Molecular mechanisms of protein aggregation from global fitting of kinetic models
Molecular mechanisms of protein aggregation from global fitting of kinetic models
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DOI：
10.1038/nprot.2016.010
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发表时间：
2016-02-01
期刊：
影响因子：
14.8
通讯作者：
Knowles, Tuomas P. J.
中科院分区：
文献类型：
--
作者：
Meisl, Georg;Kirkegaard, Julius B.;Knowles, Tuomas P. J.
阐明可溶性蛋白质转化为其淀粉样蛋白形式的分子机制是理解和控制与蛋白质聚集相关的疾病（如阿尔茨海默病和帕金森病）的基本先决条件。然而，由于与聚集反应网络相关的复杂性，分析蛋白质聚集的动力学数据以获得潜在的机制是一项复杂的任务。在这里，我们描述了一个框架，使用定量动力学分析和全球拟合，以确定和验证与实验动力学数据兼容的聚集反应的分子机制。我们在一个基于网络的软件，Amylofit实现这种方法。我们的程序从动力学实验的结果开始，该实验测量聚集体质量的浓度作为时间的函数。我们说明的方法，从使用硫磺素T测量的β-淀粉样蛋白（A β）肽的聚集的结果，但该方法是适合于从任何类似的动力学实验测量聚集体质量的积累作为时间的函数的数据;输入数据是在一个标签分隔的文本文件的形式。我们还概述了一般的实验策略和实际考虑，以获得足够质量的动力学数据，得出详细的机械结论，并开始与广泛的数据质量控制的指示程序。对于分析的核心部分，我们提供了一个在线平台（http：www.amylofit.ch.cam.ac.uk），该平台可以对动力学数据进行强大的全局分析，而无需广泛的编程或详细的数学知识。该软件自动执行重复性任务，并指导用户完成动力学分析的关键步骤：根据聚集反应的浓度依赖性确定对聚集机制的约束，从描述线性聚集体组装的几个基本模型中进行选择，并使用高级最小化算法拟合所选模型，以产生反应级数和速率常数。最后，我们概述了如何使用这种方法来研究淀粉样蛋白形成的潜在抑制剂结合的目标，以及它们在反应机制中的作用。该协议从数据处理到确定机制都可以在
The elucidation of the molecular mechanisms by which soluble proteins convert into their amyloid forms is a fundamental prerequisite for understanding and controlling disorders that are linked to protein aggregation, such as Alzheimer's and Parkinson's diseases. However, because of the complexity associated with aggregation reaction networks, the analysis of kinetic data of protein aggregation to obtain the underlying mechanisms represents a complex task. Here we describe a framework, using quantitative kinetic assays and global fitting, to determine and to verify a molecular mechanism for aggregation reactions that is compatible with experimental kinetic data. We implement this approach in a web-based software, AmyloFit. Our procedure starts from the results of kinetic experiments that measure the concentration of aggregate mass as a function of time. We illustrate the approach with results from the aggregation of the beta-amyloid (A beta) peptides measured using thioflavin T, but the method is suitable for data from any similar kinetic experiment measuring the accumulation of aggregate mass as a function of time; the input data are in the form of a tab-separated text file. We also outline general experimental strategies and practical considerations for obtaining kinetic data of sufficient quality to draw detailed mechanistic conclusions, and the procedure starts with instructions for extensive data quality control. For the core part of the analysis, we provide an online platform (http://www.amylofit.ch.cam.ac.uk) that enables robust global analysis of kinetic data without the need for extensive programming or detailed mathematical knowledge. The software automates repetitive tasks and guides users through the key steps of kinetic analysis: determination of constraints to be placed on the aggregation mechanism based on the concentration dependence of the aggregation reaction, choosing from several fundamental models describing assembly into linear aggregates and fitting the chosen models using an advanced minimization algorithm to yield the reaction orders and rate constants. Finally, we outline how to use this approach to investigate which targets potential inhibitors of amyloid formation bind to and where in the reaction mechanism they act. The protocol, from processing data to determining mechanisms, can be completed in