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arXiv · 2309.14954

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

Hui Zhang·Dihan Zheng·Qiurong Wu·Nieng Yan·Zuoqiang Shi·Mingxu Hu·Chenglong Bao
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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.

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Hui Zhang, Dihan Zheng, Qiurong Wu, Nieng Yan, Zuoqiang Shi, Mingxu Hu, Chenglong Bao. 2023-09-26. Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles. https://arxiv.org/abs/2309.14954

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