High-resolution real-space reconstruction of cryo-EM structures using a neural field network
Year: 2024
Type: article
Abstract: The elucidation of three-dimensional (3D) structures is crucial to unraveling 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 2D projection images. Most existing methods primarily involve the optimization of multiple 2D slices in the Fourier domain but ignore the anisotropy among these slices, thus limiting the reconstruction of high-frequency structures. In this paper, we propose a cryo-EM neural field reconstruction network (cryoNeFEN), a new 3D spatial domain optimization paradigm, that learns a directional isotropic representation of the cryo-EM structure by mapping spatial coordinates to corresponding density values. We qualitatively and quantitatively evaluate cryoNeFEN on four experimental datasets. The results demonstrate the improved directional isotropy and 3D density resolution beyond the limits of existing algorithms in homogeneous reconstruction and resolve the missing elements of SARS-CoV-2 distinctly in heterogeneous reconstruction.
Source: Nature Machine Intelligence
Institution: Shanghai Jiao Tong University
Language: English
Subfield: Structural Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4400588667