In the past decade, deep conditional generative models have revolutionized
the generation of realistic images, extending their application from
entertainment to scientific domains. Single-particle cryo-electron microscopy
(cryo-EM) is crucial in resolving near-atomic resolution 3D structures of
proteins, such as the SARS-COV-2 spike protein. To achieve high-resolution
reconstruction, AI models for particle picking and pose estimation have been
adopted. However, their performance is still limited as they lack high-quality
annotated datasets. To address this, we introduce physics-informed generative
cryo-electron microscopy (GenEM), which for the first time integrates
physical-based cryo-EM simulation with a generative unpaired noise translation
to generate physically correct synthetic cryo-EM datasets with realistic
noises. Initially, GenEM simulates the cryo-EM imaging process based on a
virtual specimen. To generate realistic noises, we leverage an unpaired noise
translation via contrastive learning with a novel mask-guided sampling scheme.
Extensive experiments show that GenEM is capable of generating realistic
cryo-EM images. The generated dataset can further enhance particle picking and
pose estimation models, eventually improving the reconstruction resolution. We
will release our code and annotated synthetic datasets.

提出了一种新的方法 ——GenEM，该方法是将物理准确的冷冻电镜模拟和生成式非成对噪声转换相结合，生成具有真实噪声的合成冷冻电镜数据集，用于改善高分辨率蛋白质结构的重建分辨率。

GenEM: 物理信息生成式低温电子显微镜

GenEM: Physics-Informed Generative Cryo-Electron Microscopy

In single-particle cryo-electron microscopy (cryo-EM), the efficient
determination of orientation parameters for 2D projection images poses a
significant challenge yet is crucial for reconstructing 3D structures. This
task is complicated by the high noise levels present in the cryo-EM datasets,
which often include outliers, necessitating several time-consuming 2D clean-up
processes. Recently, solutions based on deep learning have emerged, offering a
more streamlined approach to the traditionally laborious task of orientation
estimation. These solutions often employ amortized inference, eliminating the
need to estimate parameters individually for each image. However, these methods
frequently overlook the presence of outliers and may not adequately concentrate
on the components used within the network. This paper introduces a novel
approach that uses a 10-dimensional feature vector to represent the orientation
and applies a Quadratically-Constrained Quadratic Program to derive the
predicted orientation as a unit quaternion, supplemented by an uncertainty
metric. Furthermore, we propose a unique loss function that considers the
pairwise distances between orientations, thereby enhancing the accuracy of our
method. Finally, we also comprehensively evaluate the design choices involved
in constructing the encoder network, a topic that has not received sufficient
attention in the literature. Our numerical analysis demonstrates that our
methodology effectively recovers orientations from 2D cryo-EM images in an
end-to-end manner. Importantly, the inclusion of uncertainty quantification
allows for direct clean-up of the dataset at the 3D level. Lastly, we package
our proposed methods into a user-friendly software suite named cryo-forum,
designed for easy accessibility by the developers.

一种基于深度学习的方法，使用 10 维特征向量表示方向，并应用二次约束二次规划推导出预测的方向作为单位四元数，附加不确定度指标，提出了一种考虑方向之间的成对距离的独特损失函数以提高方法的准确性，在文献中全面评估了编码器网络的设计选择。我们的数值分析证明了我们的方法在端到端的方式从 2D 冷冻电子显微镜图像中有效地恢复方向。重要的是，包括不确定度量化，允许在 3D 级别直接清理数据集。最后，我们将我们提出的方法打包到一个名为 cryo-forum 的开发者易于访问的软件套件中。