Morris2009 - α-Synuclein aggregation variable temperature and pH

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Morris2009 - α-Synuclein aggregation variable temperature and pH

This model is described in the article:

Morris AM, Finke RG.
Biophys. Chem. 2009 Mar; 140(1-3): 9-15

Abstract:

The aggregation of proteins is believed to be intimately connected to many neurodegenerative disorders. We recently reported an "Ockham's razor"/minimalistic approach to analyze the kinetic data of protein aggregation using the Finke-Watzky (F-W) 2-step model of nucleation (A-->B, rate constant k(1)) and autocatalytic growth (A+B-->2B, rate constant k(2)). With that kinetic model we have analyzed 41 representative protein aggregation data sets in two recent publications, including amyloid beta, alpha-synuclein, polyglutamine, and prion proteins (Morris, A. M., et al. (2008) Biochemistry 47, 2413-2427; Watzky, M. A., et al. (2008) Biochemistry 47, 10790-10800). Herein we use the F-W model to reanalyze protein aggregation kinetic data obtained under the experimental conditions of variable temperature or pH 2.0 to 8.5. We provide the average nucleation (k(1)) and growth (k(2)) rate constants and correlations with variable temperature or varying pH for the protein alpha-synuclein. From the variable temperature data, activation parameters DeltaG(double dagger), DeltaH(double dagger), and DeltaS(double dagger) are provided for nucleation and growth, and those values are compared to the available parameters reported in the previous literature determined using an empirical method. Our activation parameters suggest that nucleation and growth are energetically similar for alpha-synuclein aggregation (DeltaG(double dagger)(nucleation)=23(3) kcal/mol; DeltaG(double dagger)(growth)=22(1) kcal/mol at 37 degrees C). From the variable pH data, the F-W analyses show a maximal k(1) value at pH approximately 3, as well as minimal k(1) near the isoelectric point (pI) of alpha-synuclein. Since solubility and net charge are minimized at the pI, either or both of these factors may be important in determining the kinetics of the nucleation step. On the other hand, the k(2) values increase with decreasing pH (i.e., do not appear to have a minimum or maximum near the pI) which, when combined with the k(1) vs. pH (and pI) data, suggest that solubility and charge are less important factors for growth, and that charge is important in the k(1), nucleation step of alpha-synuclein. The chemically well-defined nucleation (k(1)) rate constants obtained from the F-W analysis are, as expected, different than the 1/lag-time empirical constants previously obtained. However, k(2)x[A](0) (where k(2) is the rate constant for autocatalytic growth and [A](0) is the initial protein concentration) is related to the empirical constant, k(app) obtained previously. Overall, the average nucleation and average growth rate constants for alpha-synuclein aggregation as a function of pH and variable temperature have been quantitated. Those values support the previously suggested formation of a partially folded intermediate that promotes aggregation under high temperature or acidic conditions.

