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: 8904684
• 负责人: Jeremy David Schmit
• 金额: $ 28.04万
• 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/8904684
• 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; Health; 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; 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; 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多年时间刻度的数量级加速骨料形成的方案，这些数量级相对于多年时间标准，这些时间表表征了像阿尔茨海默氏症这样的疾病的发作。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，以推断出针对生理条件的实验观察。当前的理论工具不适合此任务。目前，分析理论缺乏对序列扰动或环境条件进行预测所需的显微镜基础，而在硅方法中，即使在不牺牲必要的分辨率的情况下也无法达到体外聚集时间标准。申请人开发了一种微观的原纤维伸长理论，该理论与实验有关 温度，变性剂和蛋白质浓度。该理论将H键状态的构象搜索确定为聚集过程中最慢的一步（与最近的模拟一致的观察结果），表明该搜索可以有效地建模为在坚固的一维潜力中的随机行走。拟议的工作将通过两种方式扩展该模型：1）通过开发新的淀粉样蛋白聚集的新微观模型。这些模型将在聚集途径中解决关键步骤，包括成核和原纤维端对新分子结合的模板效应。他们还将探索不同的自我关联模式之间的动力学竞争，包括低聚物和原纤维。 2）使用初步模型的见解来开发多尺度计算算法，以模拟原子细节中的原纤维生长和成核。在这个 算法，将使用大量的小型模拟来计算由分析理论预测的反应坐标空间中的系统扩散张量。然后，将使用此扩散张量来计算聚合过程的马尔可夫状态轨迹。这项工作的创新是使用分析建模来推断微观动力学中的慢速步骤，这些动力学目前无法通过实验或模拟来分辨，并使用这些见解来开发具有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，例如两个区分阿尔茨海默氏症相关肽A的主要形式的氨基酸，以及“动力学相图”，这将允许对聚集途径进行合理操纵，并提供从体外实验中推导生理学后果的手段。