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

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，由于所涉及的长时间尺度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相联系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解决这些挑战，并将这些方法应用于与蛋白质如何在溶液中自组装以及在生物学相关背景下相关的问题。虽然蛋白质折叠本身已经进行了多年的计算研究，但我们的工作与其他方法的不同之处在于：(1)使用创新的分布式计算方法来模拟长时间的生物相关时间尺度动力学（在毫秒到秒的时间尺度上-比以前的技术水平要长得多），以及使用详细的，完全原子的，明确的溶剂模型和(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，建议使用尖端方法进行关键实验合作，以直接连接我们提出的模拟。最后，这项工作将对我们对几种蛋白质相关疾病的基本认识产生影响，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，上一个项目期间的方法已经在阿尔茨海默病和亨廷顿病的肽聚集模拟方面取得了进展。此外，通过理解生物环境下折叠的本质，比如在膜的存在下，在生物受限的空间里，在拥挤剂的作用下，通过直接将这些模拟与细胞内折叠的新实验进行比较，我们将深入了解蛋白质在体内折叠的本质，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。

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.