Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning.

ABSTRACT: Accurate and efficient prediction of protein-ligand interactions has been a long-lasting dream of practitioners in drug discovery. The insufficient treatment of hydration is widely recognized to be a major limitation for accurate protein-ligand scoring. Using an integration of molecular dynamics simulations on thousands of protein structures with novel big-data analytics based on convolutional neural networks and deep Taylor decomposition, we consistently identify here three different patterns of hydration to be essential for protein-ligand interactions. In addition to desolvation and water-mediated interactions, the formation of enthalpically favorable networks of first-shell water molecules around solvent-exposed ligand moieties is identified to be essential for protein-ligand binding. D

SUBMITTER: Mahmoud AH 

PROVIDER: S-EPMC9814895 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature