Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles
This provides a purely computational solution for cryo-EM researchers facing preferred orientation issues, though it appears incremental as it builds on existing AI methods for a specific bottleneck.
The paper tackles the problem of preferred orientation in single-particle cryo-EM, which lacks general computational solutions, by introducing cryoPROS, an AI-based approach that generates auxiliary particles to address orientation bias, restoring near-atomic resolution structures for proteins like hemagglutinin trimer and improving resolution for membrane proteins such as NaX without requiring special experimental techniques.
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.