Liu Yun-Tao, Fan Hongcheng, Hu Jason J, Zhou Z Hong
Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA.
California NanoSystems Institute, University of California, Los Angeles, CA, USA.
bioRxiv. 2024 Apr 14:2024.04.11.588921. doi: 10.1101/2024.04.11.588921.
While advances in single-particle cryoEM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the so-called "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 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 capability of generating 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.
虽然单颗粒冷冻电镜技术的进步使得在原子分辨率下确定大分子复合物的结构成为可能，但颗粒取向偏差（即所谓的“优先”取向问题）对于大多数样本来说仍然是一个复杂因素。现有的解决方案依赖于应用于样本的生化和物理策略，且往往复杂且具有挑战性。在此，我们开发了spIsoNet，这是一款基于深度学习的端到端自监督软件，用于解决优先取向问题。通过使用优先取向视图来恢复欠采样视图中的分子信息，spIsoNet在三维重建过程中提高了角度各向同性和颗粒对齐精度。我们展示了spIsoNet从具有有限视图的代表性生物系统（包括核糖体、β-半乳糖苷酶和一个以前难以处理的血凝素三聚体数据集）生成近各向同性重建的能力。spIsoNet还可以推广应用于提高亚断层平均中优先取向分子的图谱各向同性和颗粒对齐。因此，无需额外的样本制备程序，spIsoNet为优先取向问题提供了一种通用的计算解决方案。
