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- W4387151510 abstract "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." @default.
- W4387151510 created "2023-09-29" @default.
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- W4387151510 date "2023-09-26" @default.
- W4387151510 modified "2023-09-30" @default.
- W4387151510 title "Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles" @default.
- W4387151510 doi "https://doi.org/10.48550/arxiv.2309.14954" @default.
- W4387151510 hasPublicationYear "2023" @default.
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