Letteratura scientifica selezionata sul tema "Interactions protéine-protéine – Identification"
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Tesi sul tema "Interactions protéine-protéine – Identification":
Moine-Franel, Alexandra. "Cartographie des poches aux interfaces protéine-protéine et identification de nouvelles cibles thérapeutiques potentielles". Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS634.pdf.
Protein-protein interactions (PPIs) constitute a significant source of potential therapeutic targets because they play a crucial role in numerous and diverse biological processes, including the development of pathologies. While PPIs appear as promising therapeutic targets, they are more challenging to study than conventional therapeutic targets. Indeed, known PPIs are characterized by specific structural motifs that limit their ‘druggability’, meaning their ability to bind to and be modulated by a small drug molecule. However, the growing identification of small molecules modulating various PPIs demonstrates that, with an appropriate methodology, they can represent a class of novel and innovative therapeutic targets. The objective is, therefore, to develop an in silico protocol to aid in identifying new therapeutic targets involving PPIs by rationalizing the key elements that determine the ‘druggability’ of the interaction
Douguet, Dominique. "Etude des interactions protéine-protéine et protéine-ligand par bio- et chimie-informatique structurale : Identification de petites molécules bio-actives". Habilitation à diriger des recherches, Université de Nice Sophia-Antipolis, 2007. http://tel.archives-ouvertes.fr/tel-00320089.
La modélisation par homologie permet d'obtenir un modèle tridimensionnel d'une protéine lorsque sa structure n'a pas été déterminée expérimentalement. Ma contribution dans ce domaine fut la réalisation du serveur @TOME avec le soutien de la GENOPOLE Languedoc-Roussillon (accessible à l'adresse http://bioserver.cbs.cnrs.fr). Ce serveur était le premier de ce type à avoir été développé en France. Le serveur @TOME rassemble et traite d'une manière automatique toutes les étapes nécessaires à la construction d'un modèle 3D d'une protéine. Cela inclut la reconnaissance du repliement, la construction des modèles protéiques et leur évaluation. Les résultats du CASP5 en 2005 (session internationale d'évaluation des méthodes de prédiction de la structure des protéines ; http://predictioncenter.llnl.gov/) ont montré que notre serveur utilisé en mode automatique propose des modèles très proches de la structure expérimentale lorsque l'identité de séquence avec la structure support est supérieure à 30%. Le serveur a été classé 26ième sur 187 groupes inscrits.
Dans un second temps, mes recherches m'ont permis de réaliser une base de données de complexes protéiques co-cristallisés, base fondatrice du projet DOCKGROUND. Ce projet de grande envergure, soutenu par le NIH depuis 2005, vise à établir un système intégré et dynamique de bases de données dédié à l'étude et à la prédiction des interactions entre protéines et permettre ainsi d'améliorer nos connaissances des interactions et de développer des outils de prédiction plus fiables. Ce travail a été effectué au sein de l'équipe du Pr. Ilya Vakser à l'Université de Stony Brook, NY, USA. Dans la réalisation de cette première base de données, un ensemble de programmes collectent, classent et annotent les complexes protéiques qui ont été co-cristallisés (données sur la séquence, la fonction, le repliement 3D, les particularités telles qu'une fixation à de l'ADN, ...). Ensuite, j'ai mis en œuvre une sélection dynamique des représentants des complexes contenus dans cette base. Les représentants sont essentiels pour éviter une surreprésentation de certaines familles de protéines. Cette base de donnée est accessible par Internet et est régulièrement mise à jour (http://dockground.bioinformatics.ku.edu). Le projet DOCKGROUND va être poursuivi par la réalisation de 3 autres bases de données qui s'ancreront sur la présente appelée ‘Bound-Bound'.
L'objectif principal de mes travaux est d'identifier de nouveaux composés bio-actifs afin de comprendre le fonctionnement de leur cible dans un contexte biologique. Les méthodes que j'utilise se basent sur la chémoinformatique, le criblage virtuel et le de novo ‘drug design'. Dans le cadre de ce dernier, j'ai mis au point un programme propriétaire LEA3D (‘Ligand by Evolutionary Algorithm' 3D). Le programme génère des petites molécules à partir de la combinaison de fragments moléculaires issus de drogues et de molécules ‘bio' (substrats ou produits de réactions enzymatiques). Le criblage virtuel basé sur la structure protéique et le de novo ‘drug design' par LEA3D, ont été appliqués avec succès à la thymidine monophosphate kinase (TMPK) de Mycobacterium tuberculosis dans le cadre d'une collaboration avec une équipe de chimistes et de biologistes de l'Institut Pasteur. De nouvelles familles d'inhibiteurs ont été identifiées dont un inhibiteur synthétique trois fois plus affin que le substrat naturel. Plusieurs publications et une demande de brevet couvrent les résultats de ces recherches. Dans la continuité de ces travaux, je m'intéresse maintenant, plus particulièrement, à développer des stratégies de criblages de fragments (molécules de petit poids moléculaire). Il a été montré que de petites chimiothèques contenant des petites molécules polaires sont plus efficaces pour identifier des touches. Ce travail doit être réalisé conjointement avec des criblages structuraux expérimentaux comme la RMN ou la diffraction des rayons X. Ces derniers se posent comme une alternative aux tests in vitro avec pour avantage de donner une information détaillée, au niveau atomique, des interactions entre le ligand et sa cible. S'ensuit une étape d'optimisation/maturation des touches en ligands plus élaborés et plus affins par l'utilisation d'outils de chémoinformatique.
Corsi, Flavia. "Towards the in silico reconstruction of protein interaction networks : identification of DNA- and RNA-protein interfaces, and construction of a database of multiple interactions of proteins". Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS452.pdf.
This thesis focuses on the characterization and prediction of DNA- and RNA-binding sites on protein structures, with some comparisons with protein-protein ones. We compiled and manually curated a non-redundant and representative set of 187 high resolution protein-DNA complexes, with the available 82 protein unbound conformations, that could be used as a reference benchmark. We conducted a comprehensive analysis of sequence- and structure-based properties of protein-DNA/RNA interfaces and compared them with respect to protein-protein interfaces and to non-interacting protein regions. We developed JET2DNA and JET2RNA, new methods for predicting DNA- and RNA-binding sites on protein surfaces. Combining four biologically meaningful descriptors, they outperform other machine-learning methods, in terms of predictive power and robustness to conformational changes. Our tools demonstrated to be instrumental in discovering alternative DNA/RNA-binding sites and in deciphering their properties. This could be very helpful for drug design and repurposing. To give a comprehensive view of plasticity of DNA-binding proteins and structural information on their multiple interactions, we constructed the Protein-(Protein)-DNA database (P(P)DNAdb). It comprises the 187 protein-DNA complexes in our benchmark, protein unbound forms and structures of other complexes where the proteins, or closed homologs, were in contact with other proteins. The user can access properties of the interfaces, visualize conformational changes associated to the binding of different partners and the location of the DNA-binding residues on the unbound structures and on the complexes with the other protein partners
Fradin, Aurelie. "Identification des modifications post-traductionnelles d'Ilf3 (Interleukin enhancer binding factor 3) et de NF90 (Nuclear Factor 90) et étude de leur rôle(s) fonctionnel(s)". Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066222/document.
Ilf3 and NF90, two double stranded RNA-binding proteins, are generated by exclusive splicing from the ILF3 gene. For each one, a 5? alternative splicing leads to the synthesis of a long and a short isoforms that differ by the presence or not of 13 amino acid sequence at their N-terminus corresponding to a nucleolar localization signal. The characteristic of these two proteins is to exhibit a high degree of heterogeneity with at least 20 isoforms produced from the same gene, 12 for Ilf3 and 8 for NF90. It is generated by two complementary mechanisms, alternative splicing and posttranslational modifications which two have been identified in the laboratory, the arginine 609/622 asymmetric dimethylation present in a RGG consensus sequence and catalyzed by PRMT1 ("protein arginine N-methyltransferase 1") and the serine 190/203 phosphorylation. This polymorphism could explain the various cellular functions described for both proteins and could regulate their subcellular localization and the interaction with protein or nucleic partners. By immunofluorescence and GST pull-down experiments, it was shown that these two posttranslational modifications of Ilf3 and NF90 neither seem involved in their subcellular localization, nor in the regulation of interactions with their protein partners. Because of the many functions associated with Ilf3 and NF90 proteins in the literature, the identified modifications may be implicated in regulating the interactions with their nucleic partners, DNA or RNA
Viard, Julia. "Identification et manipulation d’interactions protéines - protéines codées par des gènes impliqués dans des synaptopathies". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLE033.
Many neurodevelopmental diseases have a complex genetic architecture involving several deregulated genes and are characterized by synaptic perturbations. In order to explore how these various genetic alterations result in synaptic phenotypes, we analysed protein - protein interactions encoded by these different genes leading us to identify novel deregulated molecular pathways at the synapse.This work focussed on two different synaptopathies, the intellectual disability in Down syndrome and cognitive impairment and autism caused by the structural changes in the AUTS2 gene.First, we performed a large-scale study of the interactions of the proteins encoded by the chromosome 21, using yeast two-hybrid, and identified a network of synaptic protein which is enriched in genes involved in intellectual disability. We also highlighted nuclear interactions that are disrupted in a mouse model overexpressing DYRK1A and impair a NMDA-independent Long-Term Potentiation (LTP) synaptic plasticity phenotype.Then, we characterized the mechanistic of the gene dosage alteration of AUTS2 involved in autism and intellectual disability. We have identified a complex between AUTS2 and TTC3, the E3 ligase of AKT mediating the ubiquitination of Akt at the synapse. We managed to rescue the synaptic phenotype induced by the silencing of AUTS2 by injection of AKT and generated two mouse models displaying either duplication or deletion of the AUTS2 locus (~1Mb). These mouse models display synaptic defects.In conclusion, this work shows the relevance of studying protein interactions to understand the mechanistic consequences of multigene disturbances observed at the synapses in these diseases
Bachiri, Kamel. "Identification des interactions oncogéniques du Polyomavirus à cellules de Merkel". Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILS113.
Merkel cell carcinoma (MCC) is an extremely aggressive skin cancer with a high mortality rate. In 80% of cases, this cancer is linked to the presence of the Merkel cell polyomavirus which expresses two viral oncoproteins, small T and a truncated form of large T. The expression of these proteins is sufficient for carcinogenesis, inducing the disruption of cell cycle checkpoints, changes in the epigenetic profile, and immune evasion. The combination of interactomics and proteomics approaches allowed us to identify numerous epigenetic factors related to the T antigens. These studies have also highlighted potential mechanisms involving the regulation of protein stability, gene expression, and genetic stability via an altered DNA damage response pathway. The hypotheses formulated following these analyses were investigated in targeted assays. A link between the peptidyl-prolyl cis/trans isomerase and neddylation was demonstrated. We have thus identified a new potential mechanism for regulating the activity of ubiquitination complexes involving the recruitment of PIN1 to these complexes, thus their isomerization, by neddylation. The study of the EHMT2 functions, an epigenetic regulator of interest, revealed the importance of its role in the DNA damage response in virus positive MCC (VP-MCC). We were able to identify a protective role of tLT and EHMT2 in DNA damages in VP-MCC cells. More specifically, we also report a role of EHMT2 in solving single strand DNA breaks following replication stress. Our works have enabled the discovery of new important mechanisms in VP-MCC oncogenesis, reporting for the first time a central role of tLT in DNA damages management
Ben, Salah Iskandar. "Les mycobactéries du complexe Mycobacterium avium : identification et interactions avec les amibes libres". Aix-Marseille 2, 2009. http://www.theses.fr/2009AIX20682.
Verreman, Kathye. "Identification et caractérisation de nouveaux partenaires du facteur de transcription ERM". Thesis, Lille 1, 2009. http://www.theses.fr/2009LIL10057/document.
ERM is an ETS transcription factor which belongs to the PEA3 group and is involved in several processes such as migration and dissemination during organogenesis and cancer development. Regulation of its transcriptional activity requires post-translational modifications and interactions with partner proteins. In order to identify new ERM partners, we have developed various affinity chromatography techniques to isolate new potential partners. Among these candidates, CoAA (CoActivator Activator), MED23 and MED25 directly interact with ERM.MED23 and MED25 are subunits of the mediator. The mediator is a 30 sub-units multi-protein complex which mediates signals from transcription factors bound at upstream promoter elements or enhancers to RNA polymerase II and the general initiation factors bound at the core promoter. We found that MED23 and MED25 interact with ERM in vitro and in vivo and are required for transcriptional activation induced by ERM. However, these sub-units display various ability to recruit the mediator on ERM in vitro. The heterogeneous nuclear ribonucleoprotein-like protein CoAA regulates gene expression and RNA splicing. We demonstrated that ERM interacts in vitro and in vivo with CoAA. ERM transcriptional activity is enhanced upon CoAA overexpression and is decreased by CoAA knock-down. We demonstrated that CoAA modulates ERM transcriptional activity by decreasing sumoylated ERM levels. This work demonstrated new ways to regulate the activity of ERM and the two other PEA3 group members. The molecular mechanisms involved in the modulation of PEA3 member activity by these partners remain to be clarified
Michaux, Charlotte. "Identification et caractérisation fonctionnelle de petits ARN non codants chez Enterococcus faecalis et analyse d'une protéine "RNA-binding"". Caen, 2013. http://www.theses.fr/2013CAEN2094.
Tahir, Shifa. "A docking-based method for in silico epitope determination". Thesis, Tours, 2018. http://www.theses.fr/2018TOUR4008.
The development of therapeutic antibodies has been rapidly increasing in the last 10 years, with application to an increasing number of pathologies. The knowledge of the epitope, the region of the antigen to which the antibody binds, is crucial for understanding its functional effects. We have developed an in silico method, MAbTope, which allows the accurate prediction of the epitope, regardless of the availability of the 3D structure of the antibody of interest. This method is based on a protein-protein docking method previously developed in the BIOS group. The learning dataset was enlarged in antibody-antigen complexes, new specific scoring functions have been designed, and very importantly, the objective of machine-learning was switched from the conformational perspective towards the epitope determination perspective. We show that the resulting method allows robust and accurate prediction, whether or not the 3D structure of the antibody is available. We also show how the predictions can be easily exploited for experimental validation. Finally, we show how this method can be used for high-throughput epitope binning