期刊
STRUCTURE
卷 15, 期 12, 页码 1630-1641出版社
CELL PRESS
DOI: 10.1016/j.str.2007.09.021
关键词
-
资金
- NEI NIH HHS [PN2 EY016525, PN2 EY016525-04] Funding Source: Medline
- NIGMS NIH HHS [U54 GM072970-04, R37 GM041455, R01 GM041455, R01 GM063817, GM-41455, R37 GM041455-19, U54 GM072970] Funding Source: Medline
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据