Dataset Information

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 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.

SUBMITTER: Agarwal I

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

REPOSITORIES: biostudies-literature

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

Agarwal Ishaant I Kaczmar-Michalska Joanna J Nørrelykke Simon F SF Rzepiela Andrzej J AJ

IUCrJ 20240901 Pt 5

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 3 ...[more]

PMID: 39069881

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Project description:Recent advances in instrumentation and software have resulted in cryo-EM rapidly becoming the method of choice for structural biologists, especially for those studying the three-dimensional structures of very large macromolecular complexes. In this contribution, the tools available for macromolecular structure refinement into cryo-EM reconstructions that are available via CCP-EM are reviewed, specifically focusing on REFMAC5 and related tools. Whilst originally designed with a view to refinement against X-ray diffraction data, some of these tools have been able to be repurposed for cryo-EM owing to the same principles being applicable to refinement against cryo-EM maps. Since both techniques are used to elucidate macromolecular structures, tools encapsulating prior knowledge about macromolecules can easily be transferred. However, there are some significant qualitative differences that must be acknowledged and accounted for; relevant differences between these techniques are highlighted. The importance of phases is considered and the potential utility of replacing inaccurate amplitudes with their expectations is justified. More pragmatically, an upper bound on the correlation between observed and calculated Fourier coefficients, expressed in terms of the Fourier shell correlation between half-maps, is demonstrated. The importance of selecting appropriate levels of map blurring/sharpening is emphasized, which may be facilitated by considering the behaviour of the average map amplitude at different resolutions, as well as the utility of simultaneously viewing multiple blurred/sharpened maps. Features that are important for the purposes of computational efficiency are discussed, notably the Divide and Conquer pipeline for the parallel refinement of large macromolecular complexes. Techniques that have recently been developed or improved in Coot to facilitate and expedite the building, fitting and refinement of atomic models into cryo-EM maps are summarized. Finally, a tool for symmetry identification from a given map or coordinate set, ProSHADE, which can identify the point group of a map and thus may be used during deposition as well as during molecular visualization, is introduced.

Dataset Information

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

Publications

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

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