STATISTICAL MECHANICS STUDIES OF STRUCTURE CHANGE AND SELF-AGGREGATION OF PROTE

蛋白质结构变化和自聚集的统计力学研究

• 批准号: 8364318
• 负责人: Jian-Min Yuan
• 金额: $ 0.1万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8364318
• 关键词: Alzheimer' s Disease; Biomedical Research; Bovine Spongiform Encephalopathy; Complex; Dimensions; Disease; Equation; Funding; Grant; High Performance Computing; Mechanics; Methodology; Methods; National Center for Research Resources; Neurodegenerative Disorders; Parkinson Disease; Peptides; Phase Transition; Principal Investigator; Prions; Process; Property; Proteins; Research; Research Infrastructure; Resources; Source; Statistical Mechanics; Structure; System; Time; United States National Institutes of Health; base; cost; interest; molecular dynamics; monomer; protein aggregation; supercomputer

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. We propose to study conformational changes of proteins or peptides and their self-aggregation properties. Conformational changes include changes in the secondary structures in protein/peptides, such as helix, sheet, coil, turn, etc. Peptide or protein aggregation is a field of great current interest, because its relation to neurodegenerative diseases, such as Alzheimers and Parkinsons diseases. For, at least, some of these diseases, self-aggregation is facilitated by structure changes in proteins/peptides. A well-known example is the helix-sheet transitions in prion, associated with the mad cow disease. Therefore, part of our interests is to study the helix-sheet, sheet-coil, and helix-coil transitions in proteins/peptides, and some of their aggregation processes. We approach these problems using statistical mechanics methods, such as partition functions, transfer matrices, methodology of phase transitions, and master equations, etc. For simple systems, analytic results can be obtained by imposing on the large-N (number of monomers) approximation. However, for realistic systems, dimensions of the transfer matrices become very large, computation thus becomes numerically challenging. A statistical mechanical approach to self-aggregation processes based on stochastic methods and complex networks also becomes numerically demanding very quick as aggregate size increases. This is mainly because to build up the transition rate information needed for the interaction network, we need to carry out molecular dynamics calculation involving oligomers and monomers. We therefore request CPU time from Pittsburgh Supercomputer Center.

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其他NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 我们建议研究蛋白质或多肽的构象变化及其自聚集特性。构象变化包括蛋白质/肽的二级结构的变化，如螺旋、折叠、卷曲、转角等。肽或蛋白质聚集是当前非常感兴趣的领域，因为其与神经退行性疾病如阿尔茨海默病和帕金森病有关。至少对于这些疾病中的一些，蛋白质/肽的结构变化促进了自聚集。一个众所周知的例子是与疯牛病相关的朊病毒中的螺旋折叠转变。因此，我们的部分兴趣是研究蛋白质/肽中的螺旋-折叠、折叠-卷曲和螺旋-卷曲转变，以及它们的一些聚集过程。我们使用统计力学的方法，如配分函数，转移矩阵，方法的相变，和主方程等，对于简单的系统，可以通过施加上的大N（单体数）近似得到的分析结果。然而，对于实际系统，传递矩阵的维数变得非常大，计算因此变得具有数值挑战性。基于随机方法和复杂网络的自聚集过程的统计力学方法也随着聚集体尺寸的增加而变得非常快。这主要是因为为了建立相互作用网络所需的跃迁速率信息，我们需要进行涉及低聚物和单体的分子动力学计算。因此，我们向匹兹堡超级计算机中心请求CPU时间。