Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling.

ABSTRACT:

Motivation

The tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3-4.5 Å), improvement in the map quality facilitates structure modeling.

Results

We report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was test

SUBMITTER: Maddhuri Venkata Subramaniya SR 

PROVIDER: S-EPMC10444963 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature