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.

阐明三维 (3D) 结构对于阐明蛋白质功能和阐明结构生物学机制至关重要。低温电子显微镜 (cryo-EM) 单颗粒分析可提供直接测量以确定大分子的结构。然而，主要的挑战是从噪声极大且随机定向的二维投影图像中重建高分辨率 3D 结构。大多数现有方法涉及傅里叶域中多个二维切片的优化，但忽略了这些切片之间的各向异性，从而限制了高频结构的重建。在本文中，我们提出了一种使用 3D 空间域优化的冷冻电镜神经场重建网络，该网络通过将空间坐标映射到相应的密度值来学习冷冻电镜结构的方向各向同性表示。我们在四个数据集上定性和定量评估冷冻电镜神经场重建网络。冷冻电镜神经场重建网络提高了方向各向同性和 3D 密度分辨率，超越了现有算法在同质重建中的限制，并解决了异质重建中 SARS-CoV-2 缺失的元素。