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利用 Pathwalking 在近原子分辨率冷冻电镜密度图中进行从头建模的自动化和评估。

Automation and assessment of de novo modeling with Pathwalking in near atomic resolution cryoEM density maps.

机构信息

Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, United States.

出版信息

J Struct Biol. 2018 Dec;204(3):555-563. doi: 10.1016/j.jsb.2018.09.005. Epub 2018 Sep 17.

Abstract

With the rapidly growing number of macromolecular structures solved to near-atomic resolution using electron cryomicroscopy (cryoEM), map interpretation and model building directly from the density without the use of structural templates has become increasingly important. As part of the 2015/2016 Map and Model Challenge, we attempted to assess our latest de novo modeling tool, Pathwalking, in terms of performance and usability, as well as identify areas for future improvements. In total, we applied Pathwalking to six density maps between 3 and 4.5 Å resolution selected from the challenge data sets. In five of the six cases, Pathwalking was able to accurately determine the protein fold and in three of these cases, the final all atom model had less than 1.6 Å RMSD when compared to the known structure. Model building and refinement was nearly completely automated, used default parameters and took less than 30 min to complete a refined all atom model. A direct outgrowth of this work was a more streamlined automated command line Pathwalking utility, as well as a novel sequence assignment and optimization routine, which attempts to register sidechain density with expected side chain volume. In total, Pathwalking offers a nearly complete, robust and efficient method for constructing atomistic protein structures directly from a density map without the aid of a template.

摘要

随着使用电子冷冻显微镜(cryoEM)以近原子分辨率解析的大分子结构数量的快速增长,直接从密度图而非结构模板进行图谱解释和模型构建变得越来越重要。作为 2015/2016 年图谱和模型挑战赛的一部分,我们尝试评估我们最新的从头建模工具 Pathwalking 的性能和可用性,以及确定未来改进的领域。总共,我们将 Pathwalking 应用于从挑战赛数据集中选择的六个分辨率在 3 到 4.5Å之间的密度图。在六个案例中的五个案例中,Pathwalking 能够准确确定蛋白质折叠,在这三个案例中,与已知结构相比,最终的全原子模型的 RMSD 小于 1.6Å。模型构建和细化几乎完全自动化,使用默认参数,不到 30 分钟即可完成细化的全原子模型。这项工作的直接成果是一个更加精简的自动化命令行 Pathwalking 实用程序,以及一个新的序列分配和优化例程,该例程尝试将侧链密度与预期的侧链体积进行匹配。总的来说,Pathwalking 提供了一种几乎完整、强大且高效的方法,可直接从密度图构建原子蛋白结构,而无需模板的帮助。

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