(543d) Amyloid Aggregation: Kinetics, Mechanisms and Models
AIChE Annual Meeting
2015
2015 AIChE Annual Meeting Proceedings
Food, Pharmaceutical & Bioengineering Division
Modeling Approaches for Biological Phenomena I
Wednesday, November 11, 2015 - 1:30pm to 1:50pm
Amyloidogenic proteins associate with each other through cross beta-sheet interactions to form fibrous ordered aggregates involved in a number of neurodegenerative diseases. The kinetics of aggregation depends upon two main factors: the primary sequence and environmental conditions. The aggregation kinetics also critically influences amyloid conformation and strain properties which govern disease patterns in mammals or phenotypic differences in yeast.
The process of amyloid formation follows a two-step pattern of initial nucleation corresponding to a lag phase followed by a fiber elongation phase to finally form a mature fiber. This behavior has been modeled empirically using mathematical functions such as the logistic function, which captures the near sigmoidal shape of the aggregation curve, and other kinetic models, for example the Finke-Watzky (F-W) 2-step mechanism which deconvolutes aggregation into slow continuous nucleation, with rate constant k1, and typically fast autocatalytic growth, with rate constant k2. Various other mechanisms have also been proposed to explain the amyloid aggregation process. However, many of them are not accompanied by proper kinetic analyses. This presentation will focus on the existing models for amyloid aggregation and compare mathematical models which have been used to fit amyloid aggregation data. Non-seeded and seeded aggregation of Sup35NM protein from Saccharomyces cerevisiae and Amyloid beta1-42 in solutions of different ionic compositions and at different temperatures will be used to gain insights into the mechanisms of aggregation. Seeded aggregation is a complex process and depends on the seed characteristics, the monomer and recipient combination and the environmental conditions during aggregation. Further, the applicability of these models to the more complex cases of seeded aggregation will be discussed.
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