用可变形高斯表示法，同时解析冷冻电镜中蛋白质的构象与组成异质性。
Resolving compositional and conformational heterogeneity in cryo-EM with deformable 3D Gaussian representations
- 基于双编码器单解码器架构，将图像分解为可学习的高斯组件。
- 在公开数据集上复现了此前未观察到的细节，且保持原子级结构保真度。
- 适合研究蛋白质动态行为的生物学家和结构生物学家使用。
理解蛋白质灵活性及其与其他分子的动态相互作用对研究蛋白质功能至关重要。尽管冷冻电镜（cryo-EM）提供了直接观察大分子动态的可能，但对混合连续与离散结构状态的数据集进行计算分析仍是重大挑战。本文提出GaussianEM，一种基于高斯的伪原子框架，可同时从冷冻电镜图像中解析组成与构象异质性。GaussianEM采用双编码器-单解码器架构，将图像分解为可学习的高斯组件，通过调制参数编码变化。这种显式参数化实现了连续、直观的构象动态表示，天然保留局部结构完整性。通过在高斯空间建模位移，捕捉原子级构象景观，弥合密度图与全原子模型之间的差距。综合实验表明，GaussianEM成功重建复杂组成与构象变异性，并在公开数据集中揭示此前未见的细节。定量评估进一步证实其在不牺牲结构保真度的前提下，能够捕捉更广泛的构象多样性。
原文摘要 · Abstract (English)
Understanding protein flexibility and its dynamic interactions with other molecules is essential for studying protein function. Although cryogenic electron microscopy(cryo-EM) provides an opportunity to observe macromolecular dynamics directly, computational analysis of datasets mixing continuous and discrete structural states remains a formidable challenge. Here we introduce GaussianEM, a Gaussian-based pseudo-atomic framework that simultaneously resolves compositional and conformational heterogeneity from cryo-EM images. GaussianEM employs a dual-encoder-single-decoder architecture to decompose images into learnable Gaussian components, with variability encoded through modulated parameters. This explicit parameterization yields a continuous, intuitive representation of conformational dynamics that inherently preserves local structural integrity. By modeling displacements in Gaussian space, we capture atomic-scale conformational landscapes, bridging density maps and all-atom models. In comprehensive experiments, GaussianEM successfully reconstructs complex compositional and conformational variability,and resolves previously unobserved details in public datasets. Quantitative evaluations further confirm its ability to capture broader conformational diversity without sacrificing structural fidelity.
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