Identification and characterization of novel classes of macrophage migration inhibitory factor (MIF) inhibitors with distinct mechanisms of action.

Macrophage migration inhibitory factor (MIF), a proinflammatory cytokine, is considered an attractive therapeutic target in multiple inflammatory and autoimmune disorders. In addition to its known biologic activities, MIF can also function as a tautomerase. Several small molecules have been reported to be effective inhibitors of MIF tautomerase activity in vitro. Herein we employed a robust activity-based assay to identify different classes of novel inhibitors of the catalytic and biological activities of MIF. Several novel chemical classes of inhibitors of the catalytic activity of MIF with IC(50) values in the range of 0.2-15.5 microm were identified and validated. The interaction site and mechanism of action of these inhibitors were defined using structure-activity studies and a battery of biochemical and biophysical methods. MIF inhibitors emerging from these studies could be divided into three categories based on their mechanism of action: 1) molecules that covalently modify the catalytic site at the N-terminal proline residue, Pro(1); 2) a novel class of catalytic site inhibitors; and finally 3) molecules that disrupt the trimeric structure of MIF. Importantly, all inhibitors demonstrated total inhibition of MIF-mediated glucocorticoid overriding and AKT phosphorylation, whereas ebselen, a trimer-disrupting inhibitor, additionally acted as a potent hyperagonist in MIF-mediated chemotactic migration. The identification of biologically active compounds with known toxicity, pharmacokinetic properties, and biological activities in vivo should accelerate the development of clinically relevant MIF inhibitors. Furthermore, the diversity of chemical structures and mechanisms of action of our inhibitors makes them ideal mechanistic probes for elucidating the structure-function relationships of MIF and to further determine the role of the oligomerization state and catalytic activity of MIF in regulating the function(s) of MIF in health and disease.

Cation Transport Coupled to ATP Hydrolysis By the (Na, K)-ATPase : an integrated, animated model

An Adobe (R) animation is presented for use in undergraduate Biochemistry courses, illustrating the mechanism of Na+ and K+ translocation coupled to ATP hydrolysis by the (Na, K)-ATPase, a P-2c-type ATPase, or ATP-powered ion pump that actively translocates cations across plasma membranes. The enzyme is also known as an E-1/E-2-ATPase as it undergoes conformational changes between the E-1 and E-2 forms during the pumping cycle, altering the affinity and accessibility of the transmembrane ion-binding sites. The animation is based on Horisberger's scheme that incorporates the most recent significant findings to have improved our understanding of the (Na, K)-ATPase structure function relationship. The movements of the various domains within the (Na, K)-ATPase alpha-subunit illustrate the conformational changes that occur during Na+ and K+ translocation across the membrane and emphasize involvement of the actuator, nucleotide, and phosphorylation domains, that is, the "core engine" of the pump, with respect to ATP binding, cation transport, and ADP and P-i release.

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.

Blind docking of 260 protein-ligand complexes with EADock 2.0.

Molecular docking softwares are one of the important tools of modern drug development pipelines. The promising achievements of the last 10 years emphasize the need for further improvement, as reflected by several recent publications (Leach et al., J Med Chem 2006, 49, 5851; Warren et al., J Med Chem 2006, 49, 5912). Our initial approach, EADock, showed a good performance in reproducing the experimental binding modes for a set of 37 different ligand-protein complexes (Grosdidier et al., Proteins 2007, 67, 1010). This article presents recent improvements regarding the scoring and sampling aspects over the initial implementation, as well as a new seeding procedure based on the detection of cavities, opening the door to blind docking with EADock. These enhancements were validated on 260 complexes taken from the high quality Ligand Protein Database [LPDB, (Roche et al., J Med Chem 2001, 44, 3592)]. Two issues were identified: first, the quality of the initial structures cannot be assumed and a manual inspection and/or a search in the literature are likely to be required to achieve the best performance. Second the description of interactions involving metal ions still has to be improved. Nonetheless, a remarkable success rate of 65% was achieved for a large scale blind docking assay, when considering only the top ranked binding mode and a success threshold of 2 A RMSD to the crystal structure. When looking at the five-top ranked binding modes, the success rate increases up to 76%. In a standard local docking assay, success rates of 75 and 83% were obtained, considering only the top ranked binding mode, or the five top binding modes, respectively.

Mechanisms of OGT-mediated HCF-1 protein maturation

Post-translational protein modifications are crucial for many fundamental cellular and extracellular processes and greatly contribute to the complexity of organisms. Human HCF-1 is a transcriptional co-regulator that undergoes complex protein maturation involving reversible and irreversible post-translational modifications. Upon synthesis as a large precursor protein, HCF-1 undergoes extensive reversible glycosylation with β-N-acetylglucosamine giving rise to O-linked-β-N-acetylglucosamine (O-GlcNAc) modified serines and threonines. HCF-1 also undergoes irreversible site-specific proteolysis, which is important for one of HCF-1's major functions - the regulation of the cell-division cycle. HCF-1 O-GlcNAcylation and site-specific proteolysis are both catalyzed by a single enzyme with an unusual dual enzymatic activity, the O-GlcNAc transferase (OGT). HCF-1 is cleaved by OGT at any of six highly conserved 26 amino acid repeated sequences (HCF-1PRO repeats), but the mechanisms and the substrate requirements for OGT-mediated cleavage are not understood. In the present work, I characterized substrate requirements for OGT-mediated cleavage and O-GlcNAcylation of HCF-1. I identified key elements within the HCF-1PRO-repeat sequence that are important for proteolysis. Remarkably, an invariant single amino acid side-chain within the HCF-1PRO-repeat sequence displays particular OGT-binding properties and is essential for proteolysis. Additionally, I characterized substrate requirements for proteolysis outside of the HCF-1PRO repeat and identified a novel, highly O-GlcNAcylated OGT-binding sequence that enhances cleavage of the first HCF-1PRO repeat. These results link OGT association and its O-GlcNAcylation activities to HCF-1PRO-repeat proteolysis.