Department of Biochemistry and Molecular Biophysics and ‡Department of Physics, Kansas State University , Manhattan, Kansas 66506, United States.
J Phys Chem B. 2017 Feb 23;121(7):1576-1586. doi: 10.1021/acs.jpcb.7b00253. Epub 2017 Feb 13.
The formation of amyloid fibrils has been associated with many neurodegenerative disorders, yet the mechanism of aggregation remains elusive, partly because aggregation time scales are too long to probe with atomistic simulations. A microscopic theory of fibril elongation was recently developed that could recapitulate experimental results with respect to the effects of temperature, denaturants, and protein concentration on fibril growth kinetics (Schmit, J. D. J. Chem. Phys. 2013, 138 (18), 185102). The theory identifies the conformational search over H-bonding states as the slowest step in the aggregation process and suggests that this search can be efficiently modeled as a random walk on a rugged one-dimensional energy landscape. This insight motivated the multiscale computational algorithm for simulating fibril growth presented in this paper. Briefly, a large number of short atomistic simulations are performed to compute the system diffusion tensor in the reaction coordinate space predicted by the analytic theory. Ensemble aggregation pathways and growth kinetics are then computed from Markov state model (MSM) trajectories. The algorithm is deployed here to understand the fibril growth mechanism and kinetics of Aβ and three of its mutants. The order of growth rates of the wild-type and two single mutation peptides (CHA19 and CHA20) predicted by the MSM trajectories is consistent with experimental results. The simulation also correctly predicts that the double mutation (CHA19/CHA20) would reduce the fibril growth rate, even though the degree of rate reduction with respect to either single mutation is overestimated. This artifact may be attributed to the simplistic implicit solvent model. These trends in the growth rate are not apparent from inspection of the rate constants of individual bonds or the lifetimes of the mis-registered states that are the primary kinetic traps but only emerge in the ensemble of trajectories generated by the MSM.
淀粉样纤维的形成与许多神经退行性疾病有关，但聚集的机制仍不清楚，部分原因是聚集时间尺度太长，无法通过原子模拟来探测。最近开发了一种原纤维伸长的微观理论，该理论可以重现实验结果，即温度、变性剂和蛋白质浓度对原纤维生长动力学的影响（Schmit，J.D. J.Chem.Phys.2013,138（18），185102）。该理论将氢键状态的构象搜索确定为聚集过程中最慢的步骤，并表明可以将该搜索有效地建模为在粗糙的一维能量景观上的随机行走。这一见解促使本文提出了模拟原纤维生长的多尺度计算算法。简而言之，进行大量的短原子模拟，以计算由分析理论预测的反应坐标空间中的系统扩散张量。然后从马科夫态模型（MSM）轨迹计算集合聚合途径和生长动力学。该算法用于理解 Aβ及其三种突变体的原纤维生长机制和动力学。MSM 轨迹预测的野生型和两种单突变肽（CHA19 和 CHA20）的生长速率顺序与实验结果一致。模拟还正确预测了双突变（CHA19/CHA20）会降低原纤维生长速率，尽管相对于任一单突变的速率降低程度被高估。这种人为产物可能归因于简单的隐式溶剂模型。这些生长速率的趋势从单个键的速率常数或主要动力学陷阱的未注册状态的寿命的检查中并不明显，但仅出现在 MSM 生成的轨迹集合中出现。
