Development of Generalized-Ensemble Algorithms and their Application in Protein Studies

广义集成算法的发展及其在蛋白质研究中的应用

• 批准号: 0809002
• 负责人: Ulrich Hansmann
• 金额: $ 42万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0809002&HistoricalAwards=false
• 关键词: Development; Generalized; Ensemble; Algorithms; their

Ulrich Hansmann of Michigan Technological University is supported by an award from the Theoretical and Computational Chemistry program for work to further develop generalized-ensemble algorithms for use in protein folding simulations. Partial funding for this award has been provided by the Molecular Biophysics program within the Division of Molecular and Cellular Biology.After the successful deciphering of whole genomes a new challenge has emerged: for most of the newly found sequences we do not know the function of the corresponding proteins. Reliable computational tools that allow one to study the emergence of structure and function of proteins from their sequence of amino acids would therefore lead to deeper insight in the molecular foundation of the fundamental processes in cells. Exploring this relationship in computer simulations is extremely difficult for detailed protein models, and the computational effort to calculate accurately physical quantities increases exponentially with the number of residues in simple room-temperature simulations. In order to overcome this difficulty, the PI has pioneered the generalized ensemble approach and demonstrated its feasibility for simulation of peptides and proteins of up to 40-50 amino acids. By further advancing this approach and implementing it in his highly scalable software package SMMP the PI aims at deriving models that will describe folding and interaction of stable domains in proteins (usually consisting of 50-200 amino acids). Connecting optimization of algorithms to fundamental ideas of statistical mechanics the PI is probing folding and assembly of the 75-residue apo calbindin D9K and the 84-residue tetramer BBTA2; and binding of copper ions to the 37-residue truncated form of yeast copper thionein. His goal is to understand these processes solely from the physical interactions between the atoms within a protein, and between the protein and the surrounding environment. The work is, thus, having a broad impact on the field of biology as well as through the involvement of students, including some American Indian students, in this research.

密歇根理工大学的乌尔里希·汉斯曼因进一步开发用于蛋白质折叠模拟的广义集成算法而获得理论和计算化学计划颁发的奖项。该奖项的部分资金由分子和细胞生物学部门的分子生物物理学项目提供。在成功破译整个基因组后，出现了一个新的挑战：对于大多数新发现的序列，我们不知道相应蛋白质的功能。因此，可靠的计算工具使人们能够从蛋白质的氨基酸序列中研究蛋白质的结构和功能的出现，从而有助于更深入地了解细胞基本过程的分子基础。在计算机模拟中探索这种关系对于详细的蛋白质模型来说是极其困难的，在简单的室温模拟中，精确计算物理量的计算工作量随着残基的数量呈指数增加。为了克服这一困难，PI开创了广义集成方法的先河，并证明了其用于模拟多达40-50个氨基酸的多肽和蛋白质的可行性。通过进一步推进这一方法并将其应用于他的高度可扩展的软件包SMMP中，PI旨在推导出描述蛋白质(通常由50-200个氨基酸组成)中稳定结构域的折叠和相互作用的模型。将算法的优化与统计力学的基本思想联系起来，PI正在探索75个残基的apo calbindin D9K和84个残基的四聚体BBTA2的折叠和组装，以及铜离子与37个残基截断的酵母铜硫蛋白的结合。他的目标是仅从蛋白质内部原子之间的物理相互作用，以及蛋白质与周围环境之间的物理相互作用来理解这些过程。因此，这项工作对生物学领域产生了广泛的影响，并通过学生的参与，包括一些美国印第安人学生，参与了这项研究。