cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination
cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination
复制标题
DOI：
10.1038/nmeth.4169
复制
发表时间：
2017-03-01
期刊：
影响因子：
48
通讯作者：
Brubaker, Marcus A.
中科院分区：
文献类型：
--
作者：
Punjani, Ali;Rubinstein, John L.;Brubaker, Marcus A.
单粒子电子冷冻显微镜(Cryo-EM)是确定生物大分子结构的有力手段。有了自动显微镜，低温电子显微镜的数据往往可以在几天内获得。然而，处理冷冻-EM图像数据以揭示蛋白质结构的异质性并将3D地图细化到高分辨率经常成为一个严重的瓶颈，需要专家干预、事先的结构知识以及在昂贵的计算机集群上进行数周的计算。在这里，我们展示了随机梯度下降(SGD)和分支界限最大似然优化算法允许在廉价的台式计算机上在几小时或几分钟内完成低温电磁结构确定的主要步骤。此外，具有贝叶斯边际化的SGD允许从头开始3D分类，能够自动分析和发现意外结构，而不会从参考地图中产生偏见。这些算法被组合在一个用户友好的计算机程序中，该程序名为COREOSPARC(http://www.cryosparc.com).
Single-particle electron cryomicroscopy (cryo-EM) is a powerful method for determining the structures of biological macromolecules. With automated microscopes, cryo-EM data can often be obtained in a few days. However, processing cryo-EM image data to reveal heterogeneity in the protein structure and to refine 3D maps to high resolution frequently becomes a severe bottleneck, requiring expert intervention, prior structural knowledge, and weeks of calculations on expensive computer clusters. Here we show that stochastic gradient descent (SGD) and branch-and-bound maximum likelihood optimization algorithms permit the major steps in cryo-EM structure determination to be performed in hours or minutes on an inexpensive desktop computer. Furthermore, SGD with Bayesian marginalization allows ab initio 3D classification, enabling automated analysis and discovery of unexpected structures without bias from a reference map. These algorithms are combined in a user-friendly computer program named cryoSPARC (http://www.cryosparc.com).