Theoretical and computational modeling of amyloid aggregation

描述（由申请人提供）：蛋白质聚集的实验研究利用的方案，在几十年的时间尺度上加速聚集形成，这些时间尺度是阿尔茨海默病等疾病发病的特征。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，以便对生理条件的实验观察进行外推。目前的理论工具并不适合这项任务。分析理论目前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，计算机方法甚至难以达到体外聚集时间尺度。申请者已经开发了一种微观理论的纤维伸长，同意与实验有关的影响

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.