Treecode-Accelerated Implicit Solvent Models for Biomolecular Simulations

用于生物分子模拟的 Treecode 加速隐式溶剂模型

• 批准号: 0915057
• 负责人: Robert Krasny
• 金额: $ 24.27万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915057&HistoricalAwards=false
• 关键词: Treecode; Accelerated; Implicit; Solvent; Models

Current biomolecular simulations are unable to reach the long time scales needed to study conformation changes such as protein folding. One of the main obstacles is the high cost of computing the electrostatic forces among the solvent water molecules surrounding the protein. To address this issue, this project adopts an implicit solvent model in which the electrostatic potential satisfies the Poisson-Boltzmann (PB) equation. Numerical solution of the PB equation poses a challenge due to the geometric complexity of the molecular surface, the discontinuity in the dielectric function, and the unbounded computational domain. The investigators will overcome these difficulties by developing a boundary integral PB solver using a new Cartesian treecode algorithm for screened Coulomb interactions. The treecode-accelerated PB solver will be tested on benchmark examples such as Kirkwood's solution for a spherical surface, and the results will be compared with those obtained using other PB solvers. In addition to the electrostatic potential, the code will be extended to compute other important quantities such as the solvation free energy and solvation forces needed for dynamics. One obstacle facing current biomolecular simulations is the expense of computing the self-induced electrostatic forces among the molecules in the system. Advances in computer hardware alone won't achieve the improvements necesssary for studying long time molecular dynamics. This project therefore focuses on improving the mathematical algorithms used in these computer simulations. In addition to enabling more accurate and efficient biomolecular simulations, the algorithms developed will be potentially useful in other applications where electrostatic forces play a role, for example in modeling charge transport in fuel cells. The project will contribute to training the scientific workforce by supporting the research of a postdoc who will be mentored by the PI.

目前的生物分子模拟无法达到研究构象变化（如蛋白质折叠）所需的长时间尺度。其中一个主要障碍是计算蛋白质周围的溶剂水分子之间的静电力的成本很高。为了解决这个问题，本项目采用隐式溶剂模型，其中静电势满足泊松-玻尔兹曼（PB）方程。由于分子表面的几何复杂性、介电函数的不连续以及计算域的无界性，对PB方程的数值求解提出了挑战。研究人员将通过开发一个边界积分PB解算器来克服这些困难，该解算器使用一种新的笛卡尔树码算法来筛选库仑相互作用。树码加速PB求解器将在诸如球面的Kirkwood解等基准实例上进行测试，并将结果与使用其他PB求解器获得的结果进行比较。除了静电势，代码将被扩展到计算其他重要的量，如动力学所需的溶剂化自由能和溶剂化力。当前生物分子模拟面临的一个障碍是计算系统中分子之间自诱导静电力的费用。仅靠计算机硬件的进步并不能实现长期分子动力学研究所必需的改进。因此，这个项目的重点是改进这些计算机模拟中使用的数学算法。除了实现更精确和高效的生物分子模拟外，所开发的算法在静电力发挥作用的其他应用中也有潜在的用处，例如在燃料电池中的电荷传输建模中。该项目将通过支持一名博士后的研究，为培养科学人才做出贡献，该博士后将由PI指导。