用Transformer超网络一次性重建上千个生物分子结构，突破传统冷冻电镜局限。
CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks
- 基于Transformer构建超网络，动态调节隐式神经表示权重。
- 在含100个结构的基准数据集上达到当前最佳效果，可扩展至1000个结构。
- 适合高通量解析复杂混合物中多种分子结构的研究者。
冷冻电镜（cryo-EM）是解析动态生物大分子复合物三维结构的重要技术。尽管通常用于单个分子物种的成像，但冷冻电镜具备高通量同时解析多个目标的潜力。然而，现有方法多聚焦于单一或少数结构的构象异质性建模，难以应对由多种不同分子物种混合引起的组成异质性。为此，我们提出CryoHype，一种基于Transformer的超网络框架，通过动态调整隐式神经表示的权重实现重构。在包含100个结构的挑战性基准数据集上，CryoHype取得当前最优结果。进一步实验表明，在固定构象设置下，CryoHype可成功从无标签的冷冻电镜图像中重建出1000个不同的分子结构。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。