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首页> 外文期刊>The journal of physical chemistry, B. Condensed matter, materials, surfaces, interfaces & biophysical >Stochastic Kinetic Treatment of Protein Aggregation and the Effects of Macromolecular Crowding
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Stochastic Kinetic Treatment of Protein Aggregation and the Effects of Macromolecular Crowding

机译：蛋白质聚集的随机动力学治疗及大分子挤出的影响

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Investigation of protein self-assembly processes is important for understanding the growth processes of functional proteins as well as disease-causing amyloids. Inside cells, intrinsic molecular fluctuations are so high that they cast doubt on the validity of the deterministic rate-equation approach. Furthermore, the protein environments inside cells are often crowded with other macromolecules, with volume fractions of the crowders as high as 40%. We have developed a stochastic kinetic framework using Gillespie's algorithm for general systems undergoing particle self-assembly, including particularly protein aggregation at the cellular level. The effects of macromolecular crowding are investigated using models built on scaled-particle and transition-state theories. The stochastic kinetic method can be formulated to provide information on the dominating aggregation mechanisms in a method called reaction frequency (or propensity) analysis. This method reveals that the change of scaling laws related to the lag time can be directly related to the change in the frequencies of reaction mechanisms. Further examination of the time evolution of the fibril mass and length quantities unveils that maximal fluctuations occur in the periods of rapid fibril growth and the fluctuations of both quantities can be sensitive functions of rate constants. The presence of crowders often amplifies the roles of primary and secondary nucleation and causes shifting in the relative importance of elongation, shrinking, fragmentation, and coagulation of linear aggregates. We also show a dual effect of changing volume on the halftime of aggregation for ApoC2 which is reduced in the presence of crowders. A comparison of the results of stochastic simulations with those of rate equations gives us information on the convergence relation between them and how the roles of reaction mechanisms change as the system volume is varied.
机译：蛋白质自组装过程的研究对于理解功能蛋白质和致病淀粉样蛋白的生长过程非常重要。在细胞内部，固有的分子波动如此之高，以至于他们怀疑确定性速率方程方法的有效性。此外，细胞内的蛋白质环境通常挤满了其他大分子，其体积分数高达40%。我们使用Gillespie算法开发了一个随机动力学框架，用于经历粒子自组装的一般系统，尤其包括细胞水平的蛋白质聚集。利用基于标度粒子和过渡态理论建立的模型研究了大分子拥挤的影响。随机动力学方法可以通过反应频率（或倾向性）分析提供有关主要聚集机制的信息。该方法揭示了与滞后时间有关的标度律的变化可以直接与反应机理频率的变化有关。对纤维质量和长度量的时间演化的进一步研究揭示，最大波动发生在纤维快速生长的时期，两个量的波动可能是速率常数的敏感函数。拥挤物的存在通常会放大一次和二次成核的作用，并导致线性聚集体的伸长、收缩、碎裂和凝聚的相对重要性发生变化。我们还显示了体积变化对ApoC2聚集半时间的双重影响，这种影响在拥挤的情况下会减少。通过将随机模拟的结果与速率方程的结果进行比较，我们可以了解它们之间的收敛关系，以及反应机制的作用如何随着系统体积的变化而变化。

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