2024Agarwal crefDenoiser

Citation

Agarwal, Ishaant / Kaczmar-Michalska, Joanna / Noerrelykke, Simon F. / Rzepiela, Andrzej. Refinement of Cryo-EM 3D Maps with Self-Supervised Denoising Model: crefDenoiser. 2024. IUCR J, Vol. 11, p. 821-830

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 with the current state-of-the-art, crefDenoiser is designed to optimize for the theoretical noise-free map during self-supervised training. We demonstrate that our model successfully amplifies the signal across a wide variety of protein maps, outperforming a classic map denoiser and following a network-based sharpening model. Without biasing the map, the proposed denoising method leads to improved visibility of protein structural features, including protein domains, secondary structure elements and modest high-resolution feature restoration.

Links

https://journals.iucr.org/m/issues/2024/05/00/fq5024/index.html