A computational approach for identifying the chemical factors involved in the glycosaminoglycans-mediated acceleration of amyloid fibril formation

• Published in: PloS one 2010-06, Vol.5 (6), p.e11363-e11363
• Main Authors: Monsellier, Elodie, Ramazzotti, Matteo, Taddei, Niccolò, Chiti, Fabrizio
• Format: Article
• Language: English
• Subjects: Acceleration; Alzheimer's disease; Amyloid; Amyloidosis; Analysis; Anticoagulants; Biochemistry; Biochemistry, Molecular Biology; Biochemistry/Bioinformatics; Biophysics; Biophysics/Protein Folding; Chondroitin sulfate; Computation; Computational Biology/Literature Analysis; Computer applications; Concentration (composition); Diabetes mellitus; Fibrillogenesis; Glycosaminoglycans; Glycosaminoglycans - metabolism; Heparan sulfate; Life Sciences; Mathematical analysis; Molecular structure; Multivariate Analysis; Neurodegenerative diseases; Neurological Disorders; Neurological Disorders/Alzheimer Disease; Peptides; Protein interaction; Proteins; Proteins - metabolism; Salts; Studies; Sulfation; β-Amyloid
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0011363

Amyloid fibril formation is the hallmark of many human diseases, including Alzheimer's disease, type II diabetes and amyloidosis. Amyloid fibrils deposit in the extracellular space and generally co-localize with the glycosaminoglycans (GAGs) of the basement membrane. GAGs have been shown to accelerate the formation of amyloid fibrils in vitro for a number of protein systems. The high number of data accumulated so far has created the grounds for the construction of a database on the effects of a number of GAGs on different proteins. In this study, we have constructed such a database and have used a computational approach that uses a combination of single parameter and multivariate analyses to identify the main chemical factors that determine the GAG-induced acceleration of amyloid formation. We show that the GAG accelerating effect is mainly governed by three parameters that account for three-fourths of the observed experimental variability: the GAG sulfation state, the solute molarity, and the ratio of protein and GAG molar concentrations. We then combined these three parameters into a single equation that predicts, with reasonable accuracy, the acceleration provided by a given GAG in a given condition. In addition to shedding light on the chemical determinants of the protein:GAG interaction and to providing a novel mathematical predictive tool, our findings highlight the possibility that GAGs may not have such an accelerating effect on protein aggregation under the conditions existing in the basement membrane, given the values of salt molarity and protein:GAG molar ratio existing under such conditions.