低聚物
蛋白质聚集
分子动力学
淀粉样蛋白(真菌学)
淀粉样β
淀粉样纤维
计算机科学
化学
马尔可夫链
纳米技术
生物物理学
计算生物学
生物
计算化学
材料科学
生物化学
机器学习
病理
有机化学
无机化学
医学
疾病
作者
Suman Samantray,Wibke Schumann,Alexander-Maurice Illig,Martín Carballo‐Pacheco,Arghadwip Paul,Bogdan Barz,Birgit Strodel
出处
期刊:Methods in molecular biology
日期:2022-01-01
卷期号:: 235-279
被引量:16
标识
DOI:10.1007/978-1-0716-1546-1_12
摘要
Protein disorder and aggregation play significant roles in the pathogenesis of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. The end products of the aggregation process in these diseases are highly structured amyloid fibrils. Though in most cases, small, soluble oligomers formed during amyloid aggregation are the toxic species. A full understanding of the physicochemical forces that drive protein aggregation is thus required if one aims for the rational design of drugs targeting the formation of amyloid oligomers. 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.
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