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

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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.