Department of Physics, Drexel University, Philadelphia, Pennsylvania 19104, United States.
National Center for Atmospheric Research, Boulder, Colorado 80305, United States.
J Phys Chem B. 2021 Jun 17;125(23):6068-6079. doi: 10.1021/acs.jpcb.1c00959. Epub 2021 Jun 3.
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.
研究蛋白质自组装过程对于理解功能蛋白的生长过程以及致病淀粉样蛋白非常重要。在细胞内，固有分子波动非常高，这使得确定性速率方程方法的有效性受到质疑。此外，细胞内的蛋白质环境通常充满了其他大分子，拥挤剂的体积分数高达 40%。我们已经开发了一种使用 Gillespie 算法的随机动力学框架，用于研究经历粒子自组装的一般系统，包括特别是细胞水平的蛋白质聚集。使用基于缩放粒子和过渡态理论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称为反应频率（或倾向）分析的方法提供关于主导聚集机制的信息。该方法表明，与滞后时间相关的标度律变化可以直接与反应机制频率的变化相关联。进一步研究原纤维质量和长度随时间的演变揭示了最大波动发生在原纤维快速生长的时期，并且这两个数量的波动可以是速率常数的敏感函数。拥挤剂的存在通常放大了初级和次级成核的作用，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。我们还展示了 ApoC2 聚集半衰期随体积变化的双重效应，在拥挤剂存在下，聚集半衰期会降低。将随机模拟的结果与速率方程的结果进行比较，使我们了解它们之间的收敛关系，以及随着系统体积的变化，反应机制的作用如何变化。
