Refinement of cryo-EM 3D maps with a self-supervised denoising model: crefDenoiser.

ABSTRACT: Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging macromolecular structures. However, despite extensive processing of large image sets collected in cryo-EM experiments to amplify the signal-to-noise ratio, the reconstructed 3D protein-density maps are often limited in quality due to residual noise, which in turn affects the accuracy of the macromolecular representation. Here, crefDenoiser is introduced, a denoising neural network model designed to enhance the signal in 3D cryo-EM maps produced with standard processing pipelines. The crefDenoiser model is trained without the need for `clean' ground-truth target maps. Instead, a custom dataset is employed, composed of real noisy protein half-maps sourced from the Electron Microscopy Data Bank repository. Competing wi

SUBMITTER: Agarwal I 

PROVIDER: S-EPMC11364040 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature