Chen Lin, He Jing
Department of Mathematics & Computer Science, Elizabeth City State University, Elizabeth City, NC 27909.
Department of Computer Science, Old Dominion University, Norfolk, VA 23529.
ACM BCB. 2018 Aug;2018:651-655. doi: 10.1145/3233547.3233709.
Cryo-electron microscopy (cryo-EM) has become a major technique for protein structure determination. Many atomic structures have been derived from cryo-EM density maps of about 3Å resolution. Side-chain conformations are well determined in density maps with super-resolutions such as 1-2Å. It is desirable to have a statistical method to detect anomalous side-chains without a super-resolution density map. In this study, we analyzed structures derived from X-ray density maps with higher than 1.5Å resolution and those from cryo-EM density maps with 2-4 Å and 4-6 Å resolutions respectively. We introduce a histogram-based outlier score (HBOS) for anomaly detection in protein models built from cryo-EM density maps. This method uses the statistics derived from X-ray dataset (<1.5Å) as the reference and combines five features involving the distal block distance, side-chain length, phi, psi, and first chi angle of the residue. Higher percentages of anomalies were observed in the cryo-EM models than in the super-resolution X-ray models. Lower percentages of anomalies were observed in cryo-EM models derived after January 2017 than those derived before 2017.
冷冻电子显微镜（cryo-EM）已成为蛋白质结构测定的一项主要技术。许多原子结构是从分辨率约为3埃的冷冻电子显微镜密度图中推导出来的。在分辨率为1-2埃等超分辨率的密度图中，侧链构象能够得到很好的确定。需要有一种统计方法来在没有超分辨率密度图的情况下检测异常侧链。在本研究中，我们分别分析了来自分辨率高于1.5埃的X射线密度图以及分辨率为2-4埃和4-6埃的冷冻电子显微镜密度图的结构。我们引入了一种基于直方图的异常分数（HBOS），用于在由冷冻电子显微镜密度图构建的蛋白质模型中进行异常检测。该方法使用从X射线数据集（<1.5埃）得出的统计数据作为参考，并结合了涉及残基的远端块距离、侧链长度、φ角、ψ角和第一个χ角的五个特征。在冷冻电子显微镜模型中观察到的异常百分比高于超分辨率X射线模型。2017年1月之后推导的冷冻电子显微镜模型中观察到的异常百分比低于2017年之前推导的模型。
