De l'identification à la caractérisation des complexes protéiques : développement d'une plateforme bioinformatique d'analyse

Authors: Droit, Arnaud
Advisor: Poirier, Guy
Abstract: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. In the “post-genome” era, mass spectrometry (MS) has become an important method for the analysis of proteome data. One strategy to determine protein function is to identify protein–protein interactions. The rapid advances made in mass spectrometry in combination with other methods used in proteomics results in an increasing of proteomics projects. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore array of information to uncover biologically relevant interactions and pathways. Thus for protein interaction studies, there is clearly a need to develop a systematic and stepwise in silico approach that can predict potential interactors or are most likely to improve our understanding of how complex biological systems work. The focus of our laboratory is the study of the activity of poly(ADP-ribose) polymerases (PARPs) and their role in the cell. Poly(ADP-ribosylation) is a post-synthetic protein modification consisting of long chains of poly(ADP-ribose) (pADPr) synthesized by PARPs at the expense of NAD+. The overall objective of this research is to extensively characterize the dynamic roles of poly(ADP-ribosyl)ation in response to cellular stresses that cause DNA damage. Our approach utilizes immunoprecipitation and affinity purification followed by mass spectrometry identification of associated proteins. One part of this thesis projet is to develop the architecture and major features of a web-based utility tool, which is designed to rationally organize protein and peptide data generated by the tandem mass spectrometry. Next, we have performed benchmarking to optimize protein identification. The system will be expanded as needed in order to make the analysis more efficient. We have also explored the public database information for protein identification data mining. Using the described pipeline, we have successfully identified several interactions of biological significance between PARP and other proteins such as RFC1, 2, 3, 4, 5.
Document Type: Thèse de doctorat
Issue Date: 2007
Open Access Date: 12 April 2018
Permalink: http://hdl.handle.net/20.500.11794/19065
Grantor: Université Laval
Collection:Thèses et mémoires

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