High-resolution real-space reconstruction of cryo-EM structures using a neural field network

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. This study presents a cryogenic electron microscopy neural field reconstruction network using real-space optimization, enhancing the resolution in cryogenic electron microscopy reconstruction.