Collective variables (CVs) describing slow degrees of freedom (DOFs) in biomolecular assemblies are crucial for analyzing molecular dynamics trajectories, creating Markov models and performing CV-based enhanced sampling simulations. While time-lagged independent component analysis (tICA) and its nonlinear successor, time-lagged autoencoder (tAE), are widely used, they often struggle to capture protein dynamics due to interference from random fluctuations along fast DOFs. To address this issue, we propose a novel approach integrating discrete wavelet transform (DWT) with dimensionality reduction techniques. DWT effectively separates fast and slow motion in protein simulation trajectories by decoupling high- and low-frequency signals. Based on the trajectory after filtering out high-frequency signals, which corresponds to fast motion, tICA and tAE can accurately extract CVs representing slow DOFs, providing reliable insights into protein dynamics. Our method demonstrates superior performance in identifying CVs that distinguish metastable states compared to standard tICA and tAE, as validated through analyses of conformational changes of alanine dipeptide and tripeptide and folding of CLN025. Moreover, we show that DWT can be used to improve the performance of a variety of CV-finding algorithms by combining it with Deep-tICA, a cutting-edge CV-finding algorithm, to extract CVs for enhanced-sampling calculations. Given its negligible computational cost and remarkable ability to screen fast motion, we propose DWT as a "free lunch" for CV extraction, applicable to a wide range of CV-finding algorithms.

中文翻译：

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筛选生化过程集体变量发现中的快速模式运动。


描述生物分子组装体中慢自由度 （DOF） 的集合变量 （CV） 对于分析分子动力学轨迹、创建马尔可夫模型和执行基于 CV 的增强采样模拟至关重要。虽然延时独立成分分析 （tICA） 及其非线性后继者延时自动编码器 （tAE） 被广泛使用，但由于快速自由度的随机波动的干扰，它们通常难以捕获蛋白质动力学。为了解决这个问题，我们提出了一种将离散小波变换 （DWT） 与降维技术相结合的新方法。DWT 通过解耦高频和低频信号，有效地分离蛋白质模拟轨迹中的快动作和慢动作。根据滤除对应于快速运动的高频信号后的轨迹，tICA 和 tAE 可以准确提取代表慢自由度的 CV，从而提供对蛋白质动力学的可靠见解。与标准 tICA 和 tAE 相比，我们的方法在识别区分亚稳态的 CV 方面表现出卓越的性能，通过分析丙氨酸二肽和三肽的构象变化以及 CLN025 的折叠来验证这一点。此外，我们表明 DWT 可以通过将其与尖端的 CV 查找算法 Deep-tICA 相结合来提高各种 CV 查找算法的性能，以提取 CV 以进行增强采样计算。鉴于其可忽略不计的计算成本和卓越的筛选快速运动能力，我们建议将 DWT 作为 CV 提取的“免费午餐”，适用于各种 CV 查找算法。