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.

技术摘要这一奖项支持计算和理论研究和教育，旨在开发一种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器，其中在运行的分子动力学模拟中对一些所需的集体变量集中进行了自由能梯度，同时将这些集体变量驱动到自由能屏障上，否则传统的分子动力学将使传统的分子动力学无效。这些梯度被用作输入到优化程序中，该程序可最大程度地减少与分析自由能梯度相关的误差。研究的主要推力是：（1）基准该方法对计算粗粒子电位的替代方法的性能，用于迭代boltzmann逆转和力量造成的量式摩尔（2）工具（2） （3）开发该方法作为大规模蛋白质构象采样的统计评估的一种手段。该方法以一般方式制定，因此预计将广泛适用于几乎所有所需的集体变量。该项目将致力于开发此方法，特别是用于涉及多尺度模拟的有效粗粒电位的应用以及大型生物分子的构象统计研究。该项目中开发的所有仿真代码都将免费提供给社区。该奖项还支持以最先进的计算和数值方法对代表性不足的学生进行培训和指导。Non-echnical-technical摘要摘要这一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。 无论是设计新药还是新材料，还是只是试图了解自然的工作原理，计算机模拟对于理解物质在分子层的行为都很重要。将分子水平的理解带入日常感知领域需要统计方法，这些方法并不总是很简单，而且通常在计算上很昂贵。该奖项支持开发一种从分子级模拟中提取信息的新统计方法。该方法可能比现有方法要高得多。该研究将开发并将这种方法应用于与生物和材料应用相关的测试系统。该项目可以显着降低与从化学，物理学和生物学相交的分子水平上获取模拟中获得高质量信息相关的成本。该项目中开发的所有仿真代码都将免费提供给社区。该奖项还支持以最先进的计算和数值方法对代表性不足的学生进行培训和指导。