The interaction of a protein with its environment can be understood and
controlled via its 3D structure. Experimental methods for protein structure
determination, such as X-ray crystallography or cryogenic electron microscopy,
shed light on biological processes but introduce challenging inverse problems.
Learning-based approaches have emerged as accurate and efficient methods to
solve these inverse problems for 3D structure determination, but are
specialized for a predefined type of measurement. Here, we introduce a
versatile framework to turn raw biophysical measurements of varying types into
3D atomic models. Our method combines a physics-based forward model of the
measurement process with a pretrained generative model providing a
task-agnostic, data-driven prior. Our method outperforms posterior sampling
baselines on both linear and non-linear inverse problems. In particular, it is
the first diffusion-based method for refining atomic models from cryo-EM
density maps.

通过结合物理学模型和先前训练的生成模型，我们引入了一个多功能框架，将不同类型的生物物理测量转化为三维原子模型，该方法在线性和非线性反问题上优于后验采样基线，特别是首次利用扩散方法从低温电子显微镜密度图中改进原子模型。

使用基于扩散的先验解决蛋白质空间中的逆问题

Solving Inverse Problems in Protein Space Using Diffusion-Based Priors

Cryo-electron microscopy (cryo-EM) has revolutionized experimental protein
structure determination. Despite advances in high resolution reconstruction, a
majority of cryo-EM experiments provide either a single state of the studied
macromolecule, or a relatively small number of its conformations. This reduces
the effectiveness of the technique for proteins with flexible regions, which
are known to play a key role in protein function. Recent methods for capturing
conformational heterogeneity in cryo-EM data model it in volume space, making
recovery of continuous atomic structures challenging. Here we present a fully
deep-learning-based approach using variational auto-encoders (VAEs) to recover
a continuous distribution of atomic protein structures and poses directly from
picked particle images and demonstrate its efficacy on realistic simulated
data. We hope that methods built on this work will allow incorporation of
stronger prior information about protein structure and enable better
understanding of non-rigid protein structures.

本研究提出了使用变分自编码器的深度学习方法，从挑选后的粒子图像中恢复连续分布的原子蛋白质结构和构象，并在现实模拟数据上展示了其有效性，希望能够将该方法应用于更好地理解非刚性蛋白结构。

使用 VAEs 从冷冻电子显微成像中推断出原子坐标的连续分布

Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM  Images using VAEs

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。