Laboratory for Structural Biology Research, National Institute of Arthritis, Musculoskeletal and Skin Diseases, NIH, Bethesda, MD 20892, United States.
J Struct Biol. 2018 Oct;204(1):90-95. doi: 10.1016/j.jsb.2018.07.003. Epub 2018 Jul 5.
The Bsoft package is aimed at processing electron micrographs for the determination of the three-dimensional structures of biological specimens. Recent advances in hardware allow us to solve structures to near atomic resolution using single particle analysis (SPA). The Map Challenge offered me an opportunity to test the ability of Bsoft to produce reconstructions from cryo-electron micrographs at the best resolution. I also wanted to understand what needed to be done to work towards full automation with validation. Here, I present two cases for the Map Challenge using Bsoft: ß-galactosidase and GroEL. I processed two independent subsets in each case with resolution-limited alignment. In both cases the reconstructions approached the expected resolution within a few iterations of alignment. I further validated the results by coherency-testing: i.e., that the reconstructions from real particles give better resolutions than reconstructions from the same number of aligned noise images. The key operations requiring attention for full automation are: particle picking, faster accurate alignment, proper mask generation, appropriate map sharpening, and understanding the amount of data needed to reach a desired resolution.
Bsoft 软件包旨在处理电子显微镜图像，以确定生物样本的三维结构。硬件的最新进展使我们能够使用单颗粒分析 (SPA) 来解决接近原子分辨率的结构问题。Map Challenge 为我提供了一个机会，以测试 Bsoft 从冷冻电子显微镜图像中以最佳分辨率生成重建的能力。我还想了解需要做些什么才能朝着具有验证的完全自动化方向发展。在这里，我使用 Bsoft 为 Map Challenge 展示了两个案例：β-半乳糖苷酶和 GroEL。我在每个案例中都使用分辨率受限的对齐处理了两个独立的子集。在这两种情况下，通过几次对齐迭代，重建都接近预期的分辨率。我通过相干性测试进一步验证了结果：即，来自真实粒子的重建给出的分辨率优于来自相同数量对齐噪声图像的重建。实现完全自动化需要注意的关键操作是：粒子选择、更快速准确的对齐、适当的掩模生成、适当的图谱锐化以及了解达到所需分辨率所需的数据量。
