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

项目摘要/摘要 淀粉样原纤维的形成是包括阿尔茨海默氏症在内的许多人类疾病病因学的核心 疾病，2型糖尿病，以及各种普恩病毒疾病。尽管数以千计的分子结构 利用X射线结晶学和核磁共振等技术已经解决了淀粉样纤维 (核磁共振)，淀粉样原纤维形成的机制在很大程度上是未知的。一次成核的机制， 从而在以前不包含任何淀粉样纤维的溶剂环境中开始形成纤维， 淀粉样蛋白疾病发病的关键一步，尤其神秘。染料结合荧光显微镜 用实验观察了微流体室中自发形成纤维的过程 来自单独的初级成核点。这些实验揭示了两个关键的机制细节：1)原纤维 地层在溶液中传播，作为远离主波的恒速行波 2)纤维形成前的滞后时间与成核时间之间存在线性关系。 音量的倒数。我们假设，将胰岛素限制在较小体积内是一种进化 使淀粉样蛋白纤维形成极其缓慢的适应性，进而影响胰岛素的大小 胰岛β细胞中的颗粒。我们将开发新的自上而下的粗粒度模型，该模型利用桥接 一种方法，其中纤维形成蛋白系的两种表示(一种纯粹的拓扑结构 网络表示和显式空间中的一个粒度表示)随着时间交换信息 不断进化。该方法将利用指数族随机图的高计算效率 模型(纯拓扑)，改进的空间真实感由最小显式空间模型提供 一种伦纳德-琼斯流体。模型将首先使用三重验证策略进行拟合，据此它们将 参数化以同时再现三个已知的实验观测：纤维的拓扑 结构(来源于蛋白质数据库中报告的结构)，原纤维生长动力学(与染料相比)。 结合荧光实验)，以及纤维形成的空间传播模式(与 上述微流控实验)。然后将使用对经过验证的模型的分析来提出 一次成核的潜在机制，其调制正在积极地探索 淀粉样变性疾病预防性治疗的进展。拟议的工作将需要创新，以 网络哈密顿方法论(由PI和其他人首先引入)，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 将模型与实验结果进行比较，并增强了模拟的预测能力 以及未来分子自组装的研究。

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.