Statistical Mechanical Treatments of Protein Amyloid Formation

TL;DR

This paper reviews how statistical mechanical methods can be applied to study protein amyloid formation, enabling analysis of complex aggregation processes and providing exact thermodynamic solutions.

Contribution

It introduces the use of canonical and grand canonical ensembles for modeling protein aggregation, highlighting the advantages of the grand canonical approach.

Findings

Grand canonical ensemble simplifies modeling of competing assembly pathways.

Statistical mechanics allows for numerically exact thermodynamic calculations.

Models can be fitted to experimental data for validation.

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.