DeepEMhancer: a deep learning solution for cryo-EM volume post-processing.

ABSTRACT: Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on global B-factor correction, suffer from limitations. For instance, they ignore the heterogeneity in the map local quality that reconstructions tend to exhibit. Aiming to overcome these problems, we present DeepEMhancer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental maps and maps sharpened using their respective atomic models, DeepEMhancer has learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step.

SUBMITTER: Sanchez-Garcia R 

PROVIDER: S-EPMC8282847 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature