Department of Chemistry, University of Michigan, Ann Arbor, Michigan, USA.
Biophysics, University of Michigan, Ann Arbor, Michigan, USA.
Nat Methods. 2014 May;11(5):552-4. doi: 10.1038/nmeth.2921. Epub 2014 Apr 6.
We present a simple and general approach termed REsemble for quantifying population overlap and structural similarity between ensembles. This approach captures improvements in the quality of ensembles determined using increasing input experimental data--improvements that go undetected when conventional methods for comparing ensembles are used--and reveals unexpected similarities between RNA ensembles determined using NMR and molecular dynamics simulations.
我们提出了一种简单而通用的方法，称为 REsemble，用于量化群体之间的重叠和结构相似性。这种方法捕捉到了使用越来越多的输入实验数据确定的集合质量的提高——当使用传统的集合比较方法时，这些提高是无法检测到的——并揭示了使用 NMR 和分子动力学模拟确定的 RNA 集合之间出乎意料的相似性。
