Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
Genome Center, University of California, Davis, CA, USA.
Nat Methods. 2021 Feb;18(2):156-164. doi: 10.1038/s41592-020-01051-w. Epub 2021 Feb 4.
This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.
本文介绍了 2019 年冷冻电镜模型挑战赛的结果。该挑战赛的目标是:(1) 评估使用当前建模软件从低温电子显微镜(cryo-EM)图谱中生成的模型的质量;(2) 评估来自不同软件开发商和用户的建模结果的可重复性;(3) 比较当前用于模型评估的指标(特别是适用于图谱的指标)的性能,重点关注近原子分辨率。我们的研究结果表明,来自四个基准图谱的 13 个参赛团队所生成的 cryo-EM 模型具有较高的准确性和可重复性,其中包括三个分辨率系列(1.8 至 3.1 Å)。这些结果为在单个实验和结构数据档案(如蛋白质数据库)的背景下验证近原子 cryo-EM 结构提供了具体建议。我们建议采用多个评分参数,以提供对模型的全面、客观的注释和评估,反映出观察到的 cryo-EM 图谱密度。