Kavli Affiliate: Yi Zhou
| First 5 Authors: Yi Zhou, Yilai Li, Jing Yuan, Quanquan Gu,
| Summary:
Cryo-electron microscopy (cryo-EM) is a powerful technique in structural
biology and drug discovery, enabling the study of biomolecules at high
resolution. Significant advancements by structural biologists using cryo-EM
have led to the production of over 38,626 protein density maps at various
resolutions1. However, cryo-EM data processing algorithms have yet to fully
benefit from our knowledge of biomolecular density maps, with only a few recent
models being data-driven but limited to specific tasks. In this study, we
present CryoFM, a foundation model designed as a generative model, learning the
distribution of high-quality density maps and generalizing effectively to
downstream tasks. Built on flow matching, CryoFM is trained to accurately
capture the prior distribution of biomolecular density maps. Furthermore, we
introduce a flow posterior sampling method that leverages CRYOFM as a flexible
prior for several downstream tasks in cryo-EM and cryo-electron tomography
(cryo-ET) without the need for fine-tuning, achieving state-of-the-art
performance on most tasks and demonstrating its potential as a foundational
model for broader applications in these fields.
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