Ligand identification in CryoEM and X-ray maps using deep learning.

ABSTRACT:

Motivation

Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule ligands bind to active sites of interest. However, the interpretation of density maps is challenging, and cognitive bias can sometimes mislead investigators into modeling fictitious compounds. Ligand identification can be aided by automatic methods, but existing approaches are available only for X-ray diffraction and are based on iterative fitting or feature-engineered machine learning rather than end-to-end deep learning.

Results

Here, we propose to identify ligands using a deep-learning approach that treats density maps as 3D poin

SUBMITTER: Karolczak J 

PROVIDER: S-EPMC11709248 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature