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Dévelopement d'une méthode bio-informatique pour la prédiction des régions amyloidogéniques dans les protéines.

Abstract : A broad range of human diseases are linked to the formation of insoluble, fibrous, protein aggregates called amyloid fibrils. They include, but are not limited to, type II diabetes, rheumatoid arthritis, and perhaps most importantly, debilitating neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and Huntington's disease. There currently exists no cure, and no means of early diagnosis for any of these diseases. Numerous studies have shown that the ability to form amyloid fibrils is an inherent property of the polypeptide chain. This has lead to the development of a number of computational approaches to predict amyloidogenicity by amino acid sequences. Although these methods perform well against short peptides (about 6 residues), they generate an unsatisfactory high number of false positives when tested against longer sequences of the disease-related peptides and proteins. The main objective of this thesis was to develop an improved bioinformatics based approach to predict amyloidogenic regions from protein sequence.Recently new experimental techniques have shed light on the structure of amyloids showing that the core element of a majority of disease-related amyloid fibrils is a columnar structure (β—arcade) produced by stacking of β-strand-loop-β-strand motifs called “β-arches”. Using this structural insight, we have created a bioinformatics based approach to predict amyloidogenic regions from protein sequence information. Data from the analysis of the known and modeled β-arcade structures was incorporated into a rule based algorithm implemented in the Java programming language to create the ArchCandy program. Testing it against a set of protein and peptide sequences known to be related to diseases has shown that it correctly predicts most of these sequences and a number of mutated sequences related to the familial diseases. In addition to the prediction of regions with high amyloidogenic potential, a structural arrangement of the amyloid fibril is also suggested for each prediction. As whole genome sequencing becomes cheaper, our method provides opportunity to create individual risk profiles for the neurodegenerative, age-related and other diseases ushering in an era of personalized medicine.
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Abdullah Ahmed. Dévelopement d'une méthode bio-informatique pour la prédiction des régions amyloidogéniques dans les protéines.. Sciences agricoles. Université Montpellier II - Sciences et Techniques du Languedoc, 2013. Français. ⟨NNT : 2013MON20051⟩. ⟨tel-00998437⟩

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