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Format
SBML (L2V4)
Related Publication
  • Alpha-synuclein aggregation variable temperature and variable pH kinetic data: a re-analysis using the Finke-Watzky 2-step model of nucleation and autocatalytic growth.
  • Morris AM, Finke RG
  • Biophysical chemistry , 3/ 2009 , Volume 140 , Issue 1-3 , pages: 9-15
  • Department of Chemistry, Colorado State University, Fort Collins, CO 80523, USA.
  • The aggregation of proteins is believed to be intimately connected to many neurodegenerative disorders. We recently reported an "Ockham's razor"/minimalistic approach to analyze the kinetic data of protein aggregation using the Finke-Watzky (F-W) 2-step model of nucleation (A-->B, rate constant k(1)) and autocatalytic growth (A+B-->2B, rate constant k(2)). With that kinetic model we have analyzed 41 representative protein aggregation data sets in two recent publications, including amyloid beta, alpha-synuclein, polyglutamine, and prion proteins (Morris, A. M., et al. (2008) Biochemistry 47, 2413-2427; Watzky, M. A., et al. (2008) Biochemistry 47, 10790-10800). Herein we use the F-W model to reanalyze protein aggregation kinetic data obtained under the experimental conditions of variable temperature or pH 2.0 to 8.5. We provide the average nucleation (k(1)) and growth (k(2)) rate constants and correlations with variable temperature or varying pH for the protein alpha-synuclein. From the variable temperature data, activation parameters DeltaG(double dagger), DeltaH(double dagger), and DeltaS(double dagger) are provided for nucleation and growth, and those values are compared to the available parameters reported in the previous literature determined using an empirical method. Our activation parameters suggest that nucleation and growth are energetically similar for alpha-synuclein aggregation (DeltaG(double dagger)(nucleation)=23(3) kcal/mol; DeltaG(double dagger)(growth)=22(1) kcal/mol at 37 degrees C). From the variable pH data, the F-W analyses show a maximal k(1) value at pH approximately 3, as well as minimal k(1) near the isoelectric point (pI) of alpha-synuclein. Since solubility and net charge are minimized at the pI, either or both of these factors may be important in determining the kinetics of the nucleation step. On the other hand, the k(2) values increase with decreasing pH (i.e., do not appear to have a minimum or maximum near the pI) which, when combined with the k(1) vs. pH (and pI) data, suggest that solubility and charge are less important factors for growth, and that charge is important in the k(1), nucleation step of alpha-synuclein. The chemically well-defined nucleation (k(1)) rate constants obtained from the F-W analysis are, as expected, different than the 1/lag-time empirical constants previously obtained. However, k(2)x[A](0) (where k(2) is the rate constant for autocatalytic growth and [A](0) is the initial protein concentration) is related to the empirical constant, k(app) obtained previously. Overall, the average nucleation and average growth rate constants for alpha-synuclein aggregation as a function of pH and variable temperature have been quantitated. Those values support the previously suggested formation of a partially folded intermediate that promotes aggregation under high temperature or acidic conditions.
Contributors
administrator, Audald Lloret i Villas

Metadata information

is
BioModels Database MODEL1501160001
BioModels Database BIOMD0000000566
isDescribedBy
PubMed 19101068
hasTaxon
Taxonomy Homo sapiens
isVersionOf
Gene Ontology amyloid fibril formation
isDerivedFrom
ja9705102
PubMed 11152691
BioModels Database BIOMD0000000567
hasProperty
Human Disease Ontology Parkinson's disease
Curation status
Curated
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  • Model originally submitted by : administrator
  • Submitted: Jan 16, 2015 3:32:39 PM
  • Last Modified: Jul 2, 2018 6:26:33 PM
Revisions
  • Version: 3 public model Download this version
    • Submitted on: Jul 2, 2018 6:26:33 PM
    • Submitted by: administrator
    • With comment: make model name in the file consistent with the database
  • Version: 2 public model Download this version
    • Submitted on: Apr 8, 2016 6:52:39 PM
    • Submitted by: Audald Lloret i Villas
    • With comment: Current version of Morris2008 - Fitting protein aggregation data via F-W 2-step mechanism
  • Version: 1 public model Download this version
    • Submitted on: Jan 16, 2015 3:32:39 PM
    • Submitted by: Audald Lloret i Villas
    • With comment: Original import of Morris2008 - Fitting protein aggregation data via F-W 2-step mechanism
Curator's comment:
(added: 23 Jan 2015, 16:26:04, updated: 23 Jan 2015, 16:26:04)
Figure 1 and Figure 3 of the reference publication have been reproduced here. α-synuclein aggregation from the literature is fitted over time by the Finke-Watze 2-step model. - Figure 1 (left). α-synuclein aggregation at 37 °C - Figure 3 (right). α-synuclein aggregation at pH 5,82 Note: rate constants k2 have been defined using Parameter Scan in order to match the simulation with the figures in the reference publication, as the existing ones in the publication do not fit as reported. Simulated values are 9.5e-7 on Figure 1 and 1.57e-6 on Figure 3. The simulation was done using Copasi v4.14 (Build 89) and the plots were generated using Gnuplot. The Copasi file of the model with simulation settings can be downloaded from the below link: