The single-particle cryo-EM field faces the persistent challenge of preferred orientation, lacking general computational solutions. We introduce cryoPROS, an AI-based approach designed to address the above issue. By generating the auxiliary particles with a conditional deep generative model, cryoPROS addresses the intrinsic bias in orientation estimation for the observed particles. We effectively employed cryoPROS in the cryo-EM single particle analysis of the hemagglutinin trimer, showing the ability to restore the near-atomic resolution structure on non-tilt data. Moreover, the enhanced version named cryoPROS-MP significantly improves the resolution of the membrane protein NaX using the no-tilted data that contains the effects of micelles. Compared to the classical approaches, cryoPROS does not need special experimental or image acquisition techniques, providing a purely computational yet effective solution for the preferred orientation problem. Finally, we conduct extensive experiments that establish the low risk of model bias and the high robustness of cryoPROS.

翻译：单颗粒冷冻电镜领域长期面临择优取向这一持续挑战，目前尚无通用的计算解决方案。我们提出cryoPROS，一种基于人工智能的方法用于解决上述问题。通过条件深度生成模型生成辅助粒子，cryoPROS能够校正观测粒子在取向估计中的固有偏差。我们成功将cryoPROS应用于血凝素三聚体的冷冻电镜单颗粒分析中，展示了其在非倾斜数据上恢复近原子分辨率结构的能力。此外，增强版本cryoPROS-MP利用包含胶束效应的非倾斜数据，显著提升了膜蛋白NaX的分辨率。与经典方法相比，cryoPROS无需特殊的实验或图像采集技术，为择优取向问题提供了一种纯计算且高效解决方案。最后，我们通过大量实验验证了cryoPROS的低模型偏差风险与高鲁棒性。