Les biothérapies (anticorps monoclonaux, récepteurs solubles) ciblant les cytokines IL-6 etTNF-alpha pour le traitement des maladies inflammatoires chroniques ont constitué un succèsmajeur de l’industrie pharmaceutique. Elles présentent néanmoins des inconvénientsimportants : résistances, mode d’administration contraignant, coût élevé.Notre équipe travaille sur l’identification de petites molécules inhibant directement cescytokines, afin d’élargir l’offre thérapeutique existante. Administrées par voie orale, ellesconstitueraient une alternative particluièrement favorable aux patients.Durant ma thèse, j’ai réalisé le criblage expérimental (tests cellulaires et tests biochimiquesde liaison) des meilleurs composés identifiés par criblage virtuel d’un grande chimiothèque dediversité, ainsi que de composés dérivés de pyridazine issus d’une chimiothèque médicinale. J’aiainsi pu identifier plusieurs inhibiteurs directs du TNF-alpha et de l’IL-6. De plus, mon travail apermis d’affiner les procédures de criblage du Laboratoire.Ces travaux ouvrent de nouvelles pistes pour le développement de médicaments anti-cytokines. / Anti-cytokine biologics (monoclonal antibodies, soluble receptors) targeting TNF-alpha and IL-6in chronic inflammatory diseases have been a major success for pharmaceutical industry.However, they exhibit several drawbacks : resistance, difficult administration, high costs.Our team works on the discovery of small molecule inhibitors of cytokines suck as TNF-alphaand IL-6, in order to widen the range of therapeutic drugs. Orally active drugs would represent ahighly beneficial alternative for patients.During my PhD, I have performed an experimental screening (using cellular and biochemicalbinding testings) of the best compounds identified through virtual screening of a large chemicallibrary, and on pyridazine compounds of a medicinal chemical library. I have been able toidentify several small molecules inhibiting the interaction of TNF-! and IL-6 with their receptor.Moreover, my work will have an impact on the laboratory screening strategies.Overall, this work opens new avenues for anti-cytokine drug discovery.

Les biothérapies (anticorps monoclonaux, récepteurs solubles) ciblant les cytokines IL-6 etTNF-alpha pour le traitement des maladies inflammatoires chroniques ont constitué un succèsmajeur de l’industrie pharmaceutique. Elles présentent néanmoins des inconvénientsimportants : résistances, mode d’administration contraignant, coût élevé.Notre équipe travaille sur l’identification de petites molécules inhibant directement cescytokines, afin d’élargir l’offre thérapeutique existante. Administrées par voie orale, ellesconstitueraient une alternative particluièrement favorable aux patients.Durant ma thèse, j’ai réalisé le criblage expérimental (tests cellulaires et tests biochimiquesde liaison) des meilleurs composés identifiés par criblage virtuel d’un grande chimiothèque dediversité, ainsi que de composés dérivés de pyridazine issus d’une chimiothèque médicinale. J’aiainsi pu identifier plusieurs inhibiteurs directs du TNF-alpha et de l’IL-6. De plus, mon travail apermis d’affiner les procédures de criblage du Laboratoire.Ces travaux ouvrent de nouvelles pistes pour le développement de médicaments anti-cytokines. / Anti-cytokine biologics (monoclonal antibodies, soluble receptors) targeting TNF-alpha and IL-6in chronic inflammatory diseases have been a major success for pharmaceutical industry.However, they exhibit several drawbacks : resistance, difficult administration, high costs.Our team works on the discovery of small molecule inhibitors of cytokines suck as TNF-alphaand IL-6, in order to widen the range of therapeutic drugs. Orally active drugs would represent ahighly beneficial alternative for patients.During my PhD, I have performed an experimental screening (using cellular and biochemicalbinding testings) of the best compounds identified through virtual screening of a large chemicallibrary, and on pyridazine compounds of a medicinal chemical library. I have been able toidentify several small molecules inhibiting the interaction of TNF-! and IL-6 with their receptor.Moreover, my work will have an impact on the laboratory screening strategies.Overall, this work opens new avenues for anti-cytokine drug discovery.

