美国加州大学洛杉矶分校Z. Hong Zhou课题组发现，通过自监督深度学习可克服冷冻电镜中的优选取向问题。相关论文于2024年11月18日在线发表在《自然—方法学》杂志上。

研究人员开发了spIsoNet，一款基于自监督深度学习的端到端软件，旨在解决由优选取向问题引起的图谱各向异性和颗粒错位问题。spIsoNet通过利用优选取向视角来恢复欠采样视角中的分子信息，改善了三维重建过程中角度各向同性和颗粒对准的准确性。

研究人员展示了spIsoNet在有限视角下生成近各向同性重建的能力，适用于代表性的生物系统，包括核糖体、β-半乳糖苷酶以及一个以前难以处理的血凝素三聚体数据集。spIsoNet还可以推广应用于子图像平均中，改善优选取向分子图谱的各向同性和颗粒对准。因此，spIsoNet无需额外的样本准备程序，提供了一种通用的计算解决方案来应对优选取向问题。

研究人员表示，尽管单颗粒冷冻电镜（cryo-EM）技术的进展已使得大分子复合物的结构能够达到原子分辨率，但颗粒取向偏差（即“优选取向”问题）仍然是大多数样本的难题。现有的解决方案依赖于应用于样本的生化和物理策略，这些方法通常复杂且具有挑战性。

附：英文原文

Title: Overcoming the preferred-orientation problem in cryo-EM with self-supervised deep learning

Author: Liu, Yun-Tao, Fan, Hongcheng, Hu, Jason J., Zhou, Z. Hong

Issue&Volume: 2024-11-18

Abstract: While advances in single-particle cryo-EM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the ‘preferred’ orientation problem) remains a complication for most specimens. Existing solutions have relied on biochemical and physical strategies applied to the specimen and are often complex and challenging. Here, we develop spIsoNet, an end-to-end self-supervised deep learning-based software to address map anisotropy and particle misalignment caused by the preferred-orientation problem. Using preferred-orientation views to recover molecular information in under-sampled views, spIsoNet improves both angular isotropy and particle alignment accuracy during 3D reconstruction. We demonstrate spIsoNet’s ability to generate near-isotropic reconstructions from representative biological systems with limited views, including ribosomes, β-galactosidases and a previously intractable hemagglutinin trimer dataset. spIsoNet can also be generalized to improve map isotropy and particle alignment of preferentially oriented molecules in subtomogram averaging. Therefore, without additional specimen-preparation procedures, spIsoNet provides a general computational solution to the preferred-orientation problem.

DOI: 10.1038/s41592-024-02505-1

Source: https://www.nature.com/articles/s41592-024-02505-1