Theoretical and computational modeling of amyloid aggregation

淀粉样蛋白聚集的理论和计算模型

• 批准号: 8761359
• 负责人: Jeremy David Schmit
• 金额: $ 28.07万
• 依托单位: KANSAS STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8761359
• 关键词: Address; Affect; Age; Agreement; Algorithms; Alzheimer' s Disease; Amino Acids; Amyloid; Binding; Characteristics; Computational Technique; Computational algorithm; Computer Simulation; Computer Systems; Computing Methodologies; Diffusion; Disease; Event; Growth; Huntington Disease; Hydrogen Bonding; In Vitro; Inherited; Intervention; Kinetics; Knowledge; Laboratories; Lead; Maps; Methods; Microscopic; Mission; Modeling; Molecular; Mutation; Onset of illness; Outcome; Pathway interactions; Phase; Physiological; Play; Population; Prion Diseases; Process; Proteins; Protocols documentation; Public Health; Reaction; Relative (related person); Research; Resolution; Role; Scrapie; Simulate; Sodium Chloride; Solutions; Statistical Models; Techniques; Temperature; Theoretical model; Time; Toxic effect; Walking; Work; aggregation pathway; amyloid formation; base; conformational conversion; effective intervention; fibrillogenesis; improved; in vivo; innovation; insight; peptide A; protein aggregate; protein aggregation; public health relevance; research study; simulation; theories; tool

DESCRIPTION (provided by applicant): Experimental studies of protein aggregation utilize protocols that accelerate aggregate formation by many orders of magnitude relative to the multi-decade timescales that characterize the onset of dis- eases like Alzheimer's. The gap between in vivo and in vitro aggregation timescales demands detailed theories of the aggregation process in order to extrapolate experimental observations toward physiological conditions. Current theoretical tools are not suitable for this task. Analytic theories presently lack the microscopic basis that is needed to make predictions about sequence perturbations or environmental conditions, and in silico methods struggle to reach even in vitro aggregation timescales without sacrificing necessary resolution. The applicants have developed a microscopic theory of fibril elongation that agrees with experiments with respect to the effects of temperature, denaturants, and protein concentration. This theory identifies the conformational search over H-bonding states as the slowest step in the aggregation process (an observation that is in agreement with recent simulations) and shows that this search can be efficiently modeled as a random walk in a rugged one-dimensional potential. The proposed work will expand on this model in two ways: 1) By developing new microscopic models of amyloid aggregation. These models will resolve key steps in the aggregation pathway including nucleation and the templating effect of the fibril end upon the binding of new molecules. They will also explore the kinetic competition between different modes of self-association, including oligomers and fibrils. 2) By using the insights from the preliminary model to develop a multi-scale computational algorithm to simulate fibril growth and nucleation in atomistic detail. In this algorithm, a large number of small simulations will be used to compute the system diffusion tensor in the reaction coordinate space predicted by the analytic theory. This diffusion tensor wil then be used to compute Markov state trajectories of the aggregation process. The innovation of this work is to use analytic modeling to deduce slow steps in the microscopic kinetics that are not presently resolvable by experiments or simulation, and to use these insights to develop simulation methods with greatly improved efficiency. The outcome will be the ability to resolve the effects of small sequence perturbations, such as the two amino acids differentiating the major forms of the Alzheimer's-related peptide A, as well as a "kinetic phase diagram" that will allow the rational manipulation of aggregation pathways and provide a means to infer physiological consequences from in vitro experiments.

描述（由申请人提供）：蛋白质聚集的实验研究利用了相对于表征阿尔茨海默病等疾病发作的数十年时间尺度将聚集体形成加速多个数量级的方案。体内和体外聚集时间尺度之间的差距需要详细的聚集过程理论，以便将实验观察结果推断到生理条件。当前的理论工具不适合这项任务。目前，分析理论缺乏预测序列扰动或环境条件所需的微观基础，并且在不牺牲必要分辨率的情况下，计算机方法甚至很难达到体外聚集时间尺度。申请人开发了一种原纤维伸长的微观理论，该理论与关于以下效果的实验一致： 温度、变性剂和蛋白质浓度。该理论将氢键态上的构象搜索确定为聚集过程中最慢的步骤（这一观察结果与最近的模拟一致），并表明这种搜索可以有效地建模为崎岖的一维势中的随机游走。拟议的工作将以两种方式扩展该模型：1）通过开发新的淀粉样蛋白聚集微观模型。这些模型将解决聚集途径中的关键步骤，包括成核和原纤维末端对新分子结合的模板效应。他们还将探索不同自缔合模式（包括低聚物和原纤维）之间的动力学竞争。 2）利用初步模型的见解开发多尺度计算算法来模拟原子细节中的原纤维生长和成核。在这个 算法中，将使用大量的小型模拟来计算解析理论预测的反应坐标空间中的系统扩散张量。然后，该扩散张量将用于计算聚合过程的马尔可夫状态轨迹。这项工作的创新之处在于使用分析模型来推导目前无法通过实验或模拟解决的微观动力学中的缓慢步骤，并利用这些见解来开发效率大大提高的模拟方法。结果将是解决小序列扰动的影响的能力，例如区分阿尔茨海默病相关肽 A 的主要形式的两个氨基酸，以及“动力学相图”，该图将允许合理操纵聚集途径并提供从体外实验推断生理后果的方法。