When applied to drug discovery, modern computational systems can provide insight into the highly complex systems underlying drug activity and predict compounds or targets of interest. Many tools have been developed for computer aided drug discovery (CADD), focusing on small molecule ligands, protein targets, or both. The aim of this thesis is the improvement of CADD tools for describing small molecule properties and application of CADD to several stages of drug discovery regarding two targets for the treatment of obesity and related diseases: the neuropeptide Y4 receptor (Y4R) and the melanocortin-4 receptor (MC4R). In the first chapter, the major categories of CADD are outlined, including descriptions for many of the popular tools and examples where these tools have directly contributed to the discovery of new drugs. Following the introduction, several improvements for encoding stereochemistry and signed property distribution are introduced and tested in scenarios meant to simulate applications in virtual high-throughput screening. Y4R and MC4R are both class A G-protein coupled receptors (GPCRs) with endogenous peptide ligands that play critical roles in the signaling of satiety and energy metabolism. So far, no structures from either receptor family have been experimentally elucidated. CADD was combined with high-throughput screening (HTS) to discover the first small molecule positive allosteric modulators (PAMs) of Y4R. Secondly, CADD techniques were used to model the interaction of Y4R and pancreatic polypeptide based on experimental results that elucidate specific binding contacts. Similar SB-CADD approaches were used to model the interaction of MC4R with its high affinity peptide agonist α-MSH. Due to its role in monogenic forms of obesity, these models were used to predict which residues directly participate in binding and correlate mutated residues with their potential role in the binding site.

This thesis is focused on enzymatic studies of the secreted aspartic proteases (Saps) from Candida albicans as a tool for discovery of anti-candida drugs. C. albicans causes infections in a number of different locations, which differ widely in the protein substrates available and pH. Since C. albicans needs Saps during virulent growth, these enzymes are good targets for drug development. In order to investigate the catalytic characteristics of Saps and their inhibitor affinities, substrate-based kinetic assays were developed. Due to the low sensitivity of these assays, especially at the sub-optimal pH required to mimic the different locations of infections, these assays were not satisfactory. Therefore, a biosensor assay was developed whereby, it was possible to study interaction between Saps and inhibitors without the need to optimise catalytic efficacy. Furthermore, the biosensor assay allowed determination of affinity, as well as the individual association and dissociation rates for inhibitor interactions. Knowledge about substrate specificity, Sap subsite adaptivity, and the pH dependencies of catalytic efficacy has been accumulated. Also, screening of transition-state analogue inhibitors designed for HIV-1 protease has revealed inhibitors with affinity for Saps. Furthermore, the kinetics and pH dependencies of their interaction with Saps have been investigated. One of these inhibitors, BEA-440, displayed a complex interaction with Saps, indicating a conformational change upon binding and a very slow dissociation rate. A time dependent interaction was further supported by inhibition measurements. The structural information obtained affords possibilities for design of new more potent inhibitors that might ultimately become drugs against candidiasis. The strategy to combine substrate specificity studies with inhibitor screening has led to complementary results that generate a framework for further development of potent inhibitors.

Previously, the CCR5 receptor was found to be a good target for treating prostate cancer (PCa). Dr. Yan Zhang’s laboratory designed several CCR5 antagonists, which were screened for their inhibitory effect on the growth and invasion of the M12, DU145 and PC-3 PCa cell lines. Primary in vitro screening showed one compound (Drug 17) significantly inhibited the proliferation of PCa cells at 1μM concentration, with a half-maximal inhibitory concentration of 237.68 nM. Further in vitro assays including a proliferation, cytotoxicity and invasion assay confirmed the inhibitory effect of drug 17. The physiological effect of drug 17 was tested by the Ware laboratory in vivo by subcutaneous injection of M12 cells into male, athymic nude mice. Tumor growth was slowed in mice receiving injections of drug 17 compared to sham injected controls. Thus, in vitro and in vivo assays suggest drug 17 might be an effective therapy to block PCa progression.

The human body is colonized by more than 100 trillion microbes which make up an essential part of the body and plays a significant role in health. We now know the over use and misuse of broad-spectrum antibiotics can disrupt this microbiome contributing to the onset of disease and runs the risk of promoting antibiotic resistance. With antibiotic research still on the decline, new strategies are greatly needed to combat emerging pathogens while maintaining a healthy microbiome. We therefore set out to present a novel species-selective antimicrobial drug discovery strategy. Disruption of the homeostasis within the oral cavity can trigger the onset of one of the most common bacterial infections, periodontal disease. Even though the oral cavity is one of the most diverse sites on the human body, the Gram-negative colonizer, Porphyromonas gingivalis has long been considered a key player in the initiation of periodontitis, suggesting the potential for novel narrow-spectrum therapeutics. By targeting key pathogens, it may be possible to treat periodontitis while allowing for the recolonization of the beneficial, healthy flora. Therefore, we set out to use P. gingivalis and periodontal disease as a model for pathogen-specific antimicrobial drug discovery. In this study we present a unique approach to predict essential gene targets selective for the periodontal pathogen within the oral environment. Using our knowledge of metabolic networks and essential genes we identified a “druggable” essential target, meso-diaminopimelate dehydrogenase, which is found in a limited number of species. This enzyme, meso-diaminopimelate dehydrogenase from P. gingivalis, was first expressed and purified, then characterized for enzymatic inhibitor screening studies. We then applied a computer-based drug discovery method, combining pharmacophore models, high-throughput virtual screening and molecular docking. Utilizing the ZINC database we virtually screened over 9 million small-molecules to identify several potential target-specific inhibitors. Finally, we used target-based and whole-cell based biochemical screening to assess in vitro activity. We conclude that the establishment of this target and screening strategy provides a framework for the future development of new antimicrobials and drug discovery.

The 14-3-3 family of proteins are important signalling proteins involved in a number of cellular processes. These include cell cycle regulation, apoptosis, signal transduction and cell signalling. There is also considerable evidence in the literature that 14-3-3 proteins play a vital role in the pathology of neurodegenerative diseases, including Alzheimer’s, Parkinson’s, Huntington’s and Prion disease. The neurodegenerative disease of focus in this research is Spinocerebellar Ataxia Type 1 (SCA1). SCA1 is a polyglutamine-repeat disease and the interaction of the disease protein ataxin-1 with 14-3-3 proteins leads to the toxic accumulation and subsequent protein aggregation which is characteristic of this disease. This study focused on attempting to elucidate the structure of various domains of the disease protein and also in identifying potential inhibitors of this deleterious interaction. Unfortunately, structural studies were not successful due to a number of caveats encountered in the expression and purification of the ataxin-1 protein domains. By utilising computational methods and small molecule inhibitors, a number of potential lead compounds which possess the ability to at least partly disrupt the interaction of 14- 3-3ζ have been identified. As 14-3-3 proteins play roles in other neurodegenerative diseases, successful identification of potential drug lead treatments can have far reaching benefits in a number of neurodegenerative diseases including SCA1. Lipid rafts are also involved in neurodegenerative disease pathology. Lipid rafts are cholesterol and sphingolipid rich domains which organise the plasma membrane into discrete microdomains and act as signalling platforms and processing centres which attach specific proteins and lipids. A number of disease proteins are processed at these membrane regions, including those involved in Alzheimer’s, Parkinson’s and Prion disease. This processing is a step which is critical in the pathology of disease and abnormal processing leads to the formation of toxic protein aggregates. Previous research in the lab identified the association of low levels of the five main brain isoforms of 14-3-3 proteins with rafts. This study expanded on this to positively identify the presence of the two phospho-forms of 14-3-3, α and δ. The mechanism by which 14-3-3 proteins associate with rafts was also investigated, indicating that 14-3-3 associates with rafts via an unidentified raftbound protein(s). In addition, the phosphorylation status and quaternary structure of 14-3-3 in the presence of sphingolipids has been explored.

The misfolding and aggregation of proteins is closely associated with more than fifty human disorders, including Alzheimer's and Parkinson's diseases, all of which are currently incurable and many represent a major threat to human life. The mechanism of protein aggregation is subject to extensive studies. The damaging effects associated with protein aggregation have been attributed to amyloidogenic species that are present during the misfolding process. In particular, oligomeric species are, however, intrinsically difficult to study as a consequence of their low abundance and highly heterogeneous nature. The first chapter of my thesis gives an introduction into the field of protein folding and misfolding with a focus on the study of protein aggregation, and toxic effects relevant to human disorders. The second chapter of my thesis describes the development of a methodology that enables the study of aggregate induced lipid bilayer permeability, possibly the most general mechanism of protein aggregate toxicity. Surface-tethered lipid vesicles functioning as optochemical probes sensitive to membrane integrity are imaged using total internal reflection microscopy. It is shown that oligomeric species of the 42-residue form of the Aβ peptide (Aβ42) are responsible for the membrane disruption. The methodology can be applied to the study of other proteins such as α-synuclein and tau, and the ability of antibodies and chaperones to counteract the aggregate induced lipid bilayer permeability can be assessed. Furthermore, lipid bilayer permeability induced by aggregates formed in human induced pluripotent stem cells can be studied. The third chapter presents a new approach for the measurement of protein aggregation kinetics by following the development of the lipid bilayer permeability over the course of the aggregation process of Aβ42. The aggregation kinetics can be modulated with molecular chaperones and pre-formed seed fibrils, which allows secondary nucleation to be identified as the process that drives the formation of species responsible for the lipid bilayer permeability. The fourth chapter describes the development of a three-pronged strategy to study the mechanism of α-synuclein amyloid formation. The aggregation is studied in the presence of lipid vesicles or pre-formed fibrils at neutral or acidic pH of the solution. The influence of single-point mutations on the aggregation of α-synuclein is described. Furthermore, the strategy is applied to the characterisation of the ability of antibodies and small molecules to inhibit the aggregation, and thus has the potential for the development of therapeutical agents. The work presented in the fifth chapter characterises the amyloid fibril populations formed by α-synuclein and mutational variants associated with familial Parkinson's disease. X-ray crystallography, circular dichroism spectroscopy, Fourier transform infrared spectroscopy, transmission electron microscopy and atomic force microscopy have all been applied to the analysis of these amyloid fibrils. Finally, the sixth chapter summarises the results described in this thesis and points out future opportunities in the context of fundamental and translational studies related to the research area of protein misfolding disorders.

Submitted by Fabíola Silva (fabiola.silva@famerp.br) on 2018-02-19T12:03:16Z No. of bitstreams: 1 thiagohenrique_tese.pdf: 1021380 bytes, checksum: c215716ce3befdfb390cce0309052b80 (MD5) / Made available in DSpace on 2018-02-19T12:03:16Z (GMT). No. of bitstreams: 1 thiagohenrique_tese.pdf: 1021380 bytes, checksum: c215716ce3befdfb390cce0309052b80 (MD5) Previous issue date: 2016-05-16 / Introduction: The total amount of scientific literature on cancer has grown rapidly in recent years. This makes it difficult, if not impossible, to manually retrieve relevant information on the mechanisms that govern the neoplastic process. Furthermore, cancer is a complex disease, and its. Heterogeneity is particularly evident in head and neck squamous cell carcinoma (HNSCC); one of the most common types of cancer worldwide. Objectives: The present study aimed: a) to identify genes/proteins related to HNSCC; b) to identify ligands that specifically target molecular biomarkers of interest; c) to evaluate computationally protein-ligand complexes and d) to evaluate the effect of ligands on gene expression and on carcinoma cell behavior. Methods: The search for potential markers related to HNSCC was performed by literature mining, following a flow chart that included selection of scientific articles in PubMed by MeSH terms, association of articles with genes/proteins through the gene2pubmed file, selection of genes in external data bases and manual curation steps. In order to identify potential ligands, proteins related to HNSCC and involved in inflammatory processes were used to perform molecular docking assays with known anti-inflammatory drugs. Finally, the role of piplartine, a substance extracted from the Piper longum with anti-inflammatory and antineoplastic effects, on proliferation, migration and gene expression was investigated in neoplastic cells. Results: The curated gene-to-publication assignment yielded a total of 1,370 genes related to HNSCC, with specificity of 74% and sensibility of 87%. The diversity of results allowed identifying new and mostly unexplored gene associations, revealing, for example, that processes linked to response to steroid hormone stimulus are significantly enriched with genes related to HNSCC. The results also showed that piplartine decreases viability and cell migration, and alters expression of genes involved in inflammatory responses. Conclusion: This approach allows the identification of genes related to HNSCC and is able to reveal new associations that deserve to be further studied. Piplartine, the compound selected for in vitro studies, interacts with molecular targets similar to known anti-inflammatory drugs, decreases proliferation and cell migration, and alter the expression of genes associated with HNSCC and inflammatory processes. / Introdução: A literatura científica sobre câncer tem crescido rapidamente nos últimos anos, o que torna difícil, se não impossível, a tarefa de recuperar e analisar manualmente as informações relevantes sobre os mecanismos que governam o processo neoplásico. Além disso, o câncer é uma doença complexa e sua heterogeneidade é particularmente evidente no câncer epidermoide de cabeça e pescoço (CECP), um dos tipos mais comuns de câncer em todo o mundo. Objetivos: Os objetivos do estudo foram (a) identificar genes/proteinas relacionados a CECP a partir de dados da literatura, (b) identificar ligantes que interajam eficiente e especificamente com alvos moleculares selecionados, (c) avaliar computacionalmente o complexo proteína/ligante e (d) avaliar a ação de ligantes na expressão gênica e no comportamento de células de carcinoma. Métodos: A busca de marcadores potenciais relacionados a CECP utilizou a mineração da literatura disponível publicamente, seguindo um fluxograma que incluiu seleção de artigos científicos no PubMed por termos MeSH, associação de artigos com genes/proteínas por meio do arquivo gene2pubmed, seleção de genes em bancos de dados externos e etapas de curação manual. As proteínas identificadas como sendo relacionadas a CECP que apresentaram envolvimento em processos inflamatórios foram submetidas a experimento de docking molecular para identificação de seus ligantes entre drogas disponíveis no mercado. Finalmente, o papel da piplartina, uma substância natural extraída da pimenta Piper longum com evidências de ação anti-inflamatória e antineoplásica, foi avaliado na proliferação, na migração e na expressão gênica de células neoplásicas. Resultados: Um total de 1370 genes relacionados a CECP foi identificado pela abordagem proposta, que mostrou especificidade de 74% e sensibilidade de 87%. A diversidade dos dados permitiu obter associações potenciais ainda não exploradas, revelando, por exemplo, que a resposta ao estímulo esteróide hormonal está significativamente enriquecida com genes relacionados a CECP. Os resultados também mostraram que a piplartina reduz a viabilidade e a migração celular e modifica o padrão de expressão de um painel de genes que atuam em processos inflamatórios. Conclusão: A abordagem empregada permite a identificação de genes relacionados à CECP e revela novas associações que merecem ser estudadas. O composto piplartina selecionado para estudos in vitro interage com alvos moleculares de forma semelhante à de medicamentos anti-inflamatórios conhecidos e é capaz de diminuir a proliferação e a migração celular e de alterar a expressão de genes relacionados à CECP e a processos inflamatórios.

