Refinement of cryo-EM 3D maps with a self-supervised denoising model: crefDenoiser
| Autor: | Ishaant Agarwal, Joanna Kaczmar-Michalska, Simon F. Nørrelykke, Andrzej J. Rzepiela |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | IUCrJ, Vol 11, Iss 5, Pp 821-830 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2052-2525 20522525 |
| DOI: | 10.1107/S2052252524005918 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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