从冷冻电镜断层图中无监督分离分子构象与空间变换，识别细胞内真实形态。
Unsupervised SE(3) Disentanglement for in situ Macromolecular Morphology Identification from Cryo-Electron Tomography
- 通过新型多选学习模块，将空间变换与分子形态解耦。
- 在模拟与真实数据上发现此前未见的分子新形态。
- 无需人工调参，对噪声数据鲁棒，适合生物结构分析。
冷冻电镜断层成像（cryo-ET）可直接实现细胞内大分子的三维可视化，揭示其原位构象。该构象可视为从断层图中提取的子体积的SE(3)不变、去噪体数据表示。推断构象本质上是估计模板构象及其SE(3)变换的逆问题。现有基于期望最大化的方法常遗漏稀有但重要的构象，且需大量手动调参。为此，本文提出一种解耦的深度表征学习框架，将表示空间中的SE(3)变换与形态内容分离。框架包含一种新颖的多选学习模块，可在高噪声cryo-ET数据下实现有效解耦，所学形态内容用于生成模板构象。在模拟与真实cryo-ET数据集上的实验表明，该方法显著优于以往方法，并发现了此前未被识别的大分子构象。
原文摘要 · Abstract (English)
Cryo-electron tomography (cryo-ET) provides direct 3D visualization of macromolecules inside the cell, enabling analysis of their in situ morphology. This morphology can be regarded as an SE(3)-invariant, denoised volumetric representation of subvolumes extracted from tomograms. Inferring morphology is therefore an inverse problem of estimating both a template morphology and its SE(3) transformation. Existing expectation-maximization based solution to this problem often misses rare but important morphologies and requires extensive manual hyperparameter tuning. Addressing this issue, we present a disentangled deep representation learning framework that separates SE(3) transformations from morphological content in the representation space. The framework includes a novel multi-choice learning module that enables this disentanglement for highly noisy cryo-ET data, and the learned morphological content is used to generate template morphologies. Experiments on simulated and real cryo-ET datasets demonstrate clear improvements over prior methods, including the discovery of previously unidentified macromolecular morphologies.
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