MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions.

ABSTRACT: Metalloproteins play indispensable roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and inflammation. Discovery of high-affinity ligands for metalloproteins powers the treatment of these pathologies. Extensive efforts have been made to develop in silico approaches, such as molecular docking and machine learning (ML)-based models, for fast identification of ligands binding to heterogeneous proteins, but few of them have exclusively concentrated on metalloproteins. In this study, we first compiled the largest metalloprotein-ligand complex dataset containing 3079 high-quality structures, and systematically evaluated the scoring and doc

SUBMITTER: Jiang D 

PROVIDER: S-EPMC9945430 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature