Genome-wide association study identifies two loci strongly affecting transferrin glycosylation.

Polysaccharide sidechains attached to proteins play important roles in cell-cell and receptor-ligand interactions. Variation in the carbohydrate component has been extensively studied for the iron transport protein transferrin, because serum levels of the transferrin isoforms asialotransferrin + disialotransferrin (carbohydrate-deficient transferrin, CDT) are used as biomarkers of excessive alcohol intake. We conducted a genome-wide association study to assess whether genetic factors affect CDT concentration in serum. CDT was measured in three population-based studies: one in Switzerland (CoLaus study, n = 5181) and two in Australia (n = 1509, n = 775). The first cohort was used as the discovery panel and the latter ones served as replication. Genome-wide single-nucleotide polymorphism (SNP) typing data were used to identify loci with significant associations with CDT as a percentage of total transferrin (CDT%). The top three SNPs in the discovery panel (rs2749097 near PGM1 on chromosome 1, and missense polymorphisms rs1049296, rs1799899 in TF on chromosome 3) were successfully replicated , yielding genome-wide significant combined association with CDT% (P = 1.9 × 10(-9), 4 × 10(-39), 5.5 × 10(-43), respectively) and explain 5.8% of the variation in CDT%. These allelic effects are postulated to be caused by variation in availability of glucose-1-phosphate as a precursor of the glycan (PGM1), and variation in transferrin (TF) structure.

Crystal packing modifies ligand binding affinity: the case of aldose reductase.

The relationship between the structures of protein-ligand complexes existing in the crystal and in solution, essential in the case of fragment-based screening by X-ray crystallography (FBS-X), has been often an object of controversy. To address this question, simultaneous co-crystallization and soaking of two inhibitors with different ratios, Fidarestat (FID; K(d) = 6.5 nM) and IDD594 (594; K(d) = 61 nM), which bind to h-aldose reductase (AR), have been performed. The subatomic resolution of the crystal structures allows the differentiation of both inhibitors, even when the structures are almost superposed. We have determined the occupation ratio in solution by mass spectrometry (MS) Occ(FID)/Occ(594) = 2.7 and by X-ray crystallography Occ(FID)/Occ(594) = 0.6. The occupancies in the crystal and in solution differ 4.6 times, implying that ligand binding potency is influenced by crystal contacts. A structural analysis shows that the Loop A (residues 122-130), which is exposed to the solvent, is flexible in solution, and is involved in packing contacts within the crystal. Furthermore, inhibitor 594 contacts the base of Loop A, stabilizing it, while inhibitor FID does not. This is shown by the difference in B-factors of the Loop A between the AR-594 and AR-FID complexes. A stable loop diminishes the entropic energy barrier to binding, favoring 594 versus FID. Therefore, the effect of the crystal environment should be taken into consideration in the X-ray diffraction analysis of ligand binding to proteins. This conclusion highlights the need for additional methodologies in the case of FBS-X to validate this powerful screening technique, which is widely used.

Fatty acids, eicosanoids, and hypolipidemic agents identified as ligands of peroxisome proliferator-activated receptors by coactivator-dependent receptor ligand assay.

Peroxisome proliferator-activated receptors (PPARs) are nuclear hormone receptors controlling the expression of genes involved in lipid homeostasis. PPARs activate gene transcription in response to a variety of compounds including hypolipidemic drugs as well as natural fatty acids. From the plethora of PPAR activators, Scatchard analysis of receptor-ligand interactions has thus far identified only four ligands. These are the chemotactic agent leukotriene B4 and the hypolipidemic drug Wy 14,643 for the alpha-subtype and a prostaglandin J2 metabolite and synthetic antidiabetic thiazolidinediones for the gamma-subtype. Based on the hypothesis that ligand binding to PPAR would induce interactions of the receptor with transcriptional coactivators, we have developed a novel ligand sensor assay, termed coactivator-dependent receptor ligand assay (CARLA). With CARLA we have screened several natural and synthetic candidate ligands and have identified naturally occurring fatty acids and metabolites as well as hypolipidemic drugs as bona fide ligands of the three PPAR subtypes from Xenopus laevis. Our results suggest that PPARs, by their ability to interact with a number of structurally diverse compounds, have acquired unique ligand-binding properties among the superfamily of nuclear receptors that are compatible with their biological activity.

A protein interaction atlas for the nuclear receptors: properties and quality of a hub-based dimerisation network.

BACKGROUND: The nuclear receptors are a large family of eukaryotic transcription factors that constitute major pharmacological targets. They exert their combinatorial control through homotypic heterodimerisation. Elucidation of this dimerisation network is vital in order to understand the complex dynamics and potential cross-talk involved. RESULTS: Phylogeny, protein-protein interactions, protein-DNA interactions and gene expression data have been integrated to provide a comprehensive and up-to-date description of the topology and properties of the nuclear receptor interaction network in humans. We discriminate between DNA-binding and non-DNA-binding dimers, and provide a comprehensive interaction map, that identifies potential cross-talk between the various pathways of nuclear receptors. CONCLUSION: We infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources.

Role of CD8 in aberrant function of cytotoxic T lymphocytes.

Using H-2Kd-restricted photoprobe-specific cytotoxic T lymphocyte (CTL) clones, which permit assessment of T cell receptor (TCR)-ligand interactions by TCR photoaffinity labeling, we observed that the efficiency of antigen recognition by CTL was critically dependent on the half-life of TCR-ligand complexes. We show here that antigen recognition by CTL is essentially determined by the frequency of serial TCR engagement, except for very rapid dissociations, which resulted in aberrant TCR signaling and antagonism. Thus agonists that were efficiently recognized exhibited rapid TCR-ligand complex dissociation, and hence a high frequency of serial TCR engagement, whereas the opposite was true for weak agonists. Surprisingly, these differences were largely accounted for by the coreceptor CD8. While it was known that CD8 substantially decreases TCR-ligand complex dissociation, we observed in this study that this effect varied considerably among ligand variants, indicating that epitope modifications can alter the CD8 contribution to TCR-ligand binding, and hence the efficiency of antigen recognition by CTL.

EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization.

In recent years, protein-ligand docking has become a powerful tool for drug development. Although several approaches suitable for high throughput screening are available, there is a need for methods able to identify binding modes with high accuracy. This accuracy is essential to reliably compute the binding free energy of the ligand. Such methods are needed when the binding mode of lead compounds is not determined experimentally but is needed for structure-based lead optimization. We present here a new docking software, called EADock, that aims at this goal. It uses an hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 A around the center of mass of the ligand position in the crystal structure, and on the contrary to other benchmarks, our algorithm was fed with optimized ligand positions up to 10 A root mean square deviation (RMSD) from the crystal structure, excluding the latter. This validation illustrates the efficiency of our sampling strategy, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures could be explained by the presence of crystal contacts in the experimental structure. Finally, the ability of EADock to accurately predict binding modes on a real application was illustrated by the successful docking of the RGD cyclic pentapeptide on the alphaVbeta3 integrin, starting far away from the binding pocket.

EADock : design of a new molecular docking algorithm and some of its applications

3 Summary 3. 1 English The pharmaceutical industry has been facing several challenges during the last years, and the optimization of their drug discovery pipeline is believed to be the only viable solution. High-throughput techniques do participate actively to this optimization, especially when complemented by computational approaches aiming at rationalizing the enormous amount of information that they can produce. In siiico techniques, such as virtual screening or rational drug design, are now routinely used to guide drug discovery. Both heavily rely on the prediction of the molecular interaction (docking) occurring between drug-like molecules and a therapeutically relevant target. Several softwares are available to this end, but despite the very promising picture drawn in most benchmarks, they still hold several hidden weaknesses. As pointed out in several recent reviews, the docking problem is far from being solved, and there is now a need for methods able to identify binding modes with a high accuracy, which is essential to reliably compute the binding free energy of the ligand. This quantity is directly linked to its affinity and can be related to its biological activity. Accurate docking algorithms are thus critical for both the discovery and the rational optimization of new drugs. In this thesis, a new docking software aiming at this goal is presented, EADock. It uses a hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with .the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 R around the center of mass of the ligand position in the crystal structure, and conversely to other benchmarks, our algorithms was fed with optimized ligand positions up to 10 A root mean square deviation 2MSD) from the crystal structure. This validation illustrates the efficiency of our sampling heuristic, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best-ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures in this benchmark could be explained by the presence of crystal contacts in the experimental structure. EADock has been used to understand molecular interactions involved in the regulation of the Na,K ATPase, and in the activation of the nuclear hormone peroxisome proliferatoractivated receptors a (PPARa). It also helped to understand the action of common pollutants (phthalates) on PPARy, and the impact of biotransformations of the anticancer drug Imatinib (Gleevec®) on its binding mode to the Bcr-Abl tyrosine kinase. Finally, a fragment-based rational drug design approach using EADock was developed, and led to the successful design of new peptidic ligands for the a5ß1 integrin, and for the human PPARa. In both cases, the designed peptides presented activities comparable to that of well-established ligands such as the anticancer drug Cilengitide and Wy14,643, respectively. 3.2 French Les récentes difficultés de l'industrie pharmaceutique ne semblent pouvoir se résoudre que par l'optimisation de leur processus de développement de médicaments. Cette dernière implique de plus en plus. de techniques dites "haut-débit", particulièrement efficaces lorsqu'elles sont couplées aux outils informatiques permettant de gérer la masse de données produite. Désormais, les approches in silico telles que le criblage virtuel ou la conception rationnelle de nouvelles molécules sont utilisées couramment. Toutes deux reposent sur la capacité à prédire les détails de l'interaction moléculaire entre une molécule ressemblant à un principe actif (PA) et une protéine cible ayant un intérêt thérapeutique. Les comparatifs de logiciels s'attaquant à cette prédiction sont flatteurs, mais plusieurs problèmes subsistent. La littérature récente tend à remettre en cause leur fiabilité, affirmant l'émergence .d'un besoin pour des approches plus précises du mode d'interaction. Cette précision est essentielle au calcul de l'énergie libre de liaison, qui est directement liée à l'affinité du PA potentiel pour la protéine cible, et indirectement liée à son activité biologique. Une prédiction précise est d'une importance toute particulière pour la découverte et l'optimisation de nouvelles molécules actives. Cette thèse présente un nouveau logiciel, EADock, mettant en avant une telle précision. Cet algorithme évolutionnaire hybride utilise deux pressions de sélections, combinées à une gestion de la diversité sophistiquée. EADock repose sur CHARMM pour les calculs d'énergie et la gestion des coordonnées atomiques. Sa validation a été effectuée sur 37 complexes protéine-ligand cristallisés, incluant 11 protéines différentes. L'espace de recherche a été étendu à une sphère de 151 de rayon autour du centre de masse du ligand cristallisé, et contrairement aux comparatifs habituels, l'algorithme est parti de solutions optimisées présentant un RMSD jusqu'à 10 R par rapport à la structure cristalline. Cette validation a permis de mettre en évidence l'efficacité de notre heuristique de recherche car des modes d'interactions présentant un RMSD inférieur à 2 R par rapport à la structure cristalline ont été classés premier pour 68% des complexes. Lorsque les cinq meilleures solutions sont prises en compte, le taux de succès grimpe à 78%, et 92% lorsque la totalité de la dernière génération est prise en compte. La plupart des erreurs de prédiction sont imputables à la présence de contacts cristallins. Depuis, EADock a été utilisé pour comprendre les mécanismes moléculaires impliqués dans la régulation de la Na,K ATPase et dans l'activation du peroxisome proliferatoractivated receptor a (PPARa). Il a également permis de décrire l'interaction de polluants couramment rencontrés sur PPARy, ainsi que l'influence de la métabolisation de l'Imatinib (PA anticancéreux) sur la fixation à la kinase Bcr-Abl. Une approche basée sur la prédiction des interactions de fragments moléculaires avec protéine cible est également proposée. Elle a permis la découverte de nouveaux ligands peptidiques de PPARa et de l'intégrine a5ß1. Dans les deux cas, l'activité de ces nouveaux peptides est comparable à celles de ligands bien établis, comme le Wy14,643 pour le premier, et le Cilengitide (PA anticancéreux) pour la seconde.

The saccharinate anion: a versatile and fascinating ligand in coordination chemistry

The saccharinate anion, obtained by deprotonation of the N-H moiety of saccharin (o-sulfobenzimide) is a very versatile and polyfunctional ligand in coordination chemistry. In this review the different forms of metal-to-ligand interactions involving this anion and some other coordination peculiarities are briefly discussed on the basis of some selected examples.

Étude de la relation entre les conformations et la signalisation des 7TMRs

L’interaction d’un ligand avec un récepteur à sept domaines transmembranaires (7TMR) couplé aux protéines G, mène à l’adoption de différentes conformations par le récepteur. Ces diverses conformations pourraient expliquer l’activation différentielle des voies de signalisation. Or, le lien entre la conformation et l’activité du récepteur n’est pas tout à fait claire. Selon les modèles classiques pharmacologiques, comme le modèle du complexe ternaire, il n’existe qu’un nombre limité de conformations qu’un récepteur peut adopter. Afin d’établir un lien entre la structure et la fonction des récepteurs, nous avons choisi dans un premier temps, le récepteur de chimiokine CXCR4 comme récepteur modèle. Ce dernier, est une cible thérapeutique prometteuse, impliqué dans l’entrée du VIH-1 dans les cellules cibles et dans la dissémination de métastases cancéreuses. Grâce au transfert d’énergie par résonance de bioluminescence (BRET) nous pouvons détecter les changements conformationnels des homodimères constitutifs de CXCR4 dans les cellules vivantes. En conséquence, nous avons mesuré les conformations de mutants de CXCR4 dont les mutations affecteraient sa fonction. Nous montrons que la capacité des mutants à activer la protéine Galphai est altérée suite au traitement avec l’agoniste SDF-1. Notamment, ces mutations altèrent la conformation du récepteur à l’état basal ainsi que la réponse conformationnelle induite suite au traitement avec l’agoniste SDF-1, l’agoniste partiel AMD3100 ou l’agoniste inverse TC14012. Ainsi, différentes conformations de CXCR4 peuvent donner lieu à une activation similaire de la protéine G, ce qui implique une flexibilité des récepteurs actifs qui ne peut pas être expliquée par le modèle du complexe ternaire (Berchiche et al. 2007). Également, nous nous sommes intéressés au récepteur de chimiokine CCR2, exprimé à la surface des cellules immunitaires. Il joue un rôle important dans l’inflammation et dans des pathologies inflammatoires telles que l’asthme. CCR2 forme des homodimères constitutifs et possède différents ligands naturels dont la redondance fonctionnelle a été suggérée. Nous avons étudié le lien entre les conformations et les activations d’effecteurs (fonctions) de CCR2. Notre hypothèse est que les différents ligands naturels induisent différentes conformations du récepteur menant à différentes fonctions. Nous montrons que les réponses de CCR2 aux différents ligands ne sont pas redondantes au niveau pharmacologique et que les chimiokines CCL8, CCL7 et CCL13 (MCP-2 à MCP-4) sont des agonistes partiels de CCR2, du moins dans les systèmes que nous avons étudiés. Ainsi, l’absence de redondance fonctionnelle parmi les chimiokines liant le même récepteur, ne résulterait pas de mécanismes complexes de régulation in vivo, mais ferait partie de leurs propriétés pharmacologiques intrinsèques (Berchiche et al. 2011). Enfin, nous nous sommes intéressés au récepteur de chimiokine CXCR7. Récemment identifié, CXCR7 est le deuxième récepteur cible de la chimiokine SDF-1. Cette chimiokine a été considérée comme étant capable d’interagir uniquement avec le récepteur CXCR4. Notamment, CXCR4 et CXCR7 possèdent un patron d’expression semblable dans les tissus. Nous avons évalué l’effet de l’AMD3100, ligand synthétique de CXCR4, sur la conformation et la signalisation de CXCR7. Nos résultats montrent qu’AMD3100, tout comme SDF-1, lie CXCR7 et augmente la liaison de SDF-1 à CXCR7. Grâce au BRET, nous montrons aussi qu’AMD3100 seul est un agoniste de CXCR7 et qu’il est un modulateur allostérique positif de la liaison de SDF-1 à CXCR7. Aussi, nous montrons pour la première fois le recrutement de la beta-arrestine 2 à CXCR7 en réponse à un agoniste. L’AMD3100 est un ligand de CXCR4 et de CXCR7 avec des effets opposés, ce qui appelle à la prudence lors de l’utilisation de cette molécule pour l’étude des voies de signalisation impliquant SDF-1 (Kalatskaya et al. 2009). En conclusion, nos travaux amènent des évidences qu’il existe plusieurs conformations actives des récepteurs et appuient les modèles de structure-activité des récepteurs qui prennent en considération leur flexibilité conformationnelle.

