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Statistical Mechanical Treatments of Protein Amyloid Formation
Department of Physics, Drexel University, Philadelphia, PA 19104, USA
* Author to whom correspondence should be addressed.
Received: 27 June 2013; in revised form: 5 August 2013 / Accepted: 9 August 2013 / Published: 23 August 2013
Abstract: Protein aggregation is an important field of investigation because it is closely related to the problem of neurodegenerative diseases, to the development of biomaterials, and to the growth of cellular structures such as cyto-skeleton. Self-aggregation of protein amyloids, for example, is a complicated process involving many species and levels of structures. This complexity, however, can be dealt with using statistical mechanical tools, such as free energies, partition functions, and transfer matrices. In this article, we review general strategies for studying protein aggregation using statistical mechanical approaches and show that canonical and grand canonical ensembles can be used in such approaches. The grand canonical approach is particularly convenient since competing pathways of assembly and dis-assembly can be considered simultaneously. Another advantage of using statistical mechanics is that numerically exact solutions can be obtained for all of the thermodynamic properties of fibrils, such as the amount of fibrils formed, as a function of initial protein concentration. Furthermore, statistical mechanics models can be used to fit experimental data when they are available for comparison.
Keywords: protein aggregation; protein amyloid; statistical mechanics; partition function; transfer matrix
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MDPI and ACS Style
Schreck, J.S.; Yuan, J.-M. Statistical Mechanical Treatments of Protein Amyloid Formation. Int. J. Mol. Sci. 2013, 14, 17420-17452.
Schreck JS, Yuan J-M. Statistical Mechanical Treatments of Protein Amyloid Formation. International Journal of Molecular Sciences. 2013; 14(9):17420-17452.
Schreck, John S.; Yuan, Jian-Min. 2013. "Statistical Mechanical Treatments of Protein Amyloid Formation." Int. J. Mol. Sci. 14, no. 9: 17420-17452.