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: 8171934
• 负责人: Jian-Min Yuan
• 金额: $ 0.14万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171934
• 关键词: Bovine Spongiform Encephalopathy; Complex; Computer Retrieval of Information on Scientific Projects Database; Dimensions; Disease; Equation; Funding; Grant; Institution; Mechanics; Methodology; Methods; Neurodegenerative Disorders; Parkinson Disease; Peptides; Phase Transition; Prions; Process; Property; Proteins; Research; Research Personnel; Resources; Source; Statistical Mechanics; Structure; System; Time; United States National Institutes of Health; base; 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 我们建议研究蛋白质或肽的构象变化及其自聚集特性。构象变化包括蛋白质/肽中二级结构的变化，例如螺旋、片层、卷曲、转角等。肽或蛋白质聚集是当前备受关注的领域，因为它与神经退行性疾病（例如阿尔茨海默病和帕金森病）有关。至少对于其中一些疾病，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的螺旋片层转变。因此，我们的部分兴趣是研究蛋白质/肽中的螺旋-片层、片层-螺旋和螺旋-螺旋转变，以及它们的一些聚集过程。我们使用统计力学方法来解决这些问题，例如配分函数、传递矩阵、相变方法和主方程等。对于简单系统，可以通过施加大N（单体数量）近似来获得解析结果。然而，对于现实系统，传递矩阵的维数变得非常大，因此计算在数值上变得具有挑战性。随着聚合尺寸的增加，基于随机方法和复杂网络的自聚合过程的统计机械方法也变得非常需要数值。这主要是因为为了建立相互作用网络所需的转移率信息，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。