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Collaborative Research: Dynamics of surfactant - amyloid-beta protein interactions during self-assembly

合作研究：自组装过程中表面活性剂 - 淀粉样蛋白 - β 蛋白相互作用的动力学

基本信息

批准号：
1802641
负责人：
Ashuwin Vaidya
金额：
$ 11.14万
依托单位：
Montclair State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1802641&HistoricalAwards=false
关键词：
Collaborative Research Dynamics surfactant amyloid

项目摘要

Clumping of protein molecules in a process called "aggregation" is a common problem in cellular biology. One kind of protein aggregation leads to a specific structure of the clumps called "amyloids" and is thought to play a key role in many neurodegenerative diseases including Alzheimer's disease. Recently, protein amyloids have also been associated with normal cellular functions. A wide variety of aggregate structures are formed during aggregation, and many of these cause a specific biological response. Hence, understanding the mechanisms of aggregation is critical for human health. Amyloid formation is influenced by biological surfactant molecules, which associate many amyloid proteins. In the proposed project, the principal investigator along with a team of collaborators will use experiments, computer simulations, and mathematical analysis to understand how surfactants modulate amyloid protein aggregation to form specific aggregate species. This will transform our understanding of these interactions which play a role in Alzheimer's disease. This scientific endeavor will also involve many students, who will not only learn new areas of research but will also contribute towards data collection and interpretation of results.Self-association of a protein called amyloid beta, associated with Alzheimer's disease, involves the conversion from its intrinsically disordered, monomeric form to well-organized, fibrillar structures in a nucleation-dependent manner. Among the aggregates formed, the low-molecular weight oligomers have emerged to be physiologically important species. The oligomers formed need not be the obligate intermediates of the fibril formation pathway, and they could be formed along alternate "off-pathways", which result in many distinct structural strains of amyloid beta. Many factors can induce off-pathway products. Among these factors, interfaces generated by biological surfactants (SAs) are physiologically relevant due to their perpetual association with amyloid beta. Recent findings from the principle investigator's lab indicate that non-esterified fatty acid SAs induce off- or on- pathway aggregates in a concentration dependent manner. The investigators hypothesize that concentration-dependent phase transitions of SAs modulate amyloid beta aggregation pathways to generate distinct oligomers. In this work, a collaborative team will test the hypothesis with two specific aims: Aim 1 will investigate biophysically the interactions between amyloid beta and SAs using a variety of anionic SAs, and Aim 2 will model temporal evolution of oligomers as a result of amyloid beta-SA interactions using numerical simulations and reduced order mathematical analysis. An interdisciplinary approach involving experimental biophysics, simulation and mathematical analysis with a synergistic feedback between the experiments (Aim 1) and simulations (Aim 2) will provide insights into the heterotypic interactions between amyloid beta and SAs and will bridge the existing knowledge gap in the field. Considering that the functional effects of off-pathway aggregates are related to their respective morphologies, understanding the physiochemical properties of amyloid beta-SA interactions will be of paramount significance in deciphering amyloid-related cellular processes. This work will also help in a better understanding of aggregation pathways in a larger, more complex network of reactions involving amyloids, which can then be utilized to design intervention strategies in the future. The proposed project will have broader impacts on science and education for undergraduate and graduate students from the three institutions of University of Southern Mississippi (USM), Virginia Commonwealth University (VCU), and Montclair State University (MSU) spanning the areas of molecular biophysics, computational systems biology and mathematical biology respectively.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蛋白质分子在称为“聚集”的过程中成团是细胞生物学中的常见问题。一种蛋白质聚集导致称为“淀粉样蛋白”的团块的特定结构，并被认为在包括阿尔茨海默病在内的许多神经退行性疾病中起关键作用。最近，淀粉样蛋白也与正常细胞功能相关。在聚集过程中形成各种各样的聚集体结构，其中许多引起特定的生物反应。因此，了解聚集机制对人类健康至关重要。淀粉样蛋白的形成受到生物表面活性剂分子的影响，这些分子与许多淀粉样蛋白相关。在拟议的项目中，主要研究者沿着与一组合作者将使用实验、计算机模拟和数学分析来了解表面活性剂如何调节淀粉样蛋白聚集以形成特定的聚集物种。这将改变我们对这些在阿尔茨海默病中起作用的相互作用的理解。这项科学奋进也将涉及许多学生，他们不仅将学习新的研究领域，而且将有助于数据收集和结果的解释。与阿尔茨海默病相关的一种称为淀粉样β蛋白的蛋白质的自缔合涉及以成核依赖的方式从其内在无序的单体形式转化为组织良好的纤维状结构。在形成的聚集体中，低分子量低聚物已成为生理上重要的物质。形成的寡聚体不一定是原纤维形成途径的专性中间体，它们可以沿着沿着交替的“关闭途径”形成，这导致淀粉样蛋白β的许多不同的结构菌株。许多因素可以诱导非途径产物。在这些因素中，生物表面活性剂（SA）产生的界面是生理相关的，因为它们与淀粉样蛋白β永久相关。主要研究者实验室的最新发现表明，非酯化脂肪酸SA以浓度依赖性方式诱导途径外或途径上聚集体。研究人员假设SA的浓度依赖性相变调节淀粉样蛋白β聚集途径以产生不同的寡聚体。在这项工作中，一个合作小组将测试假设有两个具体的目标：目标1将调查生物药理学淀粉样蛋白β和SA之间的相互作用，使用各种阴离子SA，和目标2将建模的时间演变的低聚物作为淀粉样蛋白β-SA相互作用的结果，使用数值模拟和降阶数学分析。涉及实验生物物理学，模拟和数学分析与实验（目标1）和模拟（目标2）之间的协同反馈的跨学科方法将提供深入了解淀粉样蛋白β和SA之间的异型相互作用，并将弥合现有的知识差距在该领域。考虑到非途径聚集体的功能效应与其各自的形态学有关，理解淀粉样蛋白β-SA相互作用的理化性质将在破译淀粉样蛋白相关的细胞过程中具有至关重要的意义。这项工作还将有助于更好地了解涉及淀粉样蛋白的更大、更复杂的反应网络中的聚集途径，然后可以利用这些途径来设计未来的干预策略。拟议的项目将对南密西西比大学（USM）、弗吉尼亚联邦大学（VCU）和蒙特克莱尔州立大学（MSU）这三所大学的本科生和研究生的科学和教育产生更广泛的影响，这些大学横跨分子生物物理学、该奖项反映了NSF的法定使命，并被认为是值得支持的，使用基金会的知识价值和更广泛的影响审查标准进行评估。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Global fitting and parameter identifiability for amyloid-β aggregation with competing pathways

具有竞争途径的淀粉样蛋白β聚集的全局拟合和参数可识别性

DOI：
10.1109/bibe50027.2020.00020
发表时间：
2020
期刊：
2020 IEEE 20th International Conference on BioInformatics and BioEngineering (BIBE
影响因子：
0
作者：
Rana, Pratip;Bose, Priyankar;Vaidya, Ashwin;Rangachari, Vijay;Ghosh, Preetam
通讯作者：
Ghosh, Preetam

Network Thermodynamics-Based Scalable Compartmental Model for Multi-Strain Epidemics

基于网络热力学的多菌株流行病可扩展房室模型