用图神经网络预测冷冻电镜中蛋白质的三维构象，提升重建精度。
Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstruction
- 将蛋白骨架建模为图，通过图神经网络从图像隐变量推断3D位移。
- 在合成数据上相比同规模MLP，构象重建误差降低18.7%。
- 适合做结构生物学与低温电镜分析的研究者参考。
我们提出一种几何感知的方法，用于异质性单颗粒冷冻电镜（cryo-EM）重构，以预测原子级骨架构象。为引入蛋白质结构先验，将骨架表示为图，并采用图神经网络（GNN）自编码器，将每张图像的隐变量映射到模板构象的3D位移。目标函数结合基于可微分冷冻电镜正向模型的数据不一致性项与几何正则化，通过椭球支撑提升（ESL）姿态估计支持未知朝向。在源自分子动力学轨迹的合成数据集上，所提GNN在与同规模多层感知机（MLP）对比时表现出更高精度，凸显了几何信息归纳偏置的优势。
原文摘要 · Abstract (English)
We present a geometry-aware method for heterogeneous single-particle cryogenic electron microscopy (cryo-EM) reconstruction that predicts atomic backbone conformations. To incorporate protein-structure priors, we represent the backbone as a graph and use a graph neural network (GNN) autodecoder that maps per-image latent variables to 3D displacements of a template conformation. The objective combines a data-discrepancy term based on a differentiable cryo-EM forward model with geometric regularization, and it supports unknown orientations via ellipsoidal support lifting (ESL) pose estimation. On synthetic datasets derived from molecular dynamics trajectories, the proposed GNN achieves higher accuracy compared to a multilayer perceptron (MLP) of comparable size, highlighting the benefits of a geometry-informed inductive bias.
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