Probing Amyloid Fibril Self-Assembly with Network Hamiltonian Simulations in Explicit Space

项目概要/摘要 淀粉样原纤维的形成是许多人类疾病（包括阿尔茨海默氏病）的病因学的核心 疾病，2型糖尿病，和各种朊病毒疾病。虽然数千个分子结构 淀粉样纤维已经被X射线晶体学和核磁共振等技术解析 (NMR)，淀粉样蛋白原纤维形成的机制在很大程度上是未知的。初级成核机制， 由此原纤维形成在先前不含任何淀粉样蛋白原纤维的溶剂环境中开始， 淀粉样疾病发病的关键一步，特别神秘。染料结合荧光显微术 已经使用实验来观察微流体腔室中的自发形成原纤维形成 从单独的初级成核位置。这些实验揭示了两个关键的机制细节：1）原纤维 地层通过溶液传播，作为远离原生地层的恒定速度的行波 成核位点，和2）在原纤形成之前的滞后时间和成核位点之间存在线性关系。 体积的倒数。我们假设胰岛素被限制在更小的体积是一种进化 使淀粉样纤维形成异常缓慢的适应，反过来影响胰岛素的大小 胰腺β细胞中的颗粒。我们将开发一种新颖的自顶向下的粗粒度模型， 方法，其中纤维形成蛋白质的集合的两个表示（一个纯拓扑 网络表示和显式空间中的一个粒度表示）交换信息作为时间 进化这种方法将利用指数族随机图的高计算效率 模型（纯拓扑），通过基于以下的最小显式空间模型提供改进的空间真实感： Lennard-Jones流体模型将首先使用三重验证策略进行拟合， 参数化以同时再现三个已知的实验观测值：原纤维的拓扑 结构（来源于蛋白质数据库中报道的结构）、原纤维生长动力学（与染料- 结合荧光实验）和原纤维形成的空间传播模式（与 上述微流体实验）。然后将使用对验证模型的分析来提出 初级成核的潜在机制，其调制正在积极探索， 淀粉样蛋白疾病的预防性治疗的发展。拟议的工作将需要创新， 网络哈密尔顿方法（首先由PI和其他人介绍），因为它将是第一个包括 明确的空间自由度。这一发展将有利于网络哈密顿量的比较 模型的实验结果和提高预测能力的模拟，为目前的工作 以及分子自组装的未来研究。

Project Summary/Abstract Amyloid fibril formation is central to the disease etiology of a number of human diseases, including Alzheimer’s disease, type 2 diabetes, and a variety of prion diseases. Although molecular structures for thousands of amyloid fibrils have been resolved using techniques like X-ray crystallography and nuclear magnetic resonance (NMR), the mechanism of amyloid fibril formation is largely unknown. The mechanism of primary nucleation, whereby fibril formation begins in a solvent environment that previously did not contain any amyloid fibrils, a crucial step in amyloid disease onset, is particularly mysterious. Dye-binding fluorescence microscopy experiments have been used to observe the spontaneous formation fibril formation in microfluidic chambers from individual primary nucleation sites. These experiments revealed two key mechanistic details: 1) fibril formation propagated through solution as a traveling wave of constant velocity moving away from the primary nucleation site, and 2) there exists a linear relationship between the lag time before fibril formation and the inverse of volume. We hypothesize that the confinement of insulin to smaller volumes is an evolutionary adaptation that renders amyloid fibril formation prohibitively slow, in turn, influencing the size of insulin granules in pancreatic beta cells. We will develop novel top-down coarse-grained model that utilize a bridged approach, whereby two representations of an ensemble of fibril-forming proteins (one purely topological network representation and one granular representation in explicit space) exchange information as time evolves. This approach will leverage the high computational efficiency of exponential-family random graph models (purely topological), with improved spatial realism provided by a minimal explicit space model based on a Lennard-Jones fluid. The models will first be fit using a threefold validation strategy whereby they will be parameterized to simultaneously reproduce three known experimental observables: the fibril’s topological structure (derived from structures reported in the protein data bank), fibril growth kinetics (compared to dye- binding fluorescence experiments), and the spatial propagation patterns of fibril formation (compared to aforementioned microfluidic experiments). Analysis of the validated models will then be used to propose potential mechanisms for primary nucleation, the modulation of which is actively being explored for the development of preventative treatments for amyloid diseases. The proposed work will require an innovation to the network Hamiltonian methodology (first introduced by the PI and others), in that it will be the first to include explicit spatial degrees of freedom. This development will facilitate the comparison of network Hamiltonian models to experimental results and enhance the predictive power of the simulations, for both the present work and future studies in molecular self-assembly.