Résumé :
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The MS2PH project (from Structural Mutation to Human Pathologies Phenotypes) aims at investigating how mutations impact protein structures and to which extent this change could affect pathological phenotypes. This project is part of the Decrypthon program (www.decrypthon.fr) where we have implemented the MS2PH-db relational database (http://ms2phdb-pbil.ibcp.fr/). This database focuses on 1915 proteins implicated in human monogenic diseases, including 70 proteins directly implicated in various muscular dystrophies, and offers access to mined, predicted and pre-computed data. MS2PH-db provides instant access to three types of data for each entry protein: - an evolutionary overview through pre-computed hierarchized multiple alignments of complete sequences, - a structural view through an automatically generated three-dimensional model of the protein, - an additional layer of information composed of structural and functional annotations, as well as mutations and phenotypic data related to the pathology. These three views are interconnected through a user-friendly graphical interface which allows an interactive approach to study the combination of structural, functional and clinical data within the framework of the evolutionary relevance of the sequence information. Using this tool, we have studied the distribution of all mapped mutations within the 1915 MS2PH proteins with respect to known functional domains. We observed that roughly 78% of the mutated residues associated with a phenotypical impact are located in close or medium proximity of a conserved residue. In the frame of the newly developed Decrypthon Data Center, we are currently integrating the structural and evolutionary information with interactomic data, using protein interactions data provided by complementary sources: experimental interaction data generated in the Muscular Interactome project, functional and physical interactions mined from the STRING database and interacting interface predictions. The preliminary results obtained with this integrative approach are showing the way to the establishment of a system dedicated to the prediction of the link between mutations and clinical phenotypes.
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