Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches.

ABSTRACT: Accurate prediction of nucleic binding residues is essential for the understanding of transcription and translation processes. Integration of feature- and template-based strategies could improve the prediction of these key residues in proteins. Nevertheless, traditional hybrid algorithms have been surpassed by recently developed deep learning-based methods, and the possibility of integrating deep learning- and template-based approaches to improve performance remains to be explored. To address these issues, we developed a novel structure-based integrative algorithm called NABind that can accurately predict DNA- and RNA-binding residues. A deep learning module was built based on the diversified sequence and structural descriptors and edge aggregated graph attention networks, while a template

SUBMITTER: Jiang Z 

PROVIDER: S-EPMC10482303 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature