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Alzheimers Dement. 2015 Jun;11(6):681-90. doi: 10.1016/j.jalz.2014.06.007. Epub 2014 Aug 20.

A structure-based approach to predict predisposition to amyloidosis.

Author information

1
Centre de Recherches de Biochimie Macromoléculaire, UMR5237, CNRS, Université Montpellier 1 et 2, Montpellier, France; Department of Biosciences, COMSATS Institute of Information Technology, Islamabad, Pakistan.
2
Centre de Recherches de Biochimie Macromoléculaire, UMR5237, CNRS, Université Montpellier 1 et 2, Montpellier, France; Institut de Biologie Computationnelle, Montpellier, France.
3
Centre de Recherches de Biochimie Macromoléculaire, UMR5237, CNRS, Université Montpellier 1 et 2, Montpellier, France; UFR des Sciences Pharmaceutiques et Biologiques, Université Montpellier 1, Montpellier, France.
4
Centre de Recherches de Biochimie Macromoléculaire, UMR5237, CNRS, Université Montpellier 1 et 2, Montpellier, France; Institut de Biologie Computationnelle, Montpellier, France. Electronic address: andrey.kajava@crbm.cnrs.fr.

Abstract

BACKGROUND:

Neurodegenerative diseases and other amyloidoses are linked to the formation of amyloid fibrils. It has been shown that the ability to form these fibrils is coded by the amino acid sequence. Existing methods for the prediction of amyloidogenicity generate an unsatisfactory high number of false positives when tested against sequences of the disease-related proteins.

METHODS:

Recently, it has been shown that the three-dimensional structure of a majority of disease-related amyloid fibrils contains a β-strand-loop-β-strand motif called β-arch. Using this information, we have developed a novel bioinformatics approach for the prediction of amyloidogenicity.

RESULTS:

The benchmark results show the superior performance of our method over the existing programs.

CONCLUSIONS:

As genome sequencing becomes more affordable, our method provides an opportunity to create individual risk profiles for the neurodegenerative, age-related, and other diseases ushering in an era of personalized medicine. It will also be used in the large-scale analysis of proteomes to find new amyloidogenic proteins.

KEYWORDS:

3D structure; Amyloid fibrils; Computational approaches; Dementia; Early diagnosis; Human diseases; Personalized medicine; Proteomes; Sequence analysis

PMID:
25150734
DOI:
10.1016/j.jalz.2014.06.007
[Indexed for MEDLINE]

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