Yan Xiaodong, Dryden Kelly A, Tang Jinghua, Baker Timothy S
Department of Chemistry & Biochemistry, University of California, San Diego, La Jolla, CA 92093-0378, USA.
J Struct Biol. 2007 Jan;157(1):211-25. doi: 10.1016/j.jsb.2006.07.013. Epub 2006 Aug 11.
Model-based, three-dimensional (3D) image reconstruction procedures require a starting model to initiate data analysis. We have designed an ab initio method, which we call the random model (RM) method, that automatically generates models to initiate structural analysis of icosahedral viruses imaged by cryo-electron microscopy. The robustness of the RM procedure was demonstrated on experimental sets of images for five representative viruses. The RM method also provides a straightforward way to generate unbiased starting models to derive independent 3D reconstructions and obtain a more reliable assessment of resolution. The fundamental scheme embodied in the RM method should be relatively easy to integrate into other icosahedral software packages.
基于模型的三维（3D）图像重建程序需要一个起始模型来启动数据分析。我们设计了一种从头开始的方法，我们称之为随机模型（RM）方法，该方法可自动生成模型，以启动对通过冷冻电子显微镜成像的二十面体病毒的结构分析。RM程序的稳健性在五种代表性病毒的实验图像集上得到了证明。RM方法还提供了一种直接的方式来生成无偏差的起始模型，以推导独立的3D重建并获得更可靠的分辨率评估。RM方法所体现的基本方案应该相对容易集成到其他二十面体软件包中。
