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首页> 外文期刊>Journal of Molecular Biology >A Structural-informatics Approach for Tracing beta-Sheets: Building Pseudo-C(alpha) Traces for beta-Strands in Intermediate-resolution Density Maps.
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A Structural-informatics Approach for Tracing beta-Sheets: Building Pseudo-C(alpha) Traces for beta-Strands in Intermediate-resolution Density Maps.

机译：跟踪beta-Sheets的结构信息学方法：在中等分辨率密度图中为beta-strands建立伪C（alpha）迹线。

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摘要

We report the development of two computational methods to assist density map interpretation at intermediate resolutions: sheettracer for building pseudo-C(alpha) models of beta-sheets, and a deconvolution method for enhancing features attributed to major secondary structural elements. Sheettracer is tightly coupled with sheetminer, which was developed to locate sheet densities in intermediate-resolution density maps. The results from sheetminer are used as inputs to sheettracer, which employs a multi-step ad hoc morphological analysis of sheet densities to trace individual strands of beta-sheets. The methods were tested on simulated density maps from 12 protein crystal structures that represent a reasonably complete sampling of sheet morphology. The sheet-tracing results were quantitatively assessed in terms of sensitivity, specificity and rms deviations. Furthermore, sheettracer and the deconvolution method were rigorously tested on experimental maps of the lambda2 protein of reovirus at resolutions of 7.6A and 11.8A. Our results clearly demonstrate the capability of sheettracer in building pseudo-C(alpha) models of beta-sheets in intermediate-resolution density maps and the power of the deconvolution method in enhancing the performance of sheettracer. These computational methods, along with other related ones, should facilitate recognition and analysis of folding motifs from experimental data at intermediate resolutions.
机译：我们报告了两种计算方法的发展，以协助中等分辨率下的密度图解释：用于建立beta-sheet的伪C（alpha）模型的sheettracer，以及用于增强归因于主要二级结构元素的特征的反卷积方法。 Sheettracer与sheetminer紧密结合，后者被开发用于在中等分辨率密度图中定位图纸密度。 sheetminer的结果用作sheettracer的输入，sheettracer使用多步骤的薄片密度临时形态分析来追踪β薄片的各个链。在代表12个蛋白质晶体结构的模拟密度图上测试了这些方法，这些结构代表了薄片形态的合理完整采样。根据敏感性，特异性和均方根偏差对薄片追踪结果进行了定量评估。此外，在呼肠孤病毒的lambda2蛋白实验图上以7.6A和11.8A的分辨率严格测试了sheettracer和解卷积方法。我们的结果清楚地证明了sheettracer在中等分辨率密度图中构建β-表的伪C（alpha）模型的能力以及反卷积方法在增强sheettracer性能方面的能力。这些计算方法以及其他相关方法应有助于以中等分辨率从实验数据中识别和分析折叠图案。

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