Development and Application of a Multi-Scale Markov Model for Simulating Protein Folding

模拟蛋白质折叠的多尺度马尔可夫模型的开发与应用

• 批准号: 0954714
• 负责人: Vijay Pande
• 金额: $ 103.94万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2015-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0954714&HistoricalAwards=false
• 关键词: Development; Application; Multi; Scale; Markov

One of the fundamental challenges in molecular simulation is the fact that chemical detail is often necessary for answering questions of interest, yet it is this very detail which greatly limits what is feasible to study. Multi-scale simulation methods give the hope to solve this problem, by combining results from multiple scales, using detail only where it is needed most. Several key issues remain, such as how can one know which details are the most relevant? How can one efficiently, systematically, and rigorously combine results from multiple scales? Here, the PI presents a framework for a novel and highly flexible multi-scale approach, which uses Markov State Models (MSMs) as a framework for multi-scale simulation. Such multi-scale MSMs (or MSMSMs) can combine results from different simulation methodologies, with the expectation of getting the "best of both worlds", i.e. the accuracy of more detailed models and the computational tractability of simpler models. Moreover, MSMSMs present a natural, statistical framework for bringing together multiple simulation methodologies, predicting long timescale behavior from many shorter simulations, and describing and elucidating complex, heterogeneous dynamics. To test these methods, the PI will apply this model to the study of protein folding. There are several broader impacts of this work. First, if successful, The PI's methods could have very broad impact in molecular simulation in fields very far from just protein folding, since the issues of understanding which details are relevant and the use of simple vs complex models are present in essentially all fields of molecular simulation. Second, as the PI uses the Folding@home distributed computing platform for this project, there is a significant education and outreach component; the PI has pioneered several outreach aspects, including the use of YouTube videos, PlayStation 3, and large-scale distributed computing as outreach, and he proposes to continue that work with this project. This work has had the impact in making many people in the general public familiar with biology and chemistry at a sophisticated level, as many individuals participating in Folding@home wish to understand the simulations they are running. Moreover, the PI plans to expand these areas to incorporate means to engage underrepresented groups by direct interaction with teachers from local schools, building curricula and web pages to be put on our Folding@home web site. This project is jointly supported by Molecular Biophysics in the Division of Molecular and Cellular Biosciences and the Theory, Models and Computational Methods Program in the Chemistry Division.

分子模拟的基本挑战之一是，化学细节往往是回答感兴趣的问题所必需的，然而正是这些细节极大地限制了研究的可行性。多尺度模拟方法通过结合多个尺度的结果，只在最需要的地方使用细节，给解决这一问题带来了希望。几个关键问题仍然存在，比如如何知道哪些细节是最相关的？如何有效、系统、严格地结合多个尺度的结果？在这里，PI提出了一种新颖且高度灵活的多尺度方法的框架，该方法使用马尔可夫状态模型（msm）作为多尺度模拟的框架。这种多尺度msm（或msmsm）可以结合不同模拟方法的结果，期望获得“两全美”，即更详细模型的准确性和更简单模型的计算可追溯性。此外，MSMSMs提供了一个自然的统计框架，将多种模拟方法结合在一起，从许多较短的模拟中预测长时间尺度的行为，并描述和阐明复杂的异质动力学。为了测试这些方法，PI将把这个模型应用到蛋白质折叠的研究中。这项工作有几个更广泛的影响。首先，如果成功的话，PI的方法可以在分子模拟领域产生非常广泛的影响，而不仅仅是蛋白质折叠，因为理解哪些细节是相关的，以及使用简单模型和复杂模型的问题基本上存在于分子模拟的所有领域。其次，由于PI在这个项目中使用Folding@home分布式计算平台，因此有一个重要的教育和推广组成部分；PI已经开创了几个扩展方面，包括使用YouTube视频、PlayStation 3和大规模分布式计算作为扩展，他建议在这个项目中继续这项工作。这项工作已经产生了影响，使许多人在普通公众熟悉生物学和化学在一个复杂的水平，因为许多个人参与Folding@home希望了解他们正在运行的模拟。此外，PI计划扩大这些领域，通过与当地学校的教师直接互动，建立课程和网页，将代表性不足的群体纳入我们的Folding@home网站。本项目由分子与细胞生物科学部的分子生物物理学和化学部的理论、模型和计算方法项目共同支持。