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

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 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/多肽的二级结构的变化，如螺旋、片状、卷曲、转折等。多肽或蛋白质聚集是当前非常感兴趣的领域，因为它与神经退行性疾病，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/多肽的结构变化促进了自我聚集。一个广为人知的例子是与疯牛病有关的Pron中的螺旋-折叠转变。因此，我们感兴趣的一部分是研究蛋白质/肽中的螺旋-片状、片状-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，如配分函数、传递矩阵、相变方法和主方程等来处理这些问题。对于简单的系统，可以通过施加大N(单体数)近似来获得解析结果。然而，对于实际系统，传递矩阵的维度变得非常大，因此计算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机械方法也随着聚集大小的增加而变得非常迅速地需要数值计算。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，我们需要进行涉及齐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申请CPU时间。

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.