Combining efficient conformational sampling with a deformable elastic network model facilitates structure refinement at low resolution
Combining efficient conformational sampling with a deformable elastic network model facilitates structure refinement at low resolution
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DOI：
10.1016/j.str.2007.09.021
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发表时间：
2007-12-01
期刊：
影响因子：
5.7
通讯作者：
Levitt, Michael
中科院分区：
文献类型：
--
作者：
Schroeder, Gunnar F.;Brunger, Axel T.;Levitt, Michael
大分子蛋白质和蛋白质组装体的结构研究是分子生物学中一个困难而紧迫的挑战。实验通常只产生低分辨率或稀疏的数据，不足以完全确定原子结构。我们已经开发了一个通用的基于几何的算法，有效地样品的构象空间的限制下，从电子显微镜或X射线晶体学实验获得的低分辨率密度图。一个可变形的弹性网络（DEN）被用来限制采样到一个近似结构的先验知识。DEN约束显著减少了过拟合，特别是在低分辨率下。交叉验证用于优化结构信息和实验数据的权重。我们的算法是强大的，即使是噪声添加的密度图，并有一个大的收敛半径为我们的测试用例。DEN约束也可用于增强倒易空间模拟退火细化。
Structural studies of large proteins and protein assemblies are a difficult and pressing challenge in molecular biology. Experiments often yield only low-resolution or sparse data that are not sufficient to fully determine atomistic structures. We have developed a general geometry-based algorithm that efficiently samples conformational space under constraints imposed by low-resolution density maps obtained from electron microscopy or X-ray crystallography experiments. A deformable elastic network (DEN) is used to restrain the sampling to prior knowledge of an approximate structure. The DEN restraints dramatically reduce over-fitting, especially at low resolution. Crossvalidation is used to optimally weight the structural information and experimental data. Our algorithm is robust even for noise-added density maps and has a large radius of convergence for our test case. The DEN restraints can also be used to enhance reciprocal space simulated annealing refinement.