Morris2008 - Fitting protein aggregation data via F-W 2-step mechanism

Morris2008 - Fitting protein aggregation data
via F-W 2-step mechanism

This model is described in the article:

Fitting neurological protein
aggregation kinetic data via a 2-step, minimal/"Ockham's razor"
model: the Finke-Watzky mechanism of nucleation followed by
autocatalytic surface growth.

Morris AM, Watzky MA, Agar JN, Finke
RG.
Biochemistry 2008 Feb; 47(8):
2413-2427
Abstract:

The aggregation of proteins has been hypothesized to be an
underlying cause of many neurological disorders including
Alzheimer's, Parkinson's, and Huntington's diseases; protein
aggregation is also important to normal life function in cases
such as G to F-actin, glutamate dehydrogenase, and tubulin and
flagella formation. For this reason, the underlying mechanism
of protein aggregation, and accompanying kinetic models for
protein nucleation and growth (growth also being called
elongation, polymerization, or fibrillation in the literature),
have been investigated for more than 50 years. As a way to
concisely present the key prior literature in the protein
aggregation area, Table 1 in the main text summarizes 23 papers
by 10 groups of authors that provide 5 basic classes of
mechanisms for protein aggregation over the period from 1959 to
2007. However, and despite this major prior effort, still
lacking are both (i) anything approaching a consensus mechanism
(or mechanisms), and (ii) a generally useful, and thus widely
used, simplest/"Ockham's razor" kinetic model and associated
equations that can be routinely employed to analyze a broader
range of protein aggregation kinetic data. Herein we
demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism
of slow continuous nucleation, A --> B (rate constant k1),
followed by typically fast, autocatalytic surface growth, A + B
--> 2B (rate constant k2), is able to quantitatively account
for the kinetic curves from all 14 representative data sets of
neurological protein aggregation found by a literature search
(the prion literature was largely excluded for the purposes of
this study in order provide some limit to the resultant
literature that was covered). The F-W model is able to
deconvolute the desired nucleation, k1, and growth, k2, rate
constants from those 14 data sets obtained by four different
physical methods, for three different proteins, and in nine
different labs. The fits are generally good, and in many cases
excellent, with R2 values >or=0.98 in all cases. As such,
this contribution is the current record of the widest set of
protein aggregation data best fit by what is also the simplest
model offered to date. Also provided is the mathematical
connection between the 1997 F-W 2-step mechanism and the 2000
3-step mechanism proposed by Saitô and co-workers. In
particular, the kinetic equation for Saitô's 3-step
mechanism is shown to be mathematically identical to the
earlier, 1997 2-step F-W mechanism under the 3 simplifying
assumptions Saitô and co-workers used to derive their
kinetic equation. A list of the 3 main caveats/limitations of
the F-W kinetic model is provided, followed by the main
conclusions from this study as well as some needed future
experiments.

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