Literature DB >> 28129689

The Levinthal Problem in Amyloid Aggregation: Sampling of a Flat Reaction Space.

Zhiguang Jia1, Alex Beugelsdijk1, Jianhan Chen1, Jeremy D Schmit1.   

Abstract

The formation of amyloid fibrils has been associated with many neurodegenerative disorders, yet the mechanism of aggregation remains elusive, partly because aggregation time scales are too long to probe with atomistic simulations. A microscopic theory of fibril elongation was recently developed that could recapitulate experimental results with respect to the effects of temperature, denaturants, and protein concentration on fibril growth kinetics (Schmit, J. D. J. Chem. Phys. 2013, 138 (18), 185102). The theory identifies the conformational search over H-bonding states as the slowest step in the aggregation process and suggests that this search can be efficiently modeled as a random walk on a rugged one-dimensional energy landscape. This insight motivated the multiscale computational algorithm for simulating fibril growth presented in this paper. Briefly, a large number of short atomistic simulations are performed to compute the system diffusion tensor in the reaction coordinate space predicted by the analytic theory. Ensemble aggregation pathways and growth kinetics are then computed from Markov state model (MSM) trajectories. The algorithm is deployed here to understand the fibril growth mechanism and kinetics of Aβ16-22 and three of its mutants. The order of growth rates of the wild-type and two single mutation peptides (CHA19 and CHA20) predicted by the MSM trajectories is consistent with experimental results. The simulation also correctly predicts that the double mutation (CHA19/CHA20) would reduce the fibril growth rate, even though the degree of rate reduction with respect to either single mutation is overestimated. This artifact may be attributed to the simplistic implicit solvent model. These trends in the growth rate are not apparent from inspection of the rate constants of individual bonds or the lifetimes of the mis-registered states that are the primary kinetic traps but only emerge in the ensemble of trajectories generated by the MSM.

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Year:  2017        PMID: 28129689      PMCID: PMC5573152          DOI: 10.1021/acs.jpcb.7b00253

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  48 in total

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Journal:  Annu Rev Phys Chem       Date:  2011       Impact factor: 12.703

2.  EMMA: A Software Package for Markov Model Building and Analysis.

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Journal:  J Chem Theory Comput       Date:  2012-06-18       Impact factor: 6.006

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Journal:  Proc Natl Acad Sci U S A       Date:  2004-11-08       Impact factor: 11.205

Review 4.  Everything you wanted to know about Markov State Models but were afraid to ask.

Authors:  Vijay S Pande; Kyle Beauchamp; Gregory R Bowman
Journal:  Methods       Date:  2010-06-04       Impact factor: 3.608

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Journal:  Science       Date:  2002-07-19       Impact factor: 47.728

8.  Molecular structure of β-amyloid fibrils in Alzheimer's disease brain tissue.

Authors:  Jun-Xia Lu; Wei Qiang; Wai-Ming Yau; Charles D Schwieters; Stephen C Meredith; Robert Tycko
Journal:  Cell       Date:  2013-09-12       Impact factor: 41.582

9.  A kinetic approach to the sequence-aggregation relationship in disease-related protein assembly.

Authors:  Bogdan Barz; David J Wales; Birgit Strodel
Journal:  J Phys Chem B       Date:  2014-01-17       Impact factor: 2.991

10.  A generic mechanism of emergence of amyloid protofilaments from disordered oligomeric aggregates.

Authors:  Stefan Auer; Filip Meersman; Christopher M Dobson; Michele Vendruscolo
Journal:  PLoS Comput Biol       Date:  2008-11-14       Impact factor: 4.475

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  4 in total

1.  Amyloid assembly is dominated by misregistered kinetic traps on an unbiased energy landscape.

Authors:  Zhiguang Jia; Jeremy D Schmit; Jianhan Chen
Journal:  Proc Natl Acad Sci U S A       Date:  2020-04-28       Impact factor: 11.205

2.  Conformational entropy limits the transition from nucleation to elongation in amyloid aggregation.

Authors:  Tien M Phan; Jeremy D Schmit
Journal:  Biophys J       Date:  2022-07-01       Impact factor: 3.699

3.  Theory of Sequence Effects in Amyloid Aggregation.

Authors:  Caleb Huang; Elaheh Ghanati; Jeremy D Schmit
Journal:  J Phys Chem B       Date:  2018-03-09       Impact factor: 2.991

4.  Insight Into Seeded Tau Fibril Growth From Molecular Dynamics Simulation of the Alzheimer's Disease Protofibril Core.

Authors:  Cass Leonard; Christian Phillips; James McCarty
Journal:  Front Mol Biosci       Date:  2021-03-19
  4 in total

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