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Coiled-coil protein origami nanostructure modeling for improved characterization and prediction

机译:卷曲螺旋蛋白质折纸纳米结构建模改进的描述和预测

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摘要

Coiled-coil protein origami (CCPO) is a method that genetically links, folds, and assembles coiled-coil protein modules to form well-defined nanostructures. In combination with small-angle X-ray scattering (SAXS), comparative protein structure modeling has been useful to characterize CCPO nanostructures in atomistic resolution. A critical step in the comparative modeling of CCPO nanostructure is structure refinement through molecular dynamics (MD) optimization. However, the details about the MD optimization are not available, and a reliable method to assess the quality of CCPO models is required. Here, we determined the effective length of MD optimization with simulated annealing, which improved the modeling quality. The structure refinement through effective MD optimization resulted in new structural models that nearly matched SAXS data and further enabled computational prediction of the radius of gyration without SAXS data. We used the results to derive a new model evaluation method. Calculation of the error in the radius of gyration for model evaluation significantly improved the predictive power over existing standard methods. The modeling method demonstrated in this study is fast and does not require a high-performance computing resource. Thus, we envision that it will provide great potential for designing new CCPO nanostructures through computational screening and enhanced structure characterization.
机译:卷曲螺旋蛋白质折纸(CCPO)是一种方法基因的链接、折叠和组装定义良好的卷曲螺旋蛋白质模块形式纳米结构。蛋白质x射线散射(粉煤灰)、比较结构建模一直是有用的描述CCPO纳米结构在原子论的决议。建模的CCPO纳米结构是结构细化通过分子动力学(MD)优化。优化是不可用的,可靠的方法以评估CCPO模型的质量必需的。MD的长度与模拟优化退火,提高建模质量。通过有效的MD结构细化优化导致新结构模型,几乎匹配一枝数据和进一步启用计算预测的半径回转没有一枝数据。获得一个新的模型评价方法。在半径的计算错误回转显著模型评价改进了现有的预测能力标准的方法。在这项研究中速度快,不需要一个高性能计算资源。因此,我们设想,它将提供好了设计新的潜力CCPO纳米结构通过计算筛选和增强结构表征。

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