Yue Huang, Chengguang Zhu, Xiaokang Yang, Manhua Liu
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In this paper, we propose a cryo-EM neural field reconstruction network using 3D spatial-domain optimization that learns a directional isotropic representation of the cryo-EM structure by mapping the spatial coordinates to the corresponding density values. We qualitatively and quantitatively evaluate the cryo-EM neural field reconstruction network on four datasets. The cryo-EM neural field reconstruction network improves the directional isotropy and 3D density resolution beyond the limits of existing algorithms in homogeneous reconstruction and resolves the missing elements of SARS-CoV-2 in heterogeneous reconstruction. Elucidating three-dimensional structures is crucial for unravelling the macromolecule function in structural biology. This study presents a cryogenic electron microscopy neural field reconstruction network using real-space optimization, enhancing the resolution in cryogenic electron microscopy reconstruction.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":null,"pages":null},"PeriodicalIF":18.8000,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High-resolution real-space reconstruction of cryo-EM structures using a neural field network\",\"authors\":\"Yue Huang, Chengguang Zhu, Xiaokang Yang, Manhua Liu\",\"doi\":\"10.1038/s42256-024-00870-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The elucidation of three-dimensional (3D) structures is crucial for unravelling the protein function and illuminating mechanisms in structural biology. Cryogenic electron microscopy (cryo-EM) single-particle analysis provides direct measurements to determine the structures of macromolecules. However, the main challenge is reconstructing high-resolution 3D structures from extremely noisy and randomly oriented two-dimensional projection images. Most existing methods involve the optimization of multiple two-dimensional slices in the Fourier domain but ignore the anisotropy among these slices, thereby limiting the reconstruction of high-frequency structures. In this paper, we propose a cryo-EM neural field reconstruction network using 3D spatial-domain optimization that learns a directional isotropic representation of the cryo-EM structure by mapping the spatial coordinates to the corresponding density values. We qualitatively and quantitatively evaluate the cryo-EM neural field reconstruction network on four datasets. The cryo-EM neural field reconstruction network improves the directional isotropy and 3D density resolution beyond the limits of existing algorithms in homogeneous reconstruction and resolves the missing elements of SARS-CoV-2 in heterogeneous reconstruction. Elucidating three-dimensional structures is crucial for unravelling the macromolecule function in structural biology. 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High-resolution real-space reconstruction of cryo-EM structures using a neural field network
The elucidation of three-dimensional (3D) structures is crucial for unravelling the protein function and illuminating mechanisms in structural biology. Cryogenic electron microscopy (cryo-EM) single-particle analysis provides direct measurements to determine the structures of macromolecules. However, the main challenge is reconstructing high-resolution 3D structures from extremely noisy and randomly oriented two-dimensional projection images. Most existing methods involve the optimization of multiple two-dimensional slices in the Fourier domain but ignore the anisotropy among these slices, thereby limiting the reconstruction of high-frequency structures. In this paper, we propose a cryo-EM neural field reconstruction network using 3D spatial-domain optimization that learns a directional isotropic representation of the cryo-EM structure by mapping the spatial coordinates to the corresponding density values. We qualitatively and quantitatively evaluate the cryo-EM neural field reconstruction network on four datasets. The cryo-EM neural field reconstruction network improves the directional isotropy and 3D density resolution beyond the limits of existing algorithms in homogeneous reconstruction and resolves the missing elements of SARS-CoV-2 in heterogeneous reconstruction. Elucidating three-dimensional structures is crucial for unravelling the macromolecule function in structural biology. This study presents a cryogenic electron microscopy neural field reconstruction network using real-space optimization, enhancing the resolution in cryogenic electron microscopy reconstruction.
期刊介绍:
Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements.
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