Sequences are ubiquitous in the domain of biology. One of the current best machine learning techniques for analysing sequences is the long short-term memory (LSTM) network. Owing to significant barriers to adoption in biology, focussed efforts are required to realize the use of LSTMs in practice. Thus, the aim of this work is to improve the state of LSTMs for biology, and we focus on biological tasks pertaining to physiological signals, peripheral neural signals, and molecules. This goal drives the three subplots in this thesis: biological applications, visualizations, and extensions. We start by demonstrating the utility of LSTMs for biological applications. On two new physiological-signal datasets, LSTMs were found to outperform hidden Markov models. LSTM-based models, implemented by other researchers, also constituted the majority of the best performing approaches on publicly available medical datasets. However, even if these models achieve the best performance on such datasets, their adoption will be limited if they fail to indicate when they are likely mistaken. Thus, we demonstrate on medical data that it is straightforward to use LSTMs in a Bayesian framework via dropout, providing model predictions with corresponding uncertainty estimates. Another dataset used to show the utility of LSTMs is a novel collection of peripheral neural signals. Manual labelling of this dataset is prohibitively expensive, and as a remedy, we propose a sequence-to-sequence model regularized by Wasserstein adversarial networks. The results indicate that the proposed model is able to infer which actions a subject performed based on its peripheral neural signals with reasonable accuracy. As these LSTMs achieve state-of-the-art performance on many biological datasets, one of the main concerns for their practical adoption is their interpretability. We explore various visualization techniques for LSTMs applied to continuous-valued medical time series and find that learning a mask to optimally delete information in the input provides useful interpretations. Furthermore, we find that the input features looked for by the LSTM align well with medical theory. For many applications, extensions of the LSTM can provide enhanced suitability. One such application is drug discovery -- another important aspect of biology. Deep learning can aid drug discovery by means of generative models, but they often produce invalid molecules due to their complex discrete structures. As a solution, we propose a version of active learning that leverages the sequential nature of the LSTM along with its Bayesian capabilities. This approach enables efficient learning of the grammar that governs the generation of discrete-valued sequences such as molecules. Efficiency is achieved by reducing the search space from one over sequences to one over the set of possible elements at each time step -- a much smaller space. Having demonstrated the suitability of LSTMs for biological applications, we seek a hardware efficient implementation. Given the success of the gated recurrent unit (GRU), which has two gates, a natural question is whether any of the LSTM gates are redundant. Research has shown that the forget gate is one of the most important gates in the LSTM. Hence, we propose a forget-gate-only version of the LSTM -- the JANET -- which outperforms both the LSTM and some of the best contemporary models on benchmark datasets, while also reducing computational cost.