Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks.

ABSTRACT: Cryogenic electron microscopy (CryoEM) produces structures of macromolecules at near-atomic resolution. However, building molecular models with good stereochemical geometry from those structures can be challenging and time-consuming, especially when many structures are obtained from datasets with conformational heterogeneity. Here we present a model refinement protocol that automatically generates series of molecular models from CryoEM datasets, which describe the dynamics of the macromolecular system and have near-perfect geometry scores.

SUBMITTER: Chen M 

PROVIDER: S-EPMC11463374 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature