PDF(Pubmed)
Abstract：
Protein folding is a critical process that determines the functional state of proteins. Proper folding is essential for proteins to acquire their functional three-dimensional structures and execute their biological role, whereas misfolded proteins can lead to various diseases, including neurodegenerative disorders like Alzheimer\'s and Parkinson\'s. Therefore, a deeper understanding of protein folding is vital for understanding disease mechanisms and developing therapeutic strategies. This study introduces the Stochastic Landscape Classification (SLC), an innovative, automated, nonlearning algorithm that quantitatively analyzes protein folding dynamics. Focusing on collective variables (CVs) - low-dimensional representations of complex dynamical systems like molecular dynamics (MD) of macromolecules - the SLC approach segments the CVs into distinct macrostates, revealing the protein folding pathway explored by MD simulations. The segmentation is achieved by analyzing changes in CV trends and clustering these segments using a standard density-based spatial clustering of applications with noise (DBSCAN) scheme. Applied to the MD-based CV trajectories of Chignolin and Trp-Cage proteins, the SLC demonstrates apposite accuracy, validated by comparing standard classification metrics against ground-truth data. These metrics affirm the efficacy of the SLC in capturing intricate protein dynamics and offer a method to evaluate and select the most informative CVs. The practical application of this technique lies in its ability to provide a detailed, quantitative description of protein folding processes, with significant implications for understanding and manipulating protein behavior in industrial and pharmaceutical contexts.
摘要：
蛋白质折叠是决定蛋白质功能状态的关键过程。正确的折叠对于蛋白质获得其功能三维结构并执行其生物学作用至关重要，而错误折叠的蛋白质会导致各种疾病，包括神经退行性疾病，如阿尔茨海默氏症和帕金森氏症。因此,更深入地了解蛋白质折叠对于理解疾病机制和制定治疗策略至关重要.本研究介绍了随机景观分类(SLC)，一个创新的,自动化,定量分析蛋白质折叠动力学的非学习算法。专注于集体变量（CV）-复杂动力系统的低维表示，如大分子的分子动力学（MD）-SLC方法将CV分为不同的宏观状态，揭示了MD模拟探索的蛋白质折叠途径。分割是通过分析CV趋势的变化并使用标准的基于密度的噪声应用空间聚类（DBSCAN）方案对这些片段进行聚类来实现的。应用于Chignolin和Trp-Cage蛋白的基于MD的CV轨迹，SLC显示出适当的准确性，通过将标准分类指标与地面实况数据进行比较来验证。这些指标肯定了SLC在捕获复杂蛋白质动力学方面的功效，并提供了一种评估和选择信息量最大的CV的方法。这种技术的实际应用在于它能够提供详细的,蛋白质折叠过程的定量描述，对理解和操纵工业和制药环境中的蛋白质行为具有重要意义。
