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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.06332 |
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| _version_ | 1866910060444123136 |
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| author | Gu, Jeffrey Jeon, Minkyu Ma, Ambri Yeung-Levy, Serena Zhong, Ellen D. |
| author_facet | Gu, Jeffrey Jeon, Minkyu Ma, Ambri Yeung-Levy, Serena Zhong, Ellen D. |
| contents | Cryo-electron microscopy (cryo-EM) is an indispensable technique for determining the 3D structures of dynamic biomolecular complexes. While typically applied to image a single molecular species, cryo-EM has the potential for structure determination of many targets simultaneously in a high-throughput fashion. However, existing methods typically focus on modeling conformational heterogeneity within a single or a few structures and are not designed to resolve compositional heterogeneity arising from mixtures of many distinct molecular species. To address this challenge, we propose CryoHype, a transformer-based hypernetwork for cryo-EM reconstruction that dynamically adjusts the weights of an implicit neural representation. Using CryoHype, we achieve state-of-the-art results on a challenging benchmark dataset containing 100 structures. We further demonstrate that CryoHype scales to the reconstruction of 1,000 distinct structures from unlabeled cryo-EM images in the fixed-pose setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06332 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks Gu, Jeffrey Jeon, Minkyu Ma, Ambri Yeung-Levy, Serena Zhong, Ellen D. Computer Vision and Pattern Recognition Cryo-electron microscopy (cryo-EM) is an indispensable technique for determining the 3D structures of dynamic biomolecular complexes. While typically applied to image a single molecular species, cryo-EM has the potential for structure determination of many targets simultaneously in a high-throughput fashion. However, existing methods typically focus on modeling conformational heterogeneity within a single or a few structures and are not designed to resolve compositional heterogeneity arising from mixtures of many distinct molecular species. To address this challenge, we propose CryoHype, a transformer-based hypernetwork for cryo-EM reconstruction that dynamically adjusts the weights of an implicit neural representation. Using CryoHype, we achieve state-of-the-art results on a challenging benchmark dataset containing 100 structures. We further demonstrate that CryoHype scales to the reconstruction of 1,000 distinct structures from unlabeled cryo-EM images in the fixed-pose setting. |
| title | CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.06332 |