Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158-2552, USA.
Proc Natl Acad Sci U S A. 2012 Nov 13;109(46):18821-6. doi: 10.1073/pnas.1216549109. Epub 2012 Oct 29.
To obtain a structural model of a macromolecular assembly by single-particle EM, a large number of particle images need to be collected, aligned, clustered, averaged, and finally assembled via reconstruction into a 3D density map. This process is limited by the number and quality of the particle images, the accuracy of the initial model, and the compositional and conformational heterogeneity. Here, we describe a structure determination method that avoids the reconstruction procedure. The atomic structures of the individual complex components are assembled by optimizing a match against 2D EM class-average images, an excluded volume criterion, geometric complementarity, and optional restraints from proteomics and chemical cross-linking experiments. The optimization relies on a simulated annealing Monte Carlo search and a divide-and-conquer message-passing algorithm. Using simulated and experimentally determined EM class averages for 12 and 4 protein assemblies, respectively, we show that a few class averages can indeed result in accurate models for complexes of as many as five subunits. Thus, integrative structural biology can now benefit from the relative ease with which the EM class averages are determined.
通过单颗粒电镜获得大分子组装体的结构模型,需要收集大量的粒子图像,进行对齐、聚类、平均处理,最后通过重构将其组装成 3D 密度图。这个过程受到粒子图像的数量和质量、初始模型的准确性以及组成和构象异质性的限制。在这里,我们描述了一种避免重构过程的结构测定方法。通过优化与 2D EM 类平均图像、排除体积标准、几何互补性以及来自蛋白质组学和化学交联实验的可选约束的匹配,组装各个复合物组件的原子结构。优化依赖于模拟退火蒙特卡罗搜索和分而治之的消息传递算法。使用分别为 12 和 4 个蛋白质组装体模拟和实验确定的 EM 类平均,我们表明,几个类平均确实可以为多达五个亚基的复合物产生准确的模型。因此,现在整合结构生物学可以从相对容易确定的 EM 类平均值中受益。