Protein disorder and aggregation play significant roles in the pathogenesis of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's disease. The end products of the aggregation process in these diseases are β-sheet rich amyloid fibrils. Though in most cases small, soluble oligomers formed during amyloid aggregation are the toxic species. A full understanding of the physicochemical forces behind the protein aggregation process is required if one aims to reveal the molecular basis of the various amyloid diseases. Among a multitude of biophysical and biochemical techniques that are employed for studying protein aggregation, molecular dynamics (MD) simulations at the atomic level provide the highest temporal and spatial resolution of this process, capturing key steps during the formation of amyloid oligomers. Here we provide a step-by-step guide for setting up, running, and analyzing MD simulations of aggregating peptides using GROMACS. For the analysis we provide the scripts that were developed in our lab, which allow to determine the oligomer size and inter-peptide contacts that drive the aggregation process. Moreover, we explain and provide the tools to derive Markov state models and transition networks from MD data of peptide aggregation.

中文翻译：

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蛋白质聚集的分子动力学模拟：使用马尔可夫状态模型和过渡网络进行模拟设置和分析的协议

蛋白质失调和聚集在许多神经退行性疾病（例如阿尔茨海默氏病和帕金森氏病）的发病机理中起重要作用。在这些疾病中，聚集过程的最终产物是富含β-折叠的淀粉样原纤维。尽管在大多数情况下，淀粉样蛋白聚集过程中形成的小的可溶低聚物是有毒物质。如果要揭示各种淀粉样疾病的分子基础，就需要对蛋白质聚集过程背后的物理化学力有充分的了解。在用于研究蛋白质聚集的多种生物物理和生化技术中，原子级的分子动力学（MD）模拟提供了该过程的最高时空分辨率，捕获了淀粉样低聚物形成过程中的关键步骤。在这里，我们提供了使用GROMACS设置，运行和分析聚集肽的MD模拟的分步指南。为了进行分析，我们提供了在实验室中开发的脚本，这些脚本可以确定驱动聚合过程的寡聚物大小和肽间接触。此外，我们解释并提供了从肽聚集的MD数据导出Markov状态模型和过渡网络的工具。