title: A structure-based approach to predict predisposition to amyloidosis.
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.
authors.affiliation: 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.
authors.forename: Abdullah B
authors.initials: AB
authors.lastname: Ahmed
authors.affiliation: Centre de Recherches de Biochimie Macromoléculaire, UMR5237, CNRS, Université Montpellier 1 et 2, Montpellier, France; Institut de Biologie Computationnelle, Montpellier, France.
authors.forename: Nadia
authors.initials: N
authors.lastname: Znassi
authors.affiliation: 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.
authors.forename: Marie-Thérèse
authors.initials: MT
authors.lastname: Château
authors.affiliation: 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.
authors.forename: Andrey V
authors.initials: AV
authors.lastname: Kajava
mesh_terms: D000682:Amyloid / Q000235:genetics / Q000378:metabolism*; D016229:Amyloid beta-Peptides / Q000235:genetics / Q000378:metabolism; D000686:Amyloidosis / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism*; D003198:Computer Simulation; D042241:Early Diagnosis; D005656:Fungal Proteins / Q000235:genetics / Q000378:metabolism; D006801:Humans; D009154:Mutation; D019906:Nuclear Magnetic Resonance, Biomolecular; D010446:Peptide Fragments / Q000235:genetics / Q000378:metabolism; D011487:Protein Conformation; D012372:ROC Curve; D012441:Saccharomyces cerevisiae; D017421:Sequence Analysis / Q000379:methods*
chemical_list: D000682:Amyloid; D016229:Amyloid beta-Peptides; D005656:Fungal Proteins; D010446:Peptide Fragments; C075222:amyloid beta-protein (1-42)
keywords: 3D structure; Amyloid fibrils; Computational approaches; Dementia; Early diagnosis; Human diseases; Personalized medicine; Proteomes; Sequence analysis