Efficient Simulation of Protein-Membrane Interactions by Implicit Solvent Algorithms

通过隐式溶剂算法有效模拟蛋白质-膜相互作用

• 批准号: 0241236
• 负责人: Dexuan Xie
• 金额: --
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2007-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0241236&HistoricalAwards=false
• 关键词: Efficient; Simulation; Protein; Membrane; Interactions

This project aims to develop a new computational model for studying protein conformation changes upon membrane interactions. Its objectives are to (1) develop a robust and efficient parallel iterative algorithm for solving the Poisson-Boltzmann equation; (2) define a new biomolecular potential-energy function through a large reduction of the number of conformation-freedom variables for a family of particular proteins; (3) develop a new sparse matrix compress scheme for programming an efficient application-tailored preconditioner in an optimal order of memory locations; (4) develop a parallel iterative algorithm for constrained molecular dynamics simulations; and (5) train undergraduate and graduate researchers for the post-genomic era. Specifically, the new algorithms and the new model will be applied to studying the molecular mechanism of membrane damage induced by the protein toxin Cyt1A upon membrane interactions. The theoretical results will also be compared to laboratory data produced by biochemical and biophysical methods such as fluorescence spectroscopy, surface tensiometry, and electron microscopy. Synthesis of the computational and experimental data may bring new insights to the manner of membrane permeabilization and damage by proteins that do not transverse the lipid bilayer. Efficient algorithms and modeling of protein-membrane interactions can save considerable amounts of time, money, and laboratory effort in biotechnology, medicine, agriculture, and the food, pharmaceutical, or defense industries. The new algorithms and the new model developed in this project can significantly simplify computing complexity in simulations of large protein-water-membrane systems. They can also be applied to various biomolecular simulations. In fact, numerical solution of the Poisson-Boltzmann equation plays a central role in implicit solvent models. The molecular potential energy function is a cornerstone of molecular modeling, and the minimization of the molecular potential energy function and the molecular dynamics simulation are two fundamental tasks in computational biology. Furthermore, the chosen model protein, the toxin Cyt1A, has been used as an environmentally-safe insecticide specific against mosquitoes and blackflies. Elucidating its mode of action may help the industry increase the toxin's efficacy by targeting specificity and synergism with other insecticides. Additionally, through the collaboration between a mathematician/computer scientist and a biochemist/biophysicist, students involved in this project will be trained in a virtual hub of mathematics, computer science, and the life sciences. This grant is made under the Joint DMS/NIGMS Initiative to Support Research Grants in the Area of Mathematical Biology. This is a joint competition sponsored by the Division of Mathematical Sciences (DMS) at the National Science Foundation and the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health.

本计画旨在发展一种新的计算模型，以研究蛋白质在膜相互作用下的构象变化。其目标是（1）发展一种求解Poisson-Boltzmann方程的鲁棒而有效的并行迭代算法：（2）通过大量减少特定蛋白质家族的构象自由变量来定义一种新的生物分子势能函数;（3）提出了一种新的稀疏矩阵压缩方案，用于编写高效的应用程序-该研究的目的是：（1）以最佳的存储位置顺序定制预处理器;（2）开发用于约束分子动力学模拟的并行迭代算法;（3）为后基因组时代培养本科生和研究生研究人员。具体而言，新的算法和新的模型将被应用于研究蛋白毒素Cyt 1A在膜相互作用时引起的膜损伤的分子机制。理论结果也将被比较到实验室数据产生的生物化学和生物物理方法，如荧光光谱，表面张力，和电子显微镜。合成的计算和实验数据可能会带来新的见解的方式膜透性和损伤的蛋白质，不横向的脂质双层。蛋白质-膜相互作用的有效算法和建模可以节省生物技术、医学、农业和食品、制药或国防工业中大量的时间、金钱和实验室工作。本计画所发展之新演算法与新模型，可大幅简化模拟大型蛋白质-水-膜系统之计算复杂度。它们也可以应用于各种生物分子模拟。事实上，泊松-玻尔兹曼方程的数值解在隐式溶剂模型中起着核心作用。分子势能函数是分子建模的基石，分子势能函数的极小化和分子动力学模拟是计算生物学的两个基本任务。此外，所选择的模型蛋白，毒素Cyt 1A，已被用作一种环境安全的杀虫剂，专门针对蚊子和黑蝇。阐明其作用模式可能有助于该行业通过靶向特异性和与其他杀虫剂的协同作用来提高毒素的效力。此外，通过数学家/计算机科学家和生物化学家/生物化学家之间的合作，参与该项目的学生将在数学，计算机科学和生命科学的虚拟中心接受培训。这项赠款是根据联合DMS/NIGMS倡议，以支持研究赠款在数学生物学领域。这是一项由美国国家科学基金会数学科学部（DMS）和美国国立卫生研究院国家普通医学科学研究所（NIGMS）赞助的联合竞赛。