Literature DB >> 23638987

A kinetic study of amyloid formation: fibril growth and length distributions.

John S Schreck1, Jian-Min Yuan.   

Abstract

We propose a kinetic model for the self-aggregation by amyloid proteins. By extending several well-known models for protein aggregation, the time evolution of aggregate concentrations containing r proteins, denoted c(r)(t), can be written in terms of generalized Smoluchowski kinetics. With this approach, we take into account all possible aggregation and fragmentation reactions involving clusters of any size. Correspondingly, an aggregate of size x + y could be formed by or break up into two smaller constituent aggregates of sizes x and y. The rates of each aggregation or fragmentation reaction, called kernels, are specified in terms of the aggregate size, and we solve c(r)(t) for large cluster sizes using numerical techniques. We show that by using Smoluchowski kinetics many pathways to fibrillation are possible and quantities, such as the aggregate length distribution at an arbitrary time, can be calculated. We show that the predicted results of the model are in agreement with the experimental observations.

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Year:  2013        PMID: 23638987     DOI: 10.1021/jp401586p

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


  12 in total

1.  Protein Polymerization into Fibrils from the Viewpoint of Nucleation Theory.

Authors:  Dimo Kashchiev
Journal:  Biophys J       Date:  2015-11-17       Impact factor: 4.033

2.  A Kinetic Model for Cell Damage Caused by Oligomer Formation.

Authors:  Liu Hong; Ya-Jing Huang; Wen-An Yong
Journal:  Biophys J       Date:  2015-10-06       Impact factor: 4.033

3.  Inferring Mechanistic Parameters from Amyloid Formation Kinetics by Approximate Bayesian Computation.

Authors:  Eri Nakatani-Webster; Abhinav Nath
Journal:  Biophys J       Date:  2017-03-14       Impact factor: 4.033

4.  pH responsiveness of fibrous assemblies of repeat-sequence amphipathic α-helix polypeptides.

Authors:  Toshiaki Takei; Kouhei Tsumoto; Atsuhito Okonogi; Akiko Kimura; Shuichi Kojima; Kazumori Yazaki; Tsunetomo Takei; Takuya Ueda; Kin-ichiro Miura
Journal:  Protein Sci       Date:  2015-04-02       Impact factor: 6.725

5.  Comparing the Aggregation Free Energy Landscapes of Amyloid Beta(1-42) and Amyloid Beta(1-40).

Authors:  Weihua Zheng; Min-Yeh Tsai; Peter G Wolynes
Journal:  J Am Chem Soc       Date:  2017-11-07       Impact factor: 15.419

6.  Polymer-Peptide Conjugates Convert Amyloid into Protein Nanobundles through Fragmentation and Lateral Association.

Authors:  John W Smith; Xing Jiang; Hyosung An; Alexander M Barclay; Giuseppe Licari; Emad Tajkhorshid; Edwin G Moore; Chad M Rienstra; Jeffrey S Moore; Qian Chen
Journal:  ACS Appl Nano Mater       Date:  2019-09-10

7.  Numerical Simulations Reveal Randomness of Cu(II) Induced Aβ Peptide Dimerization under Conditions Present in Glutamatergic Synapses.

Authors:  Wojciech Goch; Wojciech Bal
Journal:  PLoS One       Date:  2017-01-26       Impact factor: 3.240

8.  Cyclic undecapeptide Cyclosporin A mediated inhibition of amyloid synthesis: Implications in alleviation of amyloid induced neurotoxicity.

Authors:  Shadab Kazmi; Anzar Abdul Mujeeb; Mohammad Owais
Journal:  Sci Rep       Date:  2018-11-23       Impact factor: 4.379

Review 9.  Statistical mechanical treatments of protein amyloid formation.

Authors:  John S Schreck; Jian-Min Yuan
Journal:  Int J Mol Sci       Date:  2013-08-23       Impact factor: 5.923

10.  Minimal model of self-assembly: emergence of diversity and complexity.

Authors:  Bogdan Barz; Brigita Urbanc
Journal:  J Phys Chem B       Date:  2014-03-06       Impact factor: 2.991

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