Perez Alberto, Roy Arijit, Kasavajhala Koushik, Wagaman Amy, Dill Ken A, MacCallum Justin L
Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York.
Proteins. 2014 Oct;82(10):2671-80. doi: 10.1002/prot.24633. Epub 2014 Jul 5.
A large number of methods generate conformational ensembles of biomolecules. Often one structure is selected to be representative of the whole ensemble, usually by clustering and selecting the structure closest to the center of the most populated cluster. We find that this structure is not necessarily the best representation of the cluster and present here two computationally inexpensive averaging protocols that can systematically provide better representations of the system, which can be more directly compared with structures from X-ray crystallography. In practice, systematic errors in the generated conformational ensembles appear to limit the maximum improvement of averaging methods.
大量方法可生成生物分子的构象集合。通常会选择一个结构来代表整个集合,通常是通过聚类并选择最接近最密集聚类中心的结构。我们发现这个结构不一定是聚类的最佳代表,并在此提出两种计算成本较低的平均协议,它们可以系统地提供对系统更好的表示,从而可以更直接地与X射线晶体学得到的结构进行比较。在实践中,生成的构象集合中的系统误差似乎限制了平均方法的最大改进。