Folding@Home: Simulating folding on the millisecond to second timescale

Folding@Home：在毫秒到秒的时间尺度上模拟折叠

• 批准号: 8242079
• 负责人: VIJAY S PANDE
• 金额: $ 38.09万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8242079
• 关键词: Address; Algorithms; Alzheimer' s Disease; Amino Acids; Area; Biological; Biology; Biophysics; Cell model; Cells; Cellular biology; Collaborations; Computing Methodologies; Confined Spaces; Coupled; Crowding; Data; Development; Disease; Environment; Funding; Future; Generations; Graph; Home environment; Human; Huntington Disease; In Vitro; Investigation; Kinetics; Lead; Learning; Length; Life; Membrane; Membrane Lipids; Methodology; Methods; Modeling; Molecular Chaperones; Nature; Network-based; Peptides; Play; Process; Proteins; Resolution; Ribosomes; Sampling; Scheme; Series; Simulate; Solutions; Solvents; Testing; Time; Work; base; cluster computing; computer cluster; in vivo; innovation; insight; men who have sex with men; millisecond; next generation; novel; protein complex; protein folding; protein misfolding; public health relevance; research study; simulation; theories; tool

DESCRIPTION (provided by applicant): Due the limitations of both simulation and experiment, an ultimate understanding of protein folding will come from a coupled approach of detailed simulations extensively validated and tested by experiment. However, developing simulation methodology which can quantitatively connect with experimental kinetics still remains a great theoretical challenge, due to the long timescales involved and the difficulties and complexities of detailed, atomistic models. Here, we propose new, third generation distributed computing methods to tackle these challenges and the application of these methods to questions related to how proteins self-assemble in solution as well as in the biologically relevant contexts. While protein folding has itself been studied computationally for many years, our work differs from other approaches in (1) its use of innovative distributed computing methods for simulating long, biologically relevant time scale kinetics (on the millisecond to second timescale - dramatically longer than the previous state of the art) and for large and complex proteins (on the 80 to 150 amino acid length scale) using detailed, fully atomistic, explicit solvent models and (2) the application of these detailed models to address questions of folding in the biological contexts of different environments in the cell. Moreover, we are able to perform a quantitative comparison to experiment, which is critical for both the testing and greater impact of our computational methods; indeed, key experimental collaborations using cutting edge methods are proposed to make direct connections to our proposed simulations. Finally, the proposed work would have an impact on our basic understanding of several protein-related diseases, such protein misfolding diseases, such as Alzheimer's Disease and Huntington's Disease. Indeed, methodology from the previous project period has already lead to advances in the simulation of peptide aggregation in Alzheimer's and Huntington's Disease. Also, by understanding the nature of folding in biological contexts, such as in the presence of membranes, in biologically confined spaces, and with crowding agents, and by directly comparing those simulations to novel experiments of folding in the cell, we would gain insight into the nature of protein folding in vivo, which is the next important step in our understanding of protein folding and its connection to biology and biomedical questions. PUBLIC HEALTH RELEVANCE: The process by which proteins (key building blocks in our body) assemble (or "fold") is a critical part of the central dogma of life, but yet is still poorly understood due to immense challenges both experimentally and theoretically. Moreover, numerous diseases, such as Alzheimer's Disease and Huntington's Disease, result from protein misfolding. Here, we propose novel methods to tackle the protein folding problem, at an unprecedented scale, using novel theoretical methods, new analysis tools, and the most powerful computer cluster in the world, Folding@home.

描述(申请人提供)：由于模拟和实验的限制，对蛋白质折叠的最终理解将来自于通过实验广泛验证和测试的详细模拟的耦合方法。然而，由于所涉及的时间尺度较长，以及详细的原子模型的困难和复杂性，开发能够定量地与实验动力学联系起来的模拟方法仍然是一个巨大的理论挑战。在这里，我们提出了新的第三代分布式计算方法来应对这些挑战，并将这些方法应用于与蛋白质如何在溶液中以及在生物相关的环境中自组装相关的问题。虽然蛋白质折叠本身已经通过计算研究了多年，但我们的工作与其他方法的不同之处在于：(1)它使用创新的分布式计算方法来模拟长的、具有生物学意义的时间尺度动力学(在毫秒到秒的时间尺度上-大大长于先前的技术水平)和使用详细的、完全原子化的、显式的溶剂模型来模拟大型和复杂的蛋白质(在80到150氨基酸长度尺度上)以及(2)这些详细模型的应用来解决在细胞中不同环境的生物背景下的折叠问题。此外，我们能够与实验进行定量比较，这对于我们的计算方法的测试和更大的影响都是至关重要的；事实上，使用尖端方法的关键实验协作被提议与我们提出的模拟直接联系。最后，拟议的工作将影响我们对几种与蛋白质相关的疾病的基本理解，例如蛋白质错误折叠疾病，如阿尔茨海默病和亨廷顿病。事实上，前一个项目期的方法学已经在阿尔茨海默氏症和亨廷顿病的多肽聚集模拟方面取得了进展。此外，通过了解生物环境中折叠的性质，例如在膜存在、生物受限空间和拥挤介质中，并通过将这些模拟与细胞中折叠的新实验进行直接比较，我们将深入了解体内蛋白质折叠的性质，这是我们理解蛋白质折叠及其与生物学和生物医学问题的联系的下一个重要步骤。 与公共健康相关：蛋白质(我们身体的关键组成部分)组装(或折叠)的过程是生命中心教条的关键部分，但由于实验和理论上的巨大挑战，人们仍然知之甚少。此外，许多疾病，如阿尔茨海默病和亨廷顿病，都是蛋白质错误折叠的结果。在这里，我们提出了解决蛋白质折叠问题的新方法，规模空前，使用新的理论方法，新的分析工具，以及世界上最强大的计算机集群，Folding@Home。