DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.

ABSTRACT: While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they're computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-sp

SUBMITTER: Lu W 

PROVIDER: S-EPMC10844226 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature