Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
J Chem Theory Comput. 2011 Mar 8;7(3):778-89. doi: 10.1021/ct100531j. Epub 2011 Feb 10.
We recently introduced a new method for discovering, characterizing, and monitoring spatiotemporal patterns in the conformational fluctuations in molecular dynamics simulation data ( J. Comput. Biol. 2010 , 17 ( 3 ), 309 - 324 ). Significantly, our method, called Dynamic Tensor Analysis (DTA), can be performed as the simulation is progressing. It is therefore well-suited to analyzing long timescale simulations, which are critical for studying biologically relevant motions but may be too large for traditional analysis methods. In this paper, we demonstrate that the patterns discovered by DTA often correspond to functionally important conformational substates. In particular, we apply DTA to a 150 ns simulation of ubiquitin and discover patterns that provide unique insights into ubiquitin's ability to bind multiple substrates. Moreover, we take advantage of DTA's ability to identify patterns on different timescales and investigate how fast positional fluctuations may modulate slower, large-scale motions in functionally important regions. Our findings here suggest that DTA is well-suited to organizing, visualizing, and analyzing very large trajectories and discovering conformational substates.
我们最近提出了一种新方法,用于发现、描述和监测分子动力学模拟数据中构象波动的时空模式(J. Comput. Biol. 2010, 17(3), 309-324)。值得注意的是,我们的方法称为动态张量分析(DTA),可以在模拟进行时执行。因此,它非常适合分析长时间尺度的模拟,这对于研究与生物学相关的运动至关重要,但对于传统分析方法来说可能太大了。在本文中,我们证明 DTA 发现的模式通常对应于功能上重要的构象亚态。具体来说,我们将 DTA 应用于 150ns 的泛素模拟,并发现了一些模式,这些模式为泛素能够结合多个底物的能力提供了独特的见解。此外,我们利用 DTA 识别不同时间尺度上的模式的能力,研究快速位置波动如何调节功能重要区域中的较慢的大尺度运动。我们在这里的发现表明,DTA 非常适合组织、可视化和分析非常大的轨迹并发现构象亚态。