CAREER: Resolving the Influence of Biologically Relevant Microenvironments on Amyloid Aggregation

淀粉样蛋白是一种聚集成高度有序结构的蛋白质，并表现出驱动这些蛋白质发挥功能或细胞毒性活性的特征聚集途径。一些淀粉样蛋白，如β-内啡肽（βE），是信号分子，没有任何已知的细胞毒性活性。其他的，如胰岛淀粉样多肽（IAPP）和α-突触核蛋白（αS）参与功能和细胞毒性细胞活动。淀粉样蛋白-β (a β)是一种已知的细胞毒性淀粉样蛋白，没有明确的功能目的，完成了从功能到细胞毒性状态的光谱论证。总的来说，淀粉样蛋白构成了一类非常奇怪的蛋白质，它们在进化和结构上是相互联系的。通过在原子水平上研究膜环境中的淀粉样蛋白聚集事件，我们可以确定驱动这些肽的功能和细胞毒性谱的关键分子特性。分子动力学（MD）模拟提供了一种具有成本效益和原子性的技术来探测模拟细胞环境的复杂膜环境中的这些聚集事件。利用淀粉样蛋白-结构-功能关系的主题，结合计算技术，我们将进一步创造可访问的，可扩展的，可持续的本科研究机会和K-12外展计划，增强学生的生化和数据科学知识，为他们的劳动力做好准备。总的来说，利用尖端的模拟技术系统地研究膜对淀粉样蛋白的影响将提供关于生物物理事件的基本知识，这些事件导致了基本的生物功能，可以用于材料和药物发现途径。众所周知，淀粉样蛋白在体外和体内都难以分解和处理，鉴于其瞬态、亚稳态结构折叠途径和显微镜研究中“表面相似”的比较程度，表征淀粉样蛋白仍然是一个挑战。一个值得注意的淀粉样蛋白世界假说提出，最早的生物信息传递是由淀粉样蛋白介导的，通过结构变化将环境信息加密传递给后代分子实体。鉴于淀粉样蛋白具有多方面的功能和细胞毒性，以及它们在生物信息传递中的作用，了解微环境对淀粉样蛋白聚集的生物物理特性的影响至关重要。我们假设脂膜组成和糖胺聚糖的存在会影响和重塑淀粉样蛋白的聚集事件。与此相关，淀粉样蛋白的结构形态和序列特异性基序将表明在功能性到细胞毒性淀粉样蛋白的光谱上的位置。利用尖端的分子动力学（MD）模拟，包括经典、副本交换和极化MD，我们将模拟低聚淀粉样蛋白在各种膜环境中的聚集事件。构象动力学将探索利用转移网络和马尔可夫状态模型。这项工作将推动MD模拟的能力，从原子角度探索蛋白质聚集途径，并将各种淀粉样蛋白物质（β-内啡肽（βE），胰岛淀粉样蛋白多肽（IAPP）， α-突触核蛋白（αS）和淀粉样蛋白-β (Aβ)）相互连接，以实现生物学意义和现象。利用数据丰富的计算方法将进一步加强这项工作的综合数据和研究素养教育组成部分，通过地方、区域和全球教育计划，将计算和算法思维与生物学联系起来，提高K-12学生、本科生和社区的计算技能。该奖项反映了美国国家科学基金会的法定使命，并通过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，被认为值得支持。

Amyloids are proteins that aggregate into highly ordered structures and exhibit signature aggregation pathways that drive these proteins to functional or cytotoxic activities. Some amyloids, like β-endorphin (βE), are signaling molecules and do not have any known cytotoxic activities. Others, like islet amyloid polypeptide (IAPP) and α-synuclein (αS) participate in both functional and cytotoxic cellular activities. Completing the demonstration of a spectrum of functional to cytotoxic states, amyloid-β (Aβ) is a known cytotoxic amyloid with no clear functional purpose. Taken together, amyloids constitute a very curious class of proteins that are evolutionarily and structurally connected. By studying amyloid aggregation events in membrane environments at the atomistic level, we can identify key molecular properties that drive the spectrum of functionality and cytotoxicity of these peptides. Molecular dynamics (MD) simulations offer a cost-effective and atomistic technique to probe these aggregation events in complex membrane environments that mimic cellular environments. Using the topic of protein-structure function relationship of amyloids, coupled with computational techniques, we will further create accessible, scalable, and sustainable undergraduate research opportunities and K-12 outreach programs that enhance biochemical and data science knowledge to students to prepare them for the workforce. Collectively, systematic investigation of membrane impact on amyloids using cutting-edge simulation techniques will provide essential knowledge on biophysical events that result in essential biological function that can be utilized for materials and drug discovery routes.Notoriously difficult to resolve and work with in vitro and in vivo, amyloid proteins remain a challenge to characterize given their transient, metastable structural folding pathways and a “superficially similar” degree of comparison in microscopy studies. A notable amyloid-world hypothesis has arisen in which the earliest biological information transfer was mediated by amyloid proteins, encrypting environmental information through structural changes to pass onto progeny molecular entities. Given the duality of multifaceted functionality and cytotoxicity of amyloids and their role in biological information transfer, it is crucial to understand the influence of microenvironments on the biophysics of amyloid aggregation. We hypothesize that lipid membrane composition and glycosaminoglycans presence will impact and remodel aggregation events of amyloids. Relatedly, amyloid structural morphologies and sequence-specific motifs will indicate position on the spectrum of functional to cytotoxic amyloids. Utilizing cutting-edge molecular dynamics (MD) simulations, that include classical, replica exchange, and polarizable MD, we will simulate aggregation events of oligomeric amyloids in various membrane environments. Conformational dynamics will be explored utilizing transition networks and Markov state models. This proposed work will push the capabilities of MD simulations to atomistically explore protein aggregation pathways and interconnect various amyloidogenic species (β-endorphin (βE, islet amyloid polypeptide (IAPP), α-synuclein (αS), and amyloid-β (Aβ)), to biological implications and phenomena. Utilization of data-rich, computational methods will further the integrated data and research literacy education components of this work given the ability to connect computational and algorithmic thinking with biology via local, regional, and global education programs that enhance the computational skillsets of K-12 students, undergraduates, and the community.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.