论文标题
从低分辨率的小分辨率组合使用小型数据集的低分子重建问题的鲁棒性解决方案
Robust ab initio solution of the cryo-EM reconstruction problem at low resolution with small data sets
论文作者
论文摘要
在过去的十年中，单个粒子的冷冻电子显微镜已成为结构生物学的关键工具，能够从数十万（嘈杂的）二维二维投影视图中在未知方向冻结的粒子的三维投影视图中实现原子尺度分辨率。这是通过使用一套软件工具来完成（i）（i）识别大型显微照片中的粒子，（ii）获得低分辨率重建，（iii）完善这些低分辨率结构，（iv）最终与所获得的电子散射密度与构成大分子或大分子分子复合物的组成原子相匹配。 在这里，我们关注重建管道的第二阶段：从挑选的粒子图像中获得低分辨率模型。我们的目标是创建一种算法，该算法能够从小型数据集（按几千个选定的粒子的顺序进行重新构建）。更确切地说，我们提出了一种鲁棒，自动且足够快的算法，以便在显微镜实验期间生成数据时可以帮助评估粒子质量。
Single particle cryo-electron microscopy has become a critical tool in structural biology over the last decade, able to achieve atomic scale resolution in three dimensional models from hundreds of thousands of (noisy) two-dimensional projection views of particles frozen at unknown orientations. This is accomplished by using a suite of software tools to (i) identify particles in large micrographs, (ii) obtain low-resolution reconstructions, (iii) refine those low-resolution structures, and (iv) finally match the obtained electron scattering density to the constituent atoms that make up the macromolecule or macromolecular complex of interest. Here, we focus on the second stage of the reconstruction pipeline: obtaining a low resolution model from picked particle images. Our goal is to create an algorithm that is capable of ab initio reconstruction from small data sets (on the order of a few thousand selected particles). More precisely, we propose an algorithm that is robust, automatic, and fast enough that it can potentially be used to assist in the assessment of particle quality as the data is being generated during the microscopy experiment.
