Department of Computer Science, University of California, Irvine, CA, 92697, USA.
Department of Chemistry, San José State University, San Jose, CA, 95192, USA.
Sci Rep. 2020 Sep 24;10(1):15668. doi: 10.1038/s41598-020-72260-8.
Amyloid fibril formation is central to the etiology of a wide range of serious human diseases, such as Alzheimer's disease and prion diseases. Despite an ever growing collection of amyloid fibril structures found in the Protein Data Bank (PDB) and numerous clinical trials, therapeutic strategies remain elusive. One contributing factor to the lack of progress on this challenging problem is incomplete understanding of the mechanisms by which these locally ordered protein aggregates self-assemble in solution. Many current models of amyloid deposition diseases posit that the most toxic species are oligomers that form either along the pathway to forming fibrils or in competition with their formation, making it even more critical to understand the kinetics of fibrillization. A recently introduced topological model for aggregation based on network Hamiltonians is capable of recapitulating the entire process of amyloid fibril formation, beginning with thousands of free monomers and ending with kinetically accessible and thermodynamically stable amyloid fibril structures. The model can be parameterized to match the five topological classes encompassing all amyloid fibril structures so far discovered in the PDB. This paper introduces a set of network statistical and topological metrics for quantitative analysis and characterization of the fibrillization mechanisms predicted by the network Hamiltonian model. The results not only provide insight into different mechanisms leading to similar fibril structures, but also offer targets for future experimental exploration into the mechanisms by which fibrils form.
淀粉样纤维的形成是广泛存在的严重人类疾病（如阿尔茨海默病和朊病毒病）的发病机制的核心。尽管在蛋白质数据库（PDB）中发现了越来越多的淀粉样纤维结构，并且进行了大量的临床试验，但治疗策略仍然难以捉摸。造成这一具有挑战性的问题缺乏进展的一个因素是，人们对这些局部有序的蛋白质聚集体在溶液中自组装的机制了解不完整。许多目前的淀粉样沉积疾病模型假设，最具毒性的物种是寡聚物，这些寡聚物要么沿着形成纤维的途径形成，要么与纤维的形成竞争，因此，了解纤维形成的动力学就显得更加关键。最近提出的基于网络哈密顿量的聚集拓扑模型能够重现淀粉样纤维形成的整个过程，从数千个游离单体开始，最终形成动力学可及且热力学稳定的淀粉样纤维结构。该模型可以参数化以匹配迄今为止在 PDB 中发现的所有淀粉样纤维结构的五个拓扑类别。本文介绍了一组网络统计和拓扑指标，用于对网络哈密顿模型预测的纤维形成机制进行定量分析和特征描述。这些结果不仅提供了对导致类似纤维结构的不同机制的深入了解，还为未来对纤维形成机制的实验探索提供了目标。
