CryoFM: A Flow-based Foundation Model for Cryo-EM Densities

Yi Zhou*, Yilai Li*, Jing Yuan*, Quanquan Gu#
ByteDance Research
*Equal Contribution     #Corresponding Author
{zhouyi.naive, yilai.li, yuanjing.eugene, quanquan.gu}@bytedance.com
Paper

The overview of cryoFM. In the training stage, cryoFM learns a vector field v_t(\mathbf{x}_t), whose corresponding probability flow generates the data distribution p_0(\mathbf{x}_0) of high-quality protein densities. In the inference stage, given a degraded observation \mathbf{y}, a likelihood term p_t(\mathbf{y}|\mathbf{x}_t) is incorporated to convert the unconditional vector field v_t(x_t) to a conditional one v_t(\mathbf{x}_t|\mathbf{y}), so that we can sample from the posterior distribution p_0(\mathbf{x}_0|\mathbf{y}). This enables signal restoration of the density map, resulting in improved resolution of the alpha helices in the shown case.

Abstract

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 resolutions. 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.

Results on 4 downstream tasks

BibTeX


        @article {zhou2024cryofm,
          title={CryoFM: A Flow-based Foundation Model for Cryo-EM Densities},
          author={Yi Zhou and Yilai Li and Jing Yuan and Quanquan Gu},
          year={2024},
          eprint={2410.08631},
          archivePrefix={arXiv},
          primaryClass={q-bio.BM},
          url={https://arxiv.org/abs/2410.08631},
        }