用分块建模方法提升冷冻电镜中异构生物分子的三维重构精度
Reconstructing Heterogeneous Biomolecules via Hierarchical Gaussian Mixtures and Part Discovery
- 基于分层高斯混合模型，先分割粒子部件以引入结构先验
- 在CryoBench基准上达到新最佳性能，能分辨缺失部件和柔性结构
- 适合研究复杂分子构象变化或成分差异的生物学家与计算结构生物学家
冷冻电镜是分子生物学的变革性技术，通过计算方法从极噪声的二维电子显微图像中推断出原子级分辨率的三维分子结构。当前研究前沿是如何建模粒子在非刚性构象灵活性和组成变异下的结构，包括部分缺失的情况。本文提出一种新颖的3D重建框架——CryoSPIRE，基于分层高斯混合模型，受4D场景重建中的高斯点云启发。该模型首先进行部件级分割，提供关键归纳偏置，以应对构象与组成双重变异性。在复杂实验数据集上，CryoSPIRE成功揭示了具有生物学意义的结构，并在用于评估冷冻电镜异构性方法的基准CryoBench上建立新状态。
原文摘要 · Abstract (English)
Cryo-EM is a transformational paradigm in molecular biology where computational methods are used to infer 3D molecular structure at atomic resolution from extremely noisy 2D electron microscope images. At the forefront of research is how to model the structure when the imaged particles exhibit non-rigid conformational flexibility and compositional variation where parts are sometimes missing. We introduce a novel 3D reconstruction framework with a hierarchical Gaussian mixture model, inspired in part by Gaussian Splatting for 4D scene reconstruction. In particular, the structure of the model is grounded in an initial process that infers a part-based segmentation of the particle, providing essential inductive bias in order to handle both conformational and compositional variability. The framework, called CryoSPIRE, is shown to reveal biologically meaningful structures on complex experimental datasets, and establishes a new state-of-the-art on CryoBench, a benchmark for cryo-EM heterogeneity methods.
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