The binding site distance test score: a robust method for the assessment of predicted protein binding sites

We propose a novel method for scoring the accuracy of protein binding site predictions – the Binding-site Distance Test (BDT) score. Recently, the Matthews Correlation Coefficient (MCC) has been used to evaluate binding site predictions, both by developers of new methods and by the assessors for the community wide prediction experiment – CASP8. Whilst being a rigorous scoring method, the MCC does not take into account the actual 3D location of the predicted residues from the observed binding site. Thus, an incorrectly predicted site that is nevertheless close to the observed binding site will obtain an identical score to the same number of nonbinding residues predicted at random. The MCC is somewhat affected by the subjectivity of determining observed binding residues and the ambiguity of choosing distance cutoffs. By contrast the BDT method produces continuous scores ranging between 0 and 1, relating to the distance between the predicted and observed residues. Residues predicted close to the binding site will score higher than those more distant, providing a better reflection of the true accuracy of predictions. The CASP8 function predictions were evaluated using both the MCC and BDT methods and the scores were compared. The BDT was found to strongly correlate with the MCC scores whilst also being less susceptible to the subjectivity of defining binding residues. We therefore suggest that this new simple score is a potentially more robust method for future evaluations of protein-ligand binding site predictions.

An insight into the mechanism of protein separation by colloidal gas aphrons (CGA) generated from ionic surfactants

Colloidal gas aphrons (CGA), which are surfactant stabilised microbubbles, have been previously applied for the recovery of proteins from model mixtures and a few studies have demonstrated the potential of these dispersions for the selective recovery of proteins from complex mixtures. However there is a lack of understanding of the mechanism of separation and forces governing the selectivity of the separation. In this paper a mechanistic study is carried out to determine the main factors and forces influencing the selectivity of separation of whey proteins with CGA generated from ionic surfactants. Two different separation strategies were followed: (i) separation of lactoferrin and lactoperoxidase by anionic CGA generated from a solution of sodium bis-(2-ethyl hexyl) sulfosuccinate (AOT); (ii) separation of beta-lactoglobulin by cationic CGA generated from a solution of cetyltrimethylammonium bromide (CTAB). Separation results indicate that electrostatic interactions are the main forces determining the selectivity however these could not completely explain the selectivities obtained following both strategies. Protein-surfactant interactions were studied by measuring the zeta potential changes on individual proteins upon addition of surfactant and at varying pH. Interestingly strongest electrostatic interactions were measured at those pH and surfactant to protein mass ratios which were optimum for protein separation. Effect of surfactant on protein conformation was determined by measuring the change in fluorescence intensity upon addition of surfactant at varying pH. Differences in the fluorescence patterns were detected among proteins which were correlated to differences in their conformational features which could in turn explain their different separation behaviour. The effect of conformation on selectivity was further proven by experiments in which conformational changes were induced by pre-treatment of whey (heating) and by storage at 4 degrees C. Overall it can be concluded that separation of proteins by ionic CGA is driven mainly by electrostatic interactions however conformational features will finally determine the selectivity of the separation with competitive adsorption having also an effect. (c) 2006 Elsevier B.V. All rights reserved.

