In Silico Strategies for Prospective Drug Repositionings

The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions

Automatically exploiting genomic and metabolic contexts to aid the functional annotation of prokaryote genomes

Cette thèse porte sur le développement d'approches bioinformatiques exploitant de l'information de contextes génomiques et métaboliques afin de générer des annotations fonctionnelles de gènes prokaryotes, et comporte deux projets principaux. Le premier projet focalise sur les activités enzymatiques orphelines de séquence. Environ 27% des activités définies par le International Union of Biochemistry and Molecular Biology sont encore aujourd'hui orphelines. Pour celles-ci, les méthodes bioinformatiques traditionnelles ne peuvent proposer de gènes candidats; il est donc impératif d'utiliser des méthodes exploitant des informations contextuelles dans ces cas. La stratégie CanOE (fishingCandidate genes for Orphan Enzymes) a été développée et rajoutée à la plateforme MicroScope dans ce but, intégrant des informations génomiques et métaboliques sur des milliers d'organismes prokaryotes afin de localiser des gènes probants pour des activités orphelines. Le projet miroir au précédent est celui des protéines de fonction inconnue. Un projet collaboratif a été initié au Genoscope afin de formaliser les stratégies d'exploration des fonctions de familles protéiques prokaryotes. Une version pilote du projet a été mise en place sur la famille DUF849 dont une fonction enzymatique avait été récemment découverte. Des stratégies de proposition d'activités enzymatiques alternatives et d'établissement de sous familles isofonctionnelles ont été mises en place dans le cadre de cette thèse, afin de guider les expérimentations de paillasse et d'analyser leurs résultats.The subject of this thesis concerns the development of bioinformatic strategies exploiting genomic and metabolic contextual information in order to generate functional annotations for prokaryote genes. Two main projects were involved during this work: the first focuses on sequence-orphan enzymatic activities. Today, roughly 27% of activities defined by International Union of Biochemistry and Molecular Biology are sequence-orphans. For these, traditional bioinformatic approaches cannot propose candidate genes. It is thus imperative to use alternative, context-based approaches in such cases. The CanOE strategy fishing Candidate genes for Orphan Enzymes) was developed and added to the MicroScope bioinformatics platform in this aim. It integrates genomic and metabolic information across thousands of prokaryote genomes in order to locate promising gene candidates for orphan activities. The mirror project focuses on protein families of unknown function. A collaborative project has been set up at the Genoscope in hope of formalising functional exploration strategies for prokaryote protein families. A pilot version was created on the DUF849 Pfam family, for which a single activity had recently been elucidated. Strategies for proposing novel functions and activities and creating isofunctional sub-families were researched, so as to guide biochemical experimentations and to analyse their results.EVRY-Bib. électronique (912289901) / SudocSudocFranceF

Protein Structure

Since the dawn of recorded history, and probably even before, men and women have been grasping at the mechanisms by which they themselves exist. Only relatively recently, did this grasp yield anything of substance, and only within the last several decades did the proteins play a pivotal role in this existence. In this expose on the topic of protein structure some of the current issues in this scientific field are discussed. The aim is that a non-expert can gain some appreciation for the intricacies involved, and in the current state of affairs. The expert meanwhile, we hope, can gain a deeper understanding of the topic

Computational method development for drug discovery

Protein-small molecule interactions play a central role in various aspects of the structural and functional organization of the cell and are therefore integral for drug discovery. The most comprehensive structural characterization of small molecule binding sites is provided by X-ray crystallography. However, it is often time-consuming and challenging to perform direct experimental analysis. Therefore, it is necessary to have computational methods that can predict binding site locations on unbound structures with accuracy close to that provided by X-ray crystallography. This thesis details four projects which involve the development of a fragment benchmark set, evaluation of allosteric sites in G Protein-Coupled Receptors (GPCRs), computational modeling of binding pocket dynamics, and the development of an Application Program Interface (API) framework for High-Performance Computing (HPC) centers. The first project provides a benchmark set for testing hot spot identification methods, emphasizing application to fragment-based drug discovery. Using the solvent mapping server, FTMap, which finds small molecule binding hot spots on proteins, we compared our benchmark set to an existing benchmark set that with a different method of construction. The second project details the effort to identify allosteric binding sites on GPCRs. We demonstrate that FTMap successfully identifies structurally determined allosteric sites in bound crystal structures and unbound structures. The project was further expanded to evaluate the conservation of allosteric sites across different classes, families, and types of GPCRs. The third project provides a structure-based analysis of cryptic site openings. Cryptic sites are pockets formed in ligand-bound proteins but not observed in unbound protein structures. Through analysis of crystal structures supplemented by molecular dynamics (MD) with enhanced sampling techniques, it was shown that cryptic sites can be grouped into three types: 1) “genuine” cryptic sites, which do not form without ligand binding, 2) spontaneously forming cryptic sites, and 3) cryptic sites impacted by mutations or off-site ligand binding. The fourth project presents an API framework for increasing the accessibility of HPC resources