提出首个可从头重建数十种复杂混合物的冷冻电镜方法
A meta-algorithm for ab initio reconstruction of complex mixtures in cryo-EM
- 通过迭代分类与聚合策略自动处理异质样本
- 在Tomotwin-100数据集上实现97%准确率（45类）
- 适用于无预过滤实验数据，适合自动化流程
我们提出一种系统化方法，用于生成和聚合多类别冷冻电镜重建任务。该方法形式化了研究人员常用的迭代分类与过滤策略，以处理不纯、异质的样品。据我们所知，这是首个能在包含数十种不同物种的数据集上成功实现从头重建的方法。在Tomotwin-100的45类子集上达到97%的准确率，在完整数据集上达75%。我们还成功从未经过滤的实验数据中恢复了核糖体组装状态。该方法的能力随计算资源增长，为现代实验环境中的自动化冷冻电镜工作流奠定了基础。
原文摘要 · Abstract (English)
We describe a systematic approach for spawning and aggregating multi-class cryo-EM reconstruction jobs. This approach formalizes standard ad hoc strategies of iterative classification and filtering typically used by practitioners to sort impure, heterogeneous samples. To our knowledge, this is the first method that can successfully perform ab initio reconstruction on datasets containing dozens of distinct species. We obtain 97% accuracy on ab initio reconstruction of a 45-class subset of Tomotwin-100, 75% accuracy on the full Tomotwin-100 dataset, and demonstrate recovery of ribosomal assembly states from an unfiltered experimental cryo-EM dataset. Our approach's capability scales with compute and lays the foundation for automated cryo-EM workflows in modern experimental settings.
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。