Littérature scientifique sur le sujet « Computer-based drug design »

Tumor drug targeting is one of the most promising therapeutic strategies in oncology. The aim of this PhD work was the study of the essential features required for the assembly of tumor targeting conjugates.This work was focused on the deveploment of ligands for the GRP receptor that should function as carrier molecules for the targeting of tumor cells overexpressing this receptor. For this purpose, non-peptide GRP mimetics were designed, using a computer-based drug design technique, synthesized and tested. Two analogue compounds based on a bicyclic scaffold exerted an antagonist behaviour on the GRP receptor. Synthetic studies have been performed to optimize their production as well as biological tests to determine their potential as carrier molecules. Apart from the targeting moiety, we also studied the antineoplastic part of tumor targeting conjugates. Akt is a proto-oncogenic kinase that has been associated to cancer development. Therefore, the Akt inhibitory activity of phosphatidylinositol phosphate analogues was exploited. A small library of iminosugar-based phosphatidylinositol phosphate analogues was designed and synthesized. During the biological evaluation, target compounds displayed low to moderate inhibitory activity for Akt, which suggests their feasibility for the development of new and more potent Akt inhibitors.

Carbohydrates are abundant in nature and have functions ranging from energy storage to acting as structural components. Analysis of carbohydrate structures is important and can be used for, for instance, clinical diagnosis of diseases as well as in bacterial studies. The complexity of glycans makes it difficult to determine their structures. NMR spectroscopy is an advanced method that can be used to examine carbohydrates at the atomic level, but full assignments of the signals require much work. Reliable automation of this process would be of great help. Herein studies of Escherichia coli O-antigen polysaccharides are presented, both a structure determination by NMR and also research on glycosyltransferases which assemble the polysaccharides. The computer program CASPER has been improved to assist in carbohydrate studies and in the long run make it possible to automatically determine structures based only on NMR data. Detailed computer studies of glycans can shed light on their interactions with proteins and help find inhibitors to prevent unwanted binding. The WaaG glycosyltransferase is important for the formation of E. coli lipopolysaccharides. Molecular docking analyses of structures confirmed to bind this enzyme have provided information on how inhibitors could be composed. Noroviruses cause gastroenteritis, such as the winter vomiting disease, after binding human histo-blood group antigens. In one of the projects, fragment-based docking, followed by molecular dynamics simulations and binding free energy calculations, was used to find competitive binders to the P domain of the capsid of the norovirus VA387. These novel structures have high affinity and are a very good starting point for developing drugs against noroviruses. The protein targets in these two projects are carbohydrate binding, but the techniques are general and can be applied to other research projects.<br>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Submitted. Paper 5: Manuscript. Paper 6. Manuscript.

Les molécules d'ARN sont devenues des cibles thérapeutiques majeures, et le ciblage par petites molécules se révèle particulièrement prometteur. Cependant, malgré leur potentiel, le domaine est encore en développement, avec un nombre limité de médicaments spécifiquement conçus pour l'ARN. La flexibilité intrinsèque de l'ARN, bien qu'elle constitue un obstacle, introduit des opportunités thérapeutiques que les outils computationnels actuels ne parviennent pas pleinement à exploiter malgré leur prédisposition. Le projet de cette thèse est de construire un cadre computationnel plus complet pour la conception rationnelle de composés ciblant l'ARN. La première étape pour toute approche structure-based est l'analyse des connaissances structurales disponibles. Cependant, il manquait une base de données complète, organisée et régulièrement mise à jour pour la communauté scientifique. Pour combler cette lacune, j'ai créé HARIBOSS, une base de données de toutes les structures expérimentalement déterminées des complexes ARN-petites molécules extraites de la base de données PDB. Chaque entrée de HARIBOSS, accessible via une interface web dédiée (https://hariboss.pasteur.cloud), est annotée avec les propriétés physico-chimiques des ligands et des poches d'ARN. Cette base de données constamment mise à jour facilitera l'exploration des composés drug-like liées à l'ARN, l'analyse des propriétés des ligands et des poches, et en fin de compte, le développement de stratégies in silico pour identifier des petites molécules ciblant l'ARN. Lors de sa sortie, il a été possible de souligner que la majorité des poches de liaison à l'ARN ne conviennent pas aux interactions avec des molécules drug-like. Cela est dû à une hydrophobicité moindre et une exposition au solvant accrue par rapport aux sites de liaison des protéines. Cependant, cela résulte d'une représentation statique de l'ARN, qui peut ne pas capturer pleinement les mécanismes d'interaction avec de petites molécules. Il était nécessaire d'introduire des techniques computationnelles avancées pour une prise en compte efficace de la flexibilité de l'ARN. Dans cette direction, j'ai mis en œuvre SHAMAN, une technique computationnelle pour identifier les sites de liaison potentiels des petites molécules dans les ensembles structuraux d'ARN. SHAMAN permet d'explorer le paysage conformationnel de l'ARN cible par des simulations de dynamique moléculaire atomistique. Dans le même temps, il identifie efficacement les poches d'ARN en utilisant de petits fragments dont l'exploration de la surface de l'ARN est accélérée par des techniques d'enhanced sampling. Dans un ensemble de données comprenant divers riboswitches structurés ainsi que de petits ARN viraux flexibles, SHAMAN a précisément localisé des poches résolues expérimentalement, les classant les régions d’interaction préférées. Notamment, la précision de SHAMAN est supérieure à celle d'autres outils travaillant sur des structures statiques d'ARN dans un scénario réaliste de découverte de médicaments où seules les structures apo de la cible sont disponibles. Cela confirme que SHAMAN est une plateforme robuste pour les futures initiatives de conception de médicaments ciblant l'ARN avec de petites molécules, en particulier compte tenu de sa pertinence potentielle dans les campagnes de criblage virtuel. Dans l'ensemble, ma recherche contribue à améliorer notre compréhension et notre utilisation de l'ARN en tant que cible pour les médicaments à petites molécules, ouvrant la voie à des stratégies thérapeutiques plus efficaces dans ce domaine en évolution<br>RNA molecules have recently gained huge relevance as therapeutic targets. The direct targeting of RNA with small molecule drugs emerges for its wide applicability to different classes of RNAs. Despite this potential, the field is still in its infancy and the number of available RNA-targeted drugs remains limited. A major challenge is constituted by the highly flexible and elusive nature of the RNA targets. Nonetheless, RNA flexibility also presents unique opportunities that could be leveraged to enhance the efficacy and selectivity of newly designed therapeutic agents. To this end, computer-aided drug design techniques emerge as a natural and comprehensive approach. However, existing tools do not fully account for the flexibility of the RNA. The project of this PhD work aims to build a computational framework toward the rational design of compounds targeting RNA. The first essential step for any structure-based approach is the analysis of the available structural knowledge. However, a comprehensive, curated, and regularly updated repository for the scientific community was lacking. To fill this gap, I curated the creation of HARIBOSS ("Harnessing RIBOnucleic acid - Small molecule Structures"), a database of all the experimentally-determined structures of RNA-small molecule complexes retrieved from the PDB database. HARIBOSS is available via a dedicated web interface (https://hariboss.pasteur.cloud), and is regularly updated with all the structures resolved by X-ray, NMR, and cryo-EM, in which ligands with drug-like properties interact with RNA molecules. Each HARIBOSS entry is annotated with physico-chemical properties of ligands and RNA pockets. HARIBOSS repository, constantly updated, will facilitate the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties and, ultimately, the development of in silico strategies to identify RNA-targeting small molecules. In coincidence of its release, it was possible to highlight that the majority of RNA binding pockets are unsuitable for interactions with drug-like molecules, attributed to the lower hydrophobicity and increased solvent exposure compared to protein binding sites. However, this emerges from a static depiction of RNA, which may not fully capture their interaction mechanisms with small molecules. In a broader perspective, it was necessary to introduce more advanced computational techniques for an effective accounting of RNA flexibility in the characterization of potential binding sites. In this direction, I implemented SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables the exploration of the target RNA conformational landscape through atomistic molecular dynamics. Simultaneously, it efficiently identifies RNA pockets using small probe compounds whose exploration of the RNA surface is accelerated by enhanced-sampling techniques. In a benchmark encompassing diverse large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN accurately located experimentally resolved pockets, ranking them as preferred probe hotspots. Notably, SHAMAN accuracy was superior to other tools working on static RNA structures in the realistic drug discovery scenario where only apo structures of the target are available. This establishes SHAMAN as a robust platform for future drug design endeavors targeting RNA with small molecules, especially considering its potential applicability in virtual screening campaigns. Overall, my research contributed to enhance our understanding and utilization of RNA as a target for small molecule drugs, paving the way for more effective drug design strategies in this evolving field

This dissertation thesis consists of a series of chapters that are interwoven by solving interesting biological problems, employing various computational methodologies. These techniques provide meaningful physical insights to promote the scientific fields of interest. Focus of chapter 1 concerns, the importance of computational tools like docking studies in advancing structure based drug design processes. This chapter also addresses the prime concerns like scoring functions, sampling algorithms and flexible docking studies that hamper the docking successes. Information about the different kinds of flexible dockings in terms of accuracy, time limitations and success studies are presented. Later the importance of Induced fit docking studies was explained in comparison to traditional MD simulations to predict the absolute binding modes. Chapter 2 and 3 focuses on understanding, how sickle cell disease progresses through the production of sickled hemoglobin and its effects on sickle cell patients. And how, hydroxyurea, the only FDA approved treatment of sickle cell disease acts to subside sickle cell effects. It is believed the primary mechanism of action is associated with the pharmacological elevation of nitric oxide in the blood, however, the exact details of this mechanism is still unclear. HU interacts with oxy and deoxyHb resulting in slow NO production rates. However, this did not correlate with the observed increase of NO concentrations in patients undergoing HU therapy. The discrepancy can be attributed to the interaction of HU competing with other heme based enzymes such as catalase and peroxidases. In these two chapters, we investigate the atomic level details of this process using a combination of flexible-ligand / flexible-receptor virtual screening (i.e. induced fit docking, IFD) coupled with energetic analysis that decomposes interaction energies at the atomic level. Using these tools we were able to elucidate the previously unknown substrate binding modes of a series of hydroxyurea analogs to human hemoglobin, catalase and the concomitant structural changes of the enzymes. Our results are consistent with kinetic and EPR measurements of hydroxyurea-hemoglobin reactions and a full mechanism is proposed that offers new insights into possibly improving substrate binding and/or reactivity. Finally in chapter 4, we have developed a 3D bioactive structure of O6-alkylguanine-DNA alkyltransferase (AGT), a DNA repair protein using Monte Carlo conformational search process. It is known that AGT prevents DNA damage, mutations and apoptosis arising from alkylated guanines. Various Benzyl guanine analouges of O6- methylguanine were tested for activity as potential inhibitors. The nature and position of the substitutions methyl and aminomethyl profoundly affected their activity. Molecular modeling of their interactions with alkyltransferase provided a molecular explanation for these results. The square of the correlation coefficient (R2 ) obtained between E-model scores (obtained from GLIDE XP/QPLD docking calculations) vs log(ED)values via a linear regression analysis was 0.96. The models indicate that the ortho-substitution causes a steric clash interfering with binding, whereas the meta-aminomethyl substitution allows an interaction of the amino group to generate an additional hydrogen bond with the protein. Using this model for virtually screening studies resulted in identification of seven lead compounds with novel scaffolds from National Cancer Institute Diversity Set2.