Shedding Light on the Dock-Lock Mechanism in Amyloid Fibril Growth Using Markov State Models.
Shedding Light on the Dock-Lock Mechanism in Amyloid Fibril Growth Using Markov State Models.
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DOI：
10.1021/acs.jpclett.5b00330
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
M. Schor;A. Mey;F. Noé;C. MacPhee
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文献类型：
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作者：
M. Schor;A. Mey;F. Noé;C. MacPhee
我们调查的分子机制的单体除了一个不断增长的淀粉样蛋白纤维的甲状腺素运载蛋白TTR 105 -115肽的pH值的影响。使用马尔可夫状态模型提取平衡和动力学信息，从广泛的所有原子模拟使我们能够表征生产的途径，在单体除了以及几个关闭的路径被困状态。我们发现，多个途径导致成功添加。所有生产途径都是由肽中的中心疏水残基驱动的。此外，我们表明，在系统中最慢的过渡涉及陷阱配置，即，长寿命的亚稳态。这些陷阱控制着原纤维的生长速率。改变pH值基本上会重新加重系统，导致生产路径和陷阱的相对重要性存在明显差异，但仍保留了核心机制。
We investigate how the molecular mechanism of monomer addition to a growing amyloid fibril of the transthyretin TTR105-115 peptide is affected by pH. Using Markov state models to extract equilibrium and dynamical information from extensive all atom simulations allowed us to characterize both productive pathways in monomer addition as well as several off-pathway trapped states. We found that multiple pathways result in successful addition. All productive pathways are driven by the central hydrophobic residues in the peptide. Furthermore, we show that the slowest transitions in the system involve trapped configurations, that is, long-lived metastable states. These traps dominate the rate of fibril growth. Changing the pH essentially reweights the system, leading to clear differences in the relative importance of both productive paths and traps, yet retains the core mechanism.