CryoPROS: Correcting misalignment caused by preferred orientation using AI-generated auxiliary particles

作者：Hui Zhang, Dihan Zheng, Qiurong Wu, Nieng Yan, Peng Han, Qi Hu, Ying Peng, Zhaofeng Yan, Zuoqiang Shi, Chenglong Bao, Mingxu Hu · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-59797-w · 被引用次数：9 · 研究领域：Advanced Electron Microscopy Techniques and Applications、Ion-surface interactions and analysis、Silicon Nanostructures and Photoluminescence

The preferred orientation phenomenon is a common issue in cryo-EM, posing a persistent challenge to conventional reconstruction methods. In this study, we introduce cryoPROS, a computational framework designed to correct misalignment caused by preferred orientation through co-refining the raw and auxiliary particles. These auxiliary particles, generated using a self-supervised deep generative model, enhance the alignment accuracy of particles in datasets affected by preferred orientation. CryoPROS achieved near-atomic resolution with the untilted HA-trimer dataset and successfully resolved high-resolution structures from three experimental datasets, including P001-Y, NaX, and hormone-sensitive lipase dimer, all affected by preferred orientation issues. Extensive experiments validate the robustness of cryoPROS and its minimal risk of introducing model bias. These findings suggest that in many cases thought to suffer from preferred orientation, addressing misalignment issues can lead to significant improvements in the density map. Preferred orientation in cryo-EM often limits structural resolution. Here, the authors present cryoPROS, a novel computational method that co-refines raw and deep generative auxiliary particles to correct misalignment, enabling near-atomic resolution from challenging datasets.