Heidari Zahra, Roe Daniel R, Galindo-Murillo Rodrigo, Ghasemi Jahan B, Cheatham Thomas E
Faculty of Chemistry, K. N. Toosi University of Technology , Tehran 1969764499, Iran.
Department of Medicinal Chemistry, L. S. Skaggs Pharmacy Institute, University of Utah , Salt Lake City, Utah 84112, United States.
J Chem Inf Model. 2016 Jul 25;56(7):1282-91. doi: 10.1021/acs.jcim.5b00727. Epub 2016 Jun 22.
Long time scale molecular dynamics (MD) simulations of biological systems are becoming increasingly commonplace due to the availability of both large-scale computational resources and significant advances in the underlying simulation methodologies. Therefore, it is useful to investigate and develop data mining and analysis techniques to quickly and efficiently extract the biologically relevant information from the incredible amount of generated data. Wavelet analysis (WA) is a technique that can quickly reveal significant motions during an MD simulation. Here, the application of WA on well-converged long time scale (tens of μs) simulations of a DNA helix is described. We show how WA combined with a simple clustering method can be used to identify both the physical and temporal locations of events with significant motion in MD trajectories. We also show that WA can not only distinguish and quantify the locations and time scales of significant motions, but by changing the maximum time scale of WA a more complete characterization of these motions can be obtained. This allows motions of different time scales to be identified or ignored as desired.
由于大规模计算资源的可用性以及基础模拟方法的重大进展，生物系统的长时间尺度分子动力学（MD）模拟正变得越来越普遍。因此，研究和开发数据挖掘与分析技术，以便从大量生成的数据中快速有效地提取生物学相关信息是很有用的。小波分析（WA）是一种能够在MD模拟过程中快速揭示显著运动的技术。在此，描述了WA在DNA螺旋的良好收敛的长时间尺度（数十微秒）模拟中的应用。我们展示了WA如何与一种简单的聚类方法相结合，用于识别MD轨迹中具有显著运动的事件的物理位置和时间位置。我们还表明，WA不仅可以区分和量化显著运动的位置和时间尺度，而且通过改变WA的最大时间尺度，可以获得对这些运动更完整的表征。这使得可以根据需要识别或忽略不同时间尺度的运动。
