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

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.