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The single-particle cryo-EM field faces the persistent challenge of preferredorientation, lacking general computational solutions. We introduce cryoPROS, anAI-based approach designed to address the above issue. By generating theauxiliary particles with a conditional deep generative model, cryoPROSaddresses the intrinsic bias in orientation estimation for the observedparticles. We effectively employed cryoPROS in the cryo-EM single particleanalysis of the hemagglutinin trimer, showing the ability to restore thenear-atomic resolution structure on non-tilt data. Moreover, the enhancedversion named cryoPROS-MP significantly improves the resolution of the membraneprotein NaX using the no-tilted data that contains the effects of micelles.Compared to the classical approaches, cryoPROS does not need specialexperimental or image acquisition techniques, providing a purely computationalyet effective solution for the preferred orientation problem. Finally, weconduct extensive experiments that establish the low risk of model bias and thehigh robustness of cryoPROS.

Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles