  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.
