Benson Noah C, Daggett Valerie
Division of Biomedical and Health Informatics, University of Washington, Seattle, WA 98195.
Department of Bioengineering, Box 355013, University of Washington, Seattle, WA 98195-5013.
Int J Wavelets Multiresolut Inf Process. 2012 Jul;10(4). doi: 10.1142/S0219691312500403.
As high-throughput molecular dynamics simulations of proteins become more common and the databases housing the results become larger and more prevalent, more sophisticated methods to quickly and accurately mine large numbers of trajectories for relevant information will have to be developed. One such method, which is only recently gaining popularity in molecular biology, is the continuous wavelet transform, which is especially well-suited for time course data such as molecular dynamics simulations. We describe techniques for the calculation and analysis of wavelet transforms of molecular dynamics trajectories in detail and present examples of how these techniques can be useful in data mining. We demonstrate that wavelets are sensitive to structural rearrangements in proteins and that they can be used to quickly detect physically relevant events. Finally, as an example of the use of this approach, we show how wavelet data mining has led to a novel hypothesis related to the mechanism of the protein resolvase.
随着蛋白质高通量分子动力学模拟变得越来越普遍，存储结果的数据库也变得越来越大且越来越普遍，因此必须开发更复杂的方法，以便快速、准确地从大量轨迹中挖掘相关信息。连续小波变换就是这样一种方法，它最近才在分子生物学中受到欢迎，特别适用于分子动力学模拟等时程数据。我们详细描述了分子动力学轨迹小波变换的计算和分析技术，并给出了这些技术在数据挖掘中如何有用的示例。我们证明小波对蛋白质中的结构重排敏感，并且可用于快速检测物理相关事件。最后，作为这种方法应用的一个例子，我们展示了小波数据挖掘如何引出了一个与蛋白质解离酶机制相关的新假设。
