Collaborative Research: On-The-Fly free Energy Parameterization in Molecular Aimulations

合作研究：分子模拟中的动态自由能参数化

• 批准号: 1207389
• 负责人: Cameron Abrams
• 金额: $ 28.37万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1207389&HistoricalAwards=false
• 关键词: Collaborative; Research; Fly; free; Energy

TECHNICAL SUMMARYThis award supports computational and theoretical research and education with an aim to develop a new method for extracting free energies from molecular dynamics simulations. The method is based on temperature-acceleration, wherein free energy gradients in some desired set of collective variables are sampled in a running molecular dynamics simulation while simultaneously driving those collective variables over free energy barriers that would otherwise render traditional molecular dynamics ineffective. These gradients are used as inputs into an optimization procedure that minimizes the error associated with gradients of an analytical free energy.The major thrusts of the research are: (1) benchmark the performance of the method against alternative approaches for computing coarse grained potentials, such as iterative Boltzmann inversion and force-matching, (2) developing the method as a tool for deriving potentials of mean force for multiscale polymer molecular simulations, and (3) developing the method as a means of statistical evaluation of large-scale protein conformational sampling.The method is formulated in a general way and is therefore expected to be applicable broadly to almost any desired collective variables. This project will be devoted to developing this method particularly for applications involving deriving effective coarse-grained potentials for multiscale simulations and to the study of conformational statistics of large biomolecules. All simulation codes developed in this project will be made freely available to the community.This award also supports training and mentoring of underrepresented students in state of the art computational and numerical methods.NON-TECHNICAL SUMMARYThis award supports computational and theoretical research and education with an aim to develop a new method of extracting information from computer simulations. Whether in designing new drugs or new materials, or simply trying to understand how nature works, computer simulations are important in understanding how matter behaves at the molecular level. Bringing molecular-level understanding into the realm of everyday perception requires statistical approaches that are not always straightforward and often computationally quite expensive. This award supports developing a new statistical method for extracting information from molecular-level simulations. The method is potentially much more efficient than existing approaches. The research will develop and apply this method to test systems relevant for biological and materials applications. The project could significantly reduce the cost associated with obtaining high-quality information from simulations at the molecular level that at the intersections of chemistry, physics, and biology. All simulation codes developed in this project will be made freely available to the community.This award also supports training and mentoring of underrepresented students in state of the art computational and numerical methods.

该奖项支持计算和理论研究和教育，旨在开发从分子动力学模拟中提取自由能的新方法。该方法基于温度加速，其中在运行的分子动力学模拟中对一些期望的集合变量中的自由能梯度进行采样，同时驱动那些集合变量越过自由能势垒，否则这些自由能势垒将使传统的分子动力学无效。这些梯度被用作最优化过程的输入，最大限度地减少与分析自由能梯度相关的误差。研究的主要目标是：（1）将该方法的性能与用于计算粗粒度势的替代方法（诸如迭代玻尔兹曼反演和力匹配）进行基准测试，（2）发展该方法作为多尺度聚合物分子模拟的平均力势的推导工具，（3）将该方法发展为大型企业的统计评价手段，规模蛋白质构象采样。该方法是制定在一个一般的方式，因此预计将广泛适用于几乎任何所需的集体变量。该项目将致力于开发这种方法，特别是用于涉及获得有效的粗粒度的多尺度模拟和大生物分子的构象统计研究的潜力的应用。该项目开发的所有模拟代码将免费提供给社区。该奖项还支持对代表性不足的学生进行最先进的计算和数值方法的培训和指导。非技术总结该奖项支持计算和理论研究和教育，旨在开发从计算机模拟中提取信息的新方法。 无论是在设计新药物或新材料，或只是试图了解自然界是如何工作的，计算机模拟对于理解物质在分子水平上的行为都很重要。将分子水平的理解带入日常感知领域需要统计方法，这些方法并不总是简单明了的，而且在计算上往往非常昂贵。该奖项支持开发一种新的统计方法，用于从分子水平的模拟中提取信息。该方法可能比现有方法更有效。该研究将开发并应用这种方法来测试与生物和材料应用相关的系统。该项目可以显着降低从化学，物理和生物学交叉点的分子水平模拟中获得高质量信息的成本。该项目开发的所有模拟代码将免费提供给社区。该奖项还支持在最先进的计算和数值方法方面对代表性不足的学生进行培训和指导。