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: 8638971
• 负责人: VIJAY S PANDE
• 金额: $ 37.83万
• 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/8638971
• 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.

描述（由申请人提供）：由于模拟和实验的局限性，对蛋白质折叠的最终理解将来自通过实验广泛验证和测试的详细模拟的耦合方法。然而，开发模拟方法，可以定量连接实验动力学仍然是一个巨大的理论挑战，由于涉及的时间尺度长，详细的，原子模型的困难和复杂性。在这里，我们提出了新的，第三代分布式计算方法来解决这些挑战和这些方法的应用程序有关的问题，蛋白质如何自组装的解决方案，以及在生物相关的背景下。虽然蛋白质折叠本身已经被计算研究了很多年，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，生物相关时标动力学（在毫秒到秒的时间尺度上-比以前的技术水平显著更长）和对于大的和复杂的蛋白质（在80至150个氨基酸长度尺度上）使用详细的、完全原子化的、明确的溶剂模型，以及（2）应用这些详细的模型来解决细胞中不同环境的生物学背景下的折叠问题。此外，我们能够对实验进行定量比较，这对于我们的计算方法的测试和更大的影响至关重要;事实上，使用尖端方法的关键实验合作被提出来与我们提出的模拟直接联系。最后，所提出的工作将对我们对几种蛋白质相关疾病的基本理解产生影响，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，前一个项目期间的方法已经导致阿尔茨海默氏症和亨廷顿氏病中肽聚集模拟的进展。此外，通过了解生物学背景下折叠的性质，例如在膜的存在下，在生物学受限空间中，以及与拥挤剂，并通过直接将这些模拟与细胞中折叠的新实验进行比较，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联系的下一个重要步骤。