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

用显式空间中的网络哈密顿模拟探测淀粉样蛋白原纤维的自组装

• 批准号: 10715891
• 负责人: Gianmarc Grazioli
• 金额: $ 16.88万
• 依托单位: SAN JOSE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-16 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10715891
• 关键词: Alzheimer' s Disease; Amyloid Fibrils; Amyloidosis; Area; Binding; Biological; Biological Products; Biomimetic Materials; Computer Models; Computer Simulation; Coupled; Dependence; Development; Disease; Dyes; Environment; Etiology; Exhibits; Family; Fluorescence; Fluorescence Microscopy; Freedom; Future; Grain; Graph; Growth; Individual; Insulin; Kinetics; Length; Liquid substance; Measures; Methodology; Methods; Microfluidics; Microtubules; Mission; Modeling; Molecular Structure; Motion; National Institute of General Medical Sciences; Non-Insulin-Dependent Diabetes Mellitus; Nuclear Magnetic Resonance; Onset of illness; Pattern; Pharmaceutical Preparations; Phase; Polymers; Predisposition; Preventive treatment; Prion Diseases; Process; Proteins; Reaction; Reporting; Research; Site; Solvents; Space Models; Structure; Structure of beta Cell of islet; Study models; System; Techniques; Testing; Time; Travel; United States National Institutes of Health; Validation; Work; X-Ray Crystallography; amyloid fibril formation; amyloid structure; computational network modeling; computerized tools; design; drug development; experimental study; human disease; improved; innovation; insulin granule; meter; molecular assembly/self assembly; monomer; novel; novel strategies; particle; pressure; protein aggregation; protein data bank; research and development; self assembly; simulation; technological innovation; time use; Alzheimer' s Disease; Amyloid Fibrils; Amyloidosis; Area; Binding; Biological; Biological Products; Biomimetic Materials; Computer Models; Computer Simulation; Coupled; Dependence; Development; Disease; Dyes; Environment; Etiology; Exhibits; Family; Fluorescence; Fluorescence Microscopy; Freedom; Future; Grain; Graph; Growth; Individual; Insulin; Kinetics; Length; Liquid substance; Measures; Methodology; Methods; Microfluidics; Microtubules; Mission; Modeling; Molecular Structure; Motion; National Institute of General Medical Sciences; Non-Insulin-Dependent Diabetes Mellitus; Nuclear Magnetic Resonance; Onset of illness; Pattern; Pharmaceutical Preparations; Phase; Polymers; Predisposition; Preventive treatment; Prion Diseases; Process; Proteins; Reaction; Reporting; Research; Site; Solvents; Space Models; Structure; Structure of beta Cell of islet; Study models; System; Techniques; Testing; Time; Travel; United States National Institutes of Health; Validation; Work; X-Ray Crystallography; amyloid fibril formation; amyloid structure; computational network modeling; computerized tools; design; drug development; experimental study; human disease; improved; innovation; insulin granule; meter; molecular assembly/self assembly; monomer; novel; novel strategies; particle; pressure; protein aggregation; protein data bank; research and development; self assembly; simulation; technological innovation; time use

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.

项目摘要/摘要 淀粉样蛋白原纤维形成是许多人类疾病的疾病病因的核心，包括阿尔茨海默氏症 疾病，2型糖尿病和各种疾病。虽然成千上万的分子结构 淀粉样蛋白原纤维已经使用X射线晶体学和核磁共振等技术解决了 （NMR），淀粉样蛋白原纤维形成的机制在很大程度上未知。一级成核的机制， 原纤维形成在以前不包含任何淀粉样纤维的溶液环境中开始 淀粉样蛋白疾病发作的关键步骤特别神秘。染料结合荧光显微镜 实验已用于观察微流体腔中的赞助形成原纤维形成 来自单个主要的成核位点。这些实验揭示了两个关键机械细节：1）原纤维 通过溶液传播的形成是恒定速度远离初级速度的波动波 成核位点和2）在原纤维形成前的滞后时间与 数量倒数。我们假设将胰岛素限制在较小体积上是一种进化 使淀粉样蛋白原纤维形成的适应性依次慢慢地影响胰岛素的大小 胰腺β细胞中的颗粒。我们将开发新颖的自上而下的粗粒模型，该模型利用桥接 方法，通过两种形式的原纤维形成蛋白的代表（一种纯粹的拓扑 网络表示和一个明确空间中的颗粒状表示）交换信息作为时间 进化。这种方法将利用指数式随机图的高计算效率 模型（纯粹是拓扑），具有最小的显式空间模型提供的改进的空间现实主义 Lennard-Jones流体。这些模型将首先使用三倍验证策略适合它们 参数化以简单地复制三个已知的实验可观察物：原纤维的拓扑 结构（源自蛋白质数据库中报道的结构），原纤维生长动力学（与染料相比 结合荧光实验）以及原纤维形成的空间传播模式（与 关于微流体实验）。然后，对经过验证的模型的分析将用于提出 一级成核的潜在机制，正在积极探索其调节 开发淀粉样蛋白疾病的预防性治疗。拟议的工作将需要创新 网络汉密尔顿方法（首先是PI和其他方法），因为它将是第一个包括 明确的空间自由度。这种发展将有助于比较哈密顿网络 实验结果的模型并增强了模拟的预测能力，目前的两项工作 以及未来的分子自组装研究。