EAGER: Molecular-Level Stochastic Simulation To Predict The Dynamics of Protein Misfolding and Aggregation

EAGER：分子水平随机模拟预测蛋白质错误折叠和聚集的动态

• 批准号: 1158608
• 负责人: Preetam Ghosh
• 金额: $ 12.38万
• 依托单位: Virginia Commonwealth University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1158608&HistoricalAwards=false
• 关键词: EAGER; Molecular; Level; Stochastic; Simulation

EAGER: Molecular-Level Stochastic Simulation to predict the dynamics of Protein Misfolding and Aggregation Summary: Proteins are biochemical workhorses that are needed in many important biological functions. Lately one aspect of these macromolecules have gained significant attention, which is their ability to ¡¥stick to each other¡¦ to from ¡¥protein aggregates¡¦ or ¡¥amyloids¡¦. This behavior is more common when proteins fail to adopt a ¡¥correct¡¦ three dimensional shape, commonly known as a ¡¥misfolded¡¦ form. These aggregates can be both beneficial and toxic for cellular processes. Although seems simple, this process is extremely complicated and no precise molecular understanding has emerged. Also, the ability of the proteins to misfold and aggregate via multiple pathways leading to various forms of aggregates has never been explored. Such molecular-level details of the process are important to know since functional aspects of these aggregates are related to the molecular size, shapes, stability and the rates of the formation. Since many of these parameters of this stochastic process are extremely difficult to analyze via conventional biochemical means, molecular-level computational simulations can be valuable. A precise understanding of the protein aggregation phenomenon would broaden the fundamental knowledge of both pathological and functional aspects of biomolecular science besides throwing newer insights. In this proposal, we will use amyloid-?Ò (A?Ò) peptide as a model protein that is known to form misfolded aggregates to accomplish our goals. Our main objective is to establish a fundamental framework for stochastic molecular-level simulation of the ¡¥on-pathway¡¦ fibril formation process that will serve as a basis for analyzing more realistic models with competing pathways to precisely predict the dynamics and mechanisms of protein aggregation. We have initiated a collaborative effort between two PIs at University of Southern Mississippi, (USM) with expertise in computational and biophysical analysis, to achieve our truly inter-disciplinary objectives. Intellectual Merit: Protein aggregation is a nucleation-dependent process, however, precise understanding of its kinetics is not yet known. Aggregation and fiber formation is often considered to be a stochastic process with large variations in macroscopic molecule behavior and hence, stochastic molecular-level simulations would be essential to understand their dynamics. Furthermore, it is not realistic to consider aggregation as an isolated event as there are many different factors that influence protein aggregation in a physiological environment. Broadly, these include molecules that may ¡¥interact¡¦ with the protein besides others such as ionic strength, temperature etc. Hence in this proposal, we are focused on developing a fundamental framework of modeling protein aggregation and amyloid formation phenomenon via molecular-level modeling and stochastic simulation methodologies. The biophysical experiments can show the cumulative effects of the aggregates whereas the simulation will be able to predict the concentration change dynamics with respect to time for every aggregate involved in the pathway. This will allow us to study the exact nature of each aggregate and their sensitivity to the over-all pathway dynamics. Broader Impact: USM is an excellent place to conduct this research from a scientist training perspective; MS being among the states with the highest levels of poverty besides providing a truly diverse student population. The research will provide a broader impact to the scientific community in the form of a fundamental mechanistic knowledge about protein aggregation systems. Our educational outreach mechanisms will involve giving seminars at participating undergraduate institutions in MS, recruiting economically disadvantaged students (including women and minorities) to perform summer research in the PI and Co-PI laboratories at USM. It will also enhance our graduate program through the design of new inter-disciplinary courses (e.g. ¡§Systems Biology¡¨ and ¡§Computational biophysics¡¨).

EARGER：预测蛋白质错误折叠和聚集动力学的分子水平随机模拟摘要：蛋白质是许多重要生物功能所需的生化主力。最近，这些大分子的一个方面得到了显著的关注，那就是它们相互粘连形成人民币蛋白质聚集体或人民币淀粉样蛋白的能力。当蛋白质不能采用正确的三维形状，也就是通常所说的错误折叠形式时，这种行为更为常见。这些聚集体对细胞过程既是有益的，也是有害的。尽管看起来很简单，但这个过程非常复杂，而且还没有出现精确的分子理解。此外，蛋白质通过多种途径错误折叠和聚集的能力也从未被探索过，从而导致各种形式的聚集。由于这些聚集体的功能方面与分子大小、形状、稳定性和形成速率有关，因此了解这一过程的分子水平的细节是很重要的。由于这种随机过程的许多参数很难通过传统的生化手段进行分析，因此分子水平的计算模拟可能是有价值的。对蛋白质聚集现象的准确理解除了提供新的见解外，还将拓宽生物分子科学病理和功能方面的基础知识。在这项提案中，我们将使用淀粉样多肽作为模型蛋白质，已知这种蛋白质会形成错误折叠的聚集体，以实现我们的目标。我们的主要目标是建立一个基本的随机分子水平模拟的“元在途径上”的纤维形成过程的基本框架，这将作为分析更现实的模型和竞争途径的基础，以精确预测蛋白质聚集的动力学和机制。我们在南密西西比大学(USM)的两个PI之间发起了一项合作努力，拥有计算和生物物理分析方面的专业知识，以实现我们真正的跨学科目标。智力优势：蛋白质聚集是一个依赖于成核的过程，然而，对其动力学的准确理解尚不清楚。聚集和纤维形成通常被认为是一个宏观分子行为有很大变化的随机过程，因此，随机分子水平的模拟对于理解它们的动力学是必不可少的。此外，认为聚集是一个孤立的事件是不现实的，因为在生理环境中有许多不同的因素影响蛋白质聚集。广义地说，除了离子强度、温度等分子外，还包括可能与蛋白质相互作用的分子。因此，在本提案中，我们专注于通过分子水平建模和随机模拟方法建立一个模拟蛋白质聚集和淀粉样蛋白形成现象的基本框架。生物物理实验可以显示聚集体的累积效应，而模拟将能够预测路径中涉及的每个聚集体的浓度随时间的变化动力学。这将使我们能够研究每个聚集体的确切性质以及它们对所有途径动力学的敏感性。更广泛的影响：从科学家培训的角度来看，密歇根州立大学是进行这项研究的绝佳地点；密歇根州是贫困程度最高的州之一，除了提供真正多样化的学生群体外。这项研究将以关于蛋白质聚集系统的基本机制知识的形式向科学界提供更广泛的影响。我们的教育外展机制将包括在密歇根州立大学参与的本科院校举办研讨会，招募经济困难的学生(包括女性和少数族裔)到密歇根州立大学的PI和Co-PI实验室进行暑期研究。它还将通过设计新的跨学科课程(例如“系统生物学”和“计算生物物理学”)来加强我们的研究生课程。