Cryo-electron microscopy (cryo-EM) emerges as a pivotal technology for
determining the architecture of cells, viruses, and protein assemblies at
near-atomic resolution. Traditional particle picking, a key step in cryo-EM,
struggles with manual effort and automated methods' sensitivity to low
signal-to-noise ratio (SNR) and varied particle orientations. Furthermore,
existing neural network (NN)-based approaches often require extensive labeled
datasets, limiting their practicality. To overcome these obstacles, we
introduce cryoMAE, a novel approach based on few-shot learning that harnesses
the capabilities of Masked Autoencoders (MAE) to enable efficient selection of
single particles in cryo-EM images. Contrary to conventional NN-based
techniques, cryoMAE requires only a minimal set of positive particle images for
training yet demonstrates high performance in particle detection. Furthermore,
the implementation of a self-cross similarity loss ensures distinct features
for particle and background regions, thereby enhancing the discrimination
capability of cryoMAE. Experiments on large-scale cryo-EM datasets show that
cryoMAE outperforms existing state-of-the-art (SOTA) methods, improving 3D
reconstruction resolution by up to 22.4%.

利用少样本学习的蒙版自编码器方法（cryoMAE），克服了冷冻电子显微镜中粒子选择的问题，提高了三维重建分辨率 22.4%。

CryoMAE：基于掩蔽自动编码器的少样本冷冻电镜粒子挑选

CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders

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

Cryo-electron microscopy (cryoEM) is an increasingly popular method for
protein structure determination. However, identifying a sufficient number of
particles for analysis (often >100,000) can take months of manual effort.
Current computational approaches are limited by high false positive rates and
require significant ad-hoc post-processing, especially for unusually shaped
particles. To address this shortcoming, we develop Topaz, an efficient and
accurate particle picking pipeline using neural networks trained with few
labeled particles by newly leveraging the remaining unlabeled particles through
the framework of positive-unlabeled (PU) learning. Remarkably, despite using
minimal labeled particles, Topaz allows us to improve reconstruction resolution
by up to 0.15 {\AA} over published particles on three public cryoEM datasets
without any post-processing. Furthermore, we show that our novel
generalized-expectation criteria approach to PU learning outperforms existing
general PU learning approaches when applied to particle detection, especially
for challenging datasets of non-globular proteins. We expect Topaz to be an
essential component of cryoEM analysis.

本文提出了一种基于神经网络，结合未标记样本的 PU 学习框架来进行高效准确颗粒自动挑选的方法 Topaz，并展示其在三个公共 cryoEM 数据集中的应用能够获得比已有方法更高的重构分辨率，尤其是对于非球形蛋白等难以处理的数据集。