Predicting the location of coordinated metal ion-ligand binding sites using geometry-aware graph neural networks.

ABSTRACT: More than 50 % of proteins bind to metal ions. Interactions between metal ions and proteins, especially coordinated interactions, are essential for biological functions, such as maintaining protein structure and signal transport. Physiological metal-ion binding prediction is pivotal for both elucidating the biological functions of proteins and for the design of new drugs. However, accurately predicting these interactions remains challenging. In this study, we proposed GPred, a novel structure-based method that transforms the 3-dimensional structure of a protein into a point cloud representation and then designs a geometry-aware graph neural network to learn the local structural properties of each amino acid residue under specific ligand-binding supervision. We trained our model to predict

SUBMITTER: Essien C 

PROVIDER: S-EPMC11750443 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature