具有光学随机特征的分子动力学在线变点检测
Online Change Point Detection in Molecular Dynamics With Optical Random Features
摘要：
蛋白质是由不断波动的原子组成的，但偶尔会发生大规模的变化。这种转变具有生物学意义，将蛋白质的结构与其在细胞中的功能联系起来。原子级模拟，如分子动力学（MD），被用来研究这些事件。然而，分子动力学模拟产生具有多个可观测量的时间序列，而变化通常只影响其中的一小部分。因此，检测构象变化已被证明是具有挑战性的大多数变化点检测算法。在这项工作中，我们专注于识别这样的事件给出了许多嘈杂的观测。特别是，我们表明，无先验知识指数加权移动平均（NEWMA）算法可以用于沿光学硬件，以成功地识别这些变化的实时性。我们的方法不需要区分蛋白质的背景和蛋白质本身。对于更大的模拟，它比使用传统的硅硬件更快，并且具有更低的内存占用。该技术可以增强分子构象空间的采样。它也可以用于检测其他具有大量特征的连续数据中的变点。
英文摘要：
Proteins are made of atoms constantly fluctuating, but can occasionally undergo large-scale changes. Such transitions are of biological interest, linking the structure of a protein to its function with a cell. Atomic-level simulations, such as Molecular Dynamics (MD), are used to study these events. However, molecular dynamics simulations produce time series with multiple observables, while changes often only affect a few of them. Therefore, detecting conformational changes has proven to be challenging for most change-point detection algorithms. In this work, we focus on the identification of such events given many noisy observables. In particular, we show that the No-prior-Knowledge Exponential Weighted Moving Average (NEWMA) algorithm can be used along optical hardware to successfully identify these changes in real-time. Our method does not need to distinguish between the background of a protein and the protein itself. For larger simulations, it is faster than using traditional silicon hardware and has a lower memory footprint. This technique may enhance the sampling of the conformational space of molecules. It may also be used to detect change-points in other sequential data with a large number of features.
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