RosettaHoles：用于蛋白质结构预测、优化、设计及验证的蛋白质核心堆积快速评估方法
RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation.
作者信息
Sheffler Will, Baker David
机构信息
Department of Genome Sciences, University of Washington, Seattle, Washington 98195-5065, USA.
出版信息
Protein Sci. 2009 Jan;18(1):229-39. doi: 10.1002/pro.8.
Abstract
We present a novel method called RosettaHoles for visual and quantitative assessment of underpacking in the protein core. RosettaHoles generates a set of spherical cavity balls that fill the empty volume between atoms in the protein interior. For visualization, the cavity balls are aggregated into contiguous overlapping clusters and small cavities are discarded, leaving an uncluttered representation of the unfilled regions of space in a structure. For quantitative analysis, the cavity ball data are used to estimate the probability of observing a given cavity in a high-resolution crystal structure. RosettaHoles provides excellent discrimination between real and computationally generated structures, is predictive of incorrect regions in models, identifies problematic structures in the Protein Data Bank, and promises to be a useful validation tool for newly solved experimental structures.
摘要
我们提出了一种名为RosettaHoles的新方法，用于直观且定量地评估蛋白质核心中的包装不足情况。RosettaHoles生成一组球形腔球，这些腔球填充蛋白质内部原子之间的空体积。为了进行可视化，腔球被聚合成连续重叠的簇，小腔被舍弃，从而得到结构中空余未填充区域的清晰表示。对于定量分析，腔球数据用于估计在高分辨率晶体结构中观察到给定腔的概率。RosettaHoles能够很好地区分真实结构和计算生成的结构，可预测模型中的错误区域，识别蛋白质数据库中有问题的结构，并有望成为新解析的实验结构的有用验证工具。
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