodal protein language model.

ABSTRACT: Accurate identification of nucleic acid-binding residues is crucial for understanding protein-nucleic acid interactions, which play a key role in gene expression research and the discovery of regulatory mechanisms. Despite numerous computational efforts to address this challenge, achieving high accuracy remains difficult due to the complexity of extracting meaningful insights from proteins. Here, we introduce MegSite, a novel multimodal protein language model-informed method that integrates discriminative knowledge from protein sequence, structure, and function. This work presents the first integration of ESM3 multimodal features for nucleic acid-binding site prediction. MegSite significantly outperforms existing prediction methods, as evidenced by its performance on multiple independent t

SUBMITTER: Hu F 

PROVIDER: S-EPMC12496013 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature