Literature DB >> 15993421

A toy model for predicting the rate of amyloid formation from unfolded protein.

Damien Hall1, Nami Hirota, Christopher M Dobson.   

Abstract

We develop a toy model for predicting the rate of amyloid formation from an unfolded polypeptide. The model assumes irreversible amyloid growth, employs a collision encounter scheme and uses a Gaussian chain approximation to describe the polypeptide sequence. A principal feature of the model is its dependence on a number of key sequence residues whose correct placement, geometric arrangement and orientation in relation to their interacting partners define the success, or otherwise, of the amyloid formation reaction. Although not realistic at the molecular level, the model captures some essential features of the system and is therefore useful from a heuristic standpoint. For the case of amyloid formation from an unstructured state, the model suggests that the major determinants of the rate of fibril formation are the length of the sequence separating the critical amino acids promoting amyloid formation and the positional placement of the critical residues within the sequence. Our findings suggest also that the sequence distance between the key interacting amino acid residues may play a role in defining the maximum width of a fibril and that the addition of non-interacting segments of long structure-less polypeptide chain to an amyloidogenic peptide may act to inhibit fibril formation. We discuss these findings with reference to the placement of critical sequence residues within the polypeptide chain, the design of polypeptides with lower amyloid formation propensities and the development of aggregation inhibitors as potential therapeutics for protein depositional disorders.

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Year:  2005        PMID: 15993421     DOI: 10.1016/j.jmb.2005.05.013

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  19 in total

1.  Role of zinc in human islet amyloid polypeptide aggregation.

Authors:  Jeffrey R Brender; Kevin Hartman; Ravi Prakash Reddy Nanga; Nataliya Popovych; Roberto de la Salud Bea; Subramanian Vivekanandan; E Neil G Marsh; Ayyalusamy Ramamoorthy
Journal:  J Am Chem Soc       Date:  2010-07-07       Impact factor: 15.419

2.  Membrane disruption and early events in the aggregation of the diabetes related peptide IAPP from a molecular perspective.

Authors:  Jeffrey R Brender; Samer Salamekh; Ayyalusamy Ramamoorthy
Journal:  Acc Chem Res       Date:  2011-09-25       Impact factor: 22.384

3.  Kinetics and thermodynamics of amyloid formation from direct measurements of fluctuations in fibril mass.

Authors:  Tuomas P J Knowles; Wenmiao Shu; Glyn L Devlin; Sarah Meehan; Stefan Auer; Christopher M Dobson; Mark E Welland
Journal:  Proc Natl Acad Sci U S A       Date:  2007-05-31       Impact factor: 11.205

4.  The distribution of residues in a polypeptide sequence is a determinant of aggregation optimized by evolution.

Authors:  Elodie Monsellier; Matteo Ramazzotti; Patrizia Polverino de Laureto; Gian-Gaetano Tartaglia; Niccolò Taddei; Angelo Fontana; Michele Vendruscolo; Fabrizio Chiti
Journal:  Biophys J       Date:  2007-08-31       Impact factor: 4.033

5.  A multi-pathway perspective on protein aggregation: implications for control of the rate and extent of amyloid formation.

Authors:  Damien Hall; József Kardos; Herman Edskes; John A Carver; Yuji Goto
Journal:  FEBS Lett       Date:  2015-01-31       Impact factor: 4.124

6.  Annular structures as intermediates in fibril formation of Alzheimer Abeta17-42.

Authors:  Jie Zheng; Hyunbum Jang; Buyong Ma; Ruth Nussinov
Journal:  J Phys Chem B       Date:  2008-05-06       Impact factor: 2.991

7.  What drives amyloid molecules to assemble into oligomers and fibrils?

Authors:  Jeremy D Schmit; Kingshuk Ghosh; Ken Dill
Journal:  Biophys J       Date:  2011-01-19       Impact factor: 4.033

8.  Accounting for protein-solvent contacts facilitates design of nonaggregating lattice proteins.

Authors:  Sanne Abeln; Daan Frenkel
Journal:  Biophys J       Date:  2011-02-02       Impact factor: 4.033

9.  The amyloidogenic SEVI precursor, PAP248-286, is highly unfolded in solution despite an underlying helical tendency.

Authors:  Jeffrey R Brender; Ravi Prakash Reddy Nanga; Nataliya Popovych; Ronald Soong; Peter M Macdonald; Ayyalusamy Ramamoorthy
Journal:  Biochim Biophys Acta       Date:  2011-01-22

10.  Computational modeling of the relationship between amyloid and disease.

Authors:  Damien Hall; Herman Edskes
Journal:  Biophys Rev       Date:  2012-09
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