The Xanthomonas citri effector protein PthA interacts with citrus proteins involved in nuclear transport, protein folding and ubiquitination associated with DNA repair

P>Xanthomonas axonopodis pv. citri utilizes the type III effector protein PthA to modulate host transcription to promote citrus canker. PthA proteins belong to the AvrBs3/PthA family and carry a domain comprising tandem repeats of 34 amino acids that mediates protein-protein and protein-DNA interactions. We show here that variants of PthAs from a single bacterial strain localize to the nucleus of plant cells and form homo- and heterodimers through the association of their repeat regions. We hypothesize that the PthA variants might also interact with distinct host targets. Here, in addition to the interaction with alpha-importin, known to mediate the nuclear import of AvrBs3, we describe new interactions of PthAs with citrus proteins involved in protein folding and K63-linked ubiquitination. PthAs 2 and 3 preferentially interact with a citrus cyclophilin (Cyp) and with TDX, a tetratricopeptide domain-containing thioredoxin. In addition, PthAs 2 and 3, but not 1 and 4, interact with the ubiquitin-conjugating enzyme complex formed by Ubc13 and ubiquitin-conjugating enzyme variant (Uev), required for K63-linked ubiquitination and DNA repair. We show that Cyp, TDX and Uev interact with each other, and that Cyp and Uev localize to the nucleus of plant cells. Furthermore, the citrus Ubc13 and Uev proteins complement the DNA repair phenotype of the yeast Delta ubc13 and Delta mms2/uev1a mutants, strongly indicating that they are also involved in K63-linked ubiquitination and DNA repair. Notably, PthA 2 affects the growth of yeast cells in the presence of a DNA damage agent, suggesting that it inhibits K63-linked ubiquitination required for DNA repair.

Molecular models of protein targets from Mycobacterium tuberculosis

Structural characterization of enzymes that belong to microbial metabolic pathways is very important for structure-based drug design since some of these proteins may be present in the bacterial genome, but absent in humans. Thus, metabolic pathways became potential targets for drug design. The motivation of this work is the fact that Mycobacterium tuberculosis is the cause of the deaths of millions of people in the world, so that the structural characterization of protein targets to propose new drugs has become essential. DBMODELING is a relational database, created to highlight the importance of methods of molecular modeling applied to the Mycobacterium tuberculosis genome with the aim of proposing protein-ligand docking analysis. There are currently more than 300 models for proteins from Mycobacterium tuberculosis genome in the database. The database contains a detailed description of the reaction catalyzed by each enzyme and their atomic coordinates. Information about structures, a tool for animated gif image, a table with a specification of the metabolic pathway, modeled protein, inputs used in modeling, and analysis methods used in this project are available in the database for download. The search tool can be used for reseachers to find specific pathways or enzymes.

DBMODELING - A database applied to the study of protein targets from genome projects

Genome sequencing efforts are providing us with complete genetic blueprints for hundreds of organisms. We are now faced with assigning, understanding, and modifying the functions of proteins encoded by these genomes. DBMODELING is a relational database of annotated comparative protein structure models and their metabolic pathway characterization, when identified. This procedure was applied to complete genomes such as Mycobacteritum tuberculosis and Xylella fastidiosa. The main interest in the study of metabolic pathways is that some of these pathways are not present in humans, which makes them selective targets for drug design, decreasing the impact of drugs in humans. In the database, there are currently 1116 proteins from two genomes. It can be accessed by any researcher at http://www.biocristalografia.df.ibilce.unesp.br/tools/. This project confirms that homology modeling is a useful tool in structural bioinformatics and that it can be very valuable in annotating genome sequence information, contributing to structural and functional genomics, and analyzing protein-ligand docking.