Protein-protein and protein-nucleic acid binding site prediction via interpretable hierarchical geometric deep learning.

ABSTRACT: Identification of protein-protein and protein-nucleic acid binding sites provides insights into biological processes related to protein functions and technical guidance for disease diagnosis and drug design. However, accurate predictions by computational approaches remain highly challenging due to the limited knowledge of residue binding patterns. The binding pattern of a residue should be characterized by the spatial distribution of its neighboring residues combined with their physicochemical information interaction, which yet cannot be achieved by previous methods. Here, we design GraphRBF, a hierarchical geometric deep learning model to learn residue binding patterns from big data. To achieve it, GraphRBF describes physicochemical information interactions by designing an enhanced graph

SUBMITTER: Zhang S 

PROVIDER: S-EPMC11528319 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature