Magister Pharmaceuticae - MPharm / Malaria is an infectious disease which continues to kill more than one million people every year and the African continent accounts for most of the malaria death worldwide. New classes of medicine to combat malaria are urgently needed due to the surge in resistance of the Plasmodium falciparum (the parasite that causes malaria in humans) to existing antimalarial drugs. One approach to circumvent the problem of P. falciparum resistance to antimalarial drugs could be the discovery of novel compounds with unique scaffolds and possibly new mechanisms of action. Natural products (NP) provide a wide diversity of compounds with unique scaffolds, as such, a library of virtual compounds (VC) designed from natural products with antiplasmodial activities (NAA) can be a worthy starting point.

Indiana University-Purdue University Indianapolis (IUPUI) / Protein-protein interactions are fundamental in cell signaling and cancer progression. An increasing prevalent idea in cancer therapy is the development of small molecules to disrupt protein-protein interactions. Small molecules impart their action by binding to pockets on the protein surface of their physiological target. At protein-protein interactions, these pockets are often too large and tight to be disrupted by conventional design techniques. Residues that contribute a disproportionate amount of energy at these interfaces are known as hot spots. The successful disruption of protein-protein interactions with small molecules is attributed to the ability of small molecules to mimic and engage these hot spots. Here, the role of hot spots is explored in existing inhibitors and compared with the native protein ligand to explore how hot spot residues can be leveraged in protein-protein interactions. Few studies have explored the use of interface residues for the identification of hit compounds from structure-based virtual screening. The tight uPAR•uPA interaction offers a platform to test methods that leverage hot spots on both the protein receptor and ligand. A method is described that enriches for small molecules that both engage hot spots on the protein receptor uPAR and mimic hot spots on its protein ligand uPA. In addition, differences in chemical diversity in mimicking ligand hot spots is explored. In addition to uPAR•uPA, there are additional opportunities at unperturbed protein-protein interactions implicated in cancer. Projects such as TCGA, which systematically catalog the hallmarks of cancer across multiple platforms, provide opportunities to identify novel protein-protein interactions that are paramount to cancer progression. To that end, a census of cancer-specific binding sites in the human proteome are identified to provide opportunities for drug discovery at the system level. Finally, tumor genomic, protein-protein interaction, and protein structural data is integrated to create chemogenomic libraries for phenotypic screening to uncover novel GBM targets and generate starting points for the development of GBM therapeutic agents. / 2020-10-03

Indiana University-Purdue University Indianapolis (IUPUI) / Proof of Concept - An Academic LIMS application: The aim of this project is the creation of an open-source, freeware LIMS application that can be used in an academic setting as a teaching tool for both chemistry and computer science students. The LIMS package will combine an application, developed using VB.NET, to manage the data with other open-source or freeware programs such as MySQL and WEKA. The numerous commercial chemical informatics applications available are useful tools to learn how to manage data from a user's standpoint. However, they are not readily available to the average student, nor do they offer a great understanding into how they were developed from a programmer's frame of mind. There is a great void here that, if filled can greatly help the academic community.

Deep learning models are dominating performance across a wide variety of tasks. From protein folding to computer vision to voice recognition, deep learning is changing the way we interact with data. The field of natural products, and more specifically genomic mining, has been slow to adapt to these new technological innovations. As we are in the midst of a data explosion, it is not for lack of training data. Instead, it is due to the lack of a blueprint demonstrating how to correctly integrate these models to maximise performance and inference. During my PhD, I showcase the use of large language models across a variety of data domains to improve common workflows in the field of natural product drug discovery. I improved natural product scaffold comparison by representing molecules as sentences. I developed a series of deep learning models to replace archaic technologies and create a more scalable genomic mining pipeline decreasing running times by 8X. I integrated deep learning-based genomic and enzymatic inference into legacy tooling to improve the quality of short-read assemblies. I also demonstrate how intelligent querying of multi-omic datasets can be used to facilitate the gene signature prediction of encoded microbial metabolites. The models and workflows I developed are wide in scope with the hopes of blueprinting how these industry standard tools can be applied across the entirety of natural product drug discovery. / Thesis / Doctor of Philosophy (PhD)

Microbial natural products have served a key role in the development of clinically relevant drugs. Despite significant interest, traditional strategies in their characterization have lead to diminishing returns, leaving this field stagnant. Recently developed technologies such as low-cost, high-throughput genome sequencing and high-resolution mass spectrometry allow for a much richer experimental strategy, allowing us to gather data at an unprecedented scale. Naive efforts in analyzing genomic data have already revealed the wealth of natural products encoded within diverse bacterial phylogenies. Herein, I leverage these technologies through the development of specialized computational platforms cognizant of existing natural products and their biosynthesis in order to reinvigorate our drug discovery protocols. As a first, I present a strategy for the targeted isolation of novel and structurally divergent ribosomally synthesized and post-translationally modified peptides (RiPPs). Specifically, this software platform is able to directly compare genomically encoded RiPPs to previously characterized chemical scaffolds, allowing for the identification of bacterial strains producing these specialized, and previously unstudied metabolites. Further, using metabolomics data, I have developed a strategy that facilitates direct identification and targeted isolation of these uncharacterized RiPPs. Through these set of tools, we were able to successfully isolate a structurally unique lasso peptide from a previously unexplored \textit{Streptomyces} isolate. With the technological rise of genomic sequencing, it is now possible to survey polymicrobial environments with remarkable detail. Through the use of metagenomics, we can survey the presence and abundances of bacteria, and further metatranscriptomics is able to reveal the expression of their biosynthetic pathways. Here, I developed a platform which is able to identify microbial peptides exclusively found within the human microbiome, and further characterize their putative antimicrobial properties. Through this endeavour, we identified a bacterially encoded peptide that can effectively protect against pathogenic \textit{Clostridium difficile} infections. With the wealth of publicly available multi-omics datasets, these works in conjunction demonstrate the potential of informatics strategies in the advancement of natural product discovery. / Thesis / Master of Science (MSc) / Biochemistry is the study in which life is built upon a series of diverse chemistry and their interactions. Some of these chemicals are not essential for the maintaining basic metabolism, but are instead tailored for alternative functions best suited to their environment. Often, these molecules mediate biological warfare, allowing organisms to compete and establish dominance amongst their neighbours. Understanding this, several of these molecules have been exploited in our modern pharmaceutical regimen as effective antibiotics. Due to the ever rising reality of antibiotic resistance, we are in dire need of novel antibiotics. With this goal, I have developed several software tools that can both identify these molecules encoded within bacterial genomes, but also predict their effects on neighbouring bacteria. Through these computational tools, I provide an updated strategy for the discovery and characterization of these biologically derived chemicals.

At its core, a nanopore detector has a nanometer-scale biological membrane across which a voltage is applied. The voltage draws a DNA molecule into an á-hemolysin channel in the membrane. Consequently, a distinctive channel current blockade signal is created as the molecule flexes and interacts with the channel. This flexing of the molecule is characterized by different blockade levels in the channel current signal. Previous experiments have shown that a nanopore detector is sufficiently sensitive such that nearly identical DNA molecules were classified successfully using machine learning techniques such as Hidden Markov Models and Support Vector Machines in a channel current based signal analysis platform [4-9]. In this paper, methods for improving feature extraction are presented to improve both classification and to provide biologists and chemists with a better understanding of the physical properties of a given molecule.

Com o surgimento da era computacional com especial aplicação em química, as substâncias de origem naturais puderam ter suas informações armazenadas em bancos de dados. Desta forma, surge a oportunidade de se empregar bancos de dados de produtos naturais e de algumas ferramentas de quimioinformática como os estudos de Quantitative Structure-Retention Relationship (QSRR) para acelerar a identificação de substâncias em estudos metabolômicos. Este trabalho propôs o desenvolvimento de três estudos de QSRR, bem como a construção de um banco de dados (AsterDB) com estruturas químicas da família Asteraceae e informações a elas associadas (ex.: ocorrências botânicas e taxonômicas, atividade biológica, informações analíticas etc.) para auxiliar a desreplicação de substâncias em extratos vegetais. O primeiro estudo foi elaborado com 39 lactonas sesquiterpênicas (LST) analisadas em dois diferentes sistemas de solventes (MeOH-H2O 55:45 e MeCN-H2O 35:65), três grupos de descritores estruturais (2D-descr, 3D-1conf e 3D-weigh), dois diferentes conjuntos para treino e teste (26:13 e 29:10), quatro algoritmos para seleção de descritores (best first, linear forward - LFS, greedy stepwise e algoritmo genético - GA), três diferentes tamanhos de modelos (quatro, cinco e seis descritores) e dois métodos de modelagem (mínimos quadrados parciais - PLS e redes neurais artificiais - ANN). O segundo foi desenvolvido com 50 substâncias de diferentes classes químicas com intuito de avaliar as diferenças entre substâncias analisadas individualmente e em mistura em três diferentes equipamentos e dois métodos cromatográficos. O terceiro foi elaborado com 2.635 estruturas químicas com um teste externo comum a todos os modelos (25%, n = 656), três métodos de separação para teste e treino (partição baseada na resposta e baseada nos preditores 2D e 3D), três diferentes tamanhos de modelos selecionados por GA e dois métodos de modelagem (MLR e redes neurais feed-forward com regularização bayesiana - BRNN). O banco de dados AsterDB foi desenvolvido para ser preenchido de forma gradual e atualmente possui cerca de 2.000 estruturas químicas. O primeiro estudo de QSRR gerou bons modelos capazes de estimar o logaritmo do fator de retenção (logk) das LST com P2>0,81 para o sistema MeCN-H2O. O segundo estudo mostrou que não houve diferença estatística entre as substâncias analisadas individualmente e em mistura (p-valor>0,95) e que a correlação entre os dois métodos cromatográficos e equipamentos utilizados foi reprodutível (R>0,95). Estas análises mostraram que foi possível desenvolver modelos de QSRR para um método cromatográfico e equipamento e transpô-los para outro equipamento seguindo o uso de substâncias em comum. O terceiro estudo produziu modelos com boa capacidade de predição (P2>0,81) utilizando alta amplitude de espaço químico e rigor estatístico. Conclui-se que, estas informações podem ser utilizadas como uma plataforma piloto para análises de dados com objetivo de auxiliar na desreplicação de extratos de plantas em estudos metabolômicos / After the emergence of the computing era with special application in chemistry, all substances from natural sources might have their information stored in databases. Therefore, the opportunity arises to employ natural product databases and some chemoinformatic tools such as QSRR studies to speed up the identification of substances from metabolomic studies. This paper proposes the development of three QSRR studies as well as the building of a database (AsterDB) with chemical structures from the Asteraceae family and related information (i.e.: botanical and taxonomic occurrences, biological activity, analytical information, etc.) aiming to assist the dereplication of substances in plant extracts. The first study was carried out with 39 sesquiterpene lactones (STLs) analysed using two different solvent systems (MeOH-H2O 55:45 and MeCN-H2O 35:65), three groups of structural descriptors (2D-descr, 3D-1conf, and 3D-weigh), two different sets for training and testing (26:13 and 29:10), four algorithms for selection of descriptors (best first, LFS, greedy stepwise, and GA), three different model sizes (four, five, and six descriptors) and two modelling methods (PLS and ANN). The second study was developed with 50 compounds of different chemical classification in order to assess the differences between individual and mixed compounds analysed in three different equipments and two chromatographic methods. The third was elaborated with 2,635 chemical structures with a common external test to all models (25%, n = 656), three separation methods for testing- and training-set (based on response and on 2D and 3D predictors partitions), three different sizes of models selected by GA and two modelling methods (MLR and BrNN). The AsterDB database was developed to be populated gradually and currently, it has about 2,000 chemical structures. The first QSRR study generated good models, able to estimate the logarithm of the retention factor (logk) of STLs with P2>0.81 for the MeCN-H2O system. The second study showed that there was no statistical difference between the substances analysed individually and mixed (p-value>0.95) and the correlation between the two chromatographic methods and equipments used was reproducible (R>0.95). These analyses showed that it was possible to develop QSRR models for a chromatographic method and equipment and translate them into other equipment following the use of substances in common. The third study produced models with good predictive capacity (P2>0.81) using a high range of chemical space and statistical accuracy. In conclusion, this information can be used as a pilot platform for data analysis in order to assist in plant dereplication in metabolomics studies

A doença de Chagas representa um grave problema de saúde em regiões endêmicas que vão desde o sul dos Estados Unidos até a Argentina. O protozoário tripanossomatídeo Trypanosoma cruzi é o agente causador dessa devastadora doença, que afeta milhões de pessoas. Existem em torno de 10 milhões de indivíduos contaminados e pelo menos 25 milhões de pessoas vivem em locais riscos de infecção. Os dois medicamentos, o nifurtimox e o benzonidazol, apresentam sérios efeitos colaterais além de se mostrarem ineficazes na fase crônica da doença. Esse triste perfil, felizmente, tem se alterado com recentes avanços que levaram o ravuconazol, pozaconazol e K11777 para estudos em fase clínica. Com base em seu papel fundamental no ciclo do T. cruzi, a sexta enzima da via glicolítica, a gliceraldeído 3-fosfato desidrogenase (GAPDH) vem sendo considerada um alvo promissor para a descoberta e o desenvolvimento de novos agentes quimioterápicos para o tratamento da doença de Chagas. É amplamente conhecida a importância do planejamento de compostos tanto por método baseado na estrutura do alvo quanto do ligante. A docagem molecular foi usada para a seleção inicial dos compostos para o teste biocalorimétrico, e a partir dessa estratégia foi possível, de 25 compostos. Os parâmetros cinéticos da catálise da TcGAPDH foram determinados (KM = 10,51 ± 0,91 µM, Vmax = 4,18 ± 0,09 x 10-4 mM s-1 e kcat = 85,88 ± 3,22 s-1). Os experimentos cinéticos por ITC possibilitou na identificação de cinco compostos bioativos, sendo três com constante de inibição abaixo de 100 µM (13,21 ± 0,88, 35,00 ± 1,70 e 78,45 ± 2,69 µM). Processos de simulação de dinâmica molecular foram aplicados para a predição do modo de interação dos três compostos com Ki app menores que 100 µM. / Chagas disease is a serious health problem in endemic regions ranging from the southern the United States to Argentina. The protozoan Trypanosoma cruzi is the causative agent of this devastating disease that affects millions of people. Exist about 10 million people infected and at least 25 million people live in risk of local infection. There are only two drugs used to treat Chagas disease during acute phase and it show harmful side effects. This gloomy outlook has changed due to major advances in research of anti-trypanosomatid agents; an example is posaconazole, ravuconazole and K11777 both, which currently is in clinical phase. A promising target that is receiving considerable attention is the enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH, EC 1.2.1.12) a key protein in the glycolytic pathway of trypanosomatids. SBVS methods were used for the selection of 25 compounds and these were assayed against GAPDH using Isothermal Titration Calorimetry. The kinetic parameters of catalysis were determined TcGAPDH (KM = 10.91 ± 0.91 µM, Vmax = 4.18 ± 0.09 x 10-4 mM s-1 and kcat = 85.88 ± 3.22 s-1). The kinetic experiments by ITC allowed the identification of five bioactive compounds, three with inhibition constant below 100 µM. Simulation process of molecular dynamics were applied to predict the mode of interaction for the three compounds with Kiapp less than 100 µM.

Com o surgimento da era computacional com especial aplicação em química, as substâncias de origem naturais puderam ter suas informações armazenadas em bancos de dados. Desta forma, surge a oportunidade de se empregar bancos de dados de produtos naturais e de algumas ferramentas de quimioinformática como os estudos de Quantitative Structure-Retention Relationship (QSRR) para acelerar a identificação de substâncias em estudos metabolômicos. Este trabalho propôs o desenvolvimento de três estudos de QSRR, bem como a construção de um banco de dados (AsterDB) com estruturas químicas da família Asteraceae e informações a elas associadas (ex.: ocorrências botânicas e taxonômicas, atividade biológica, informações analíticas etc.) para auxiliar a desreplicação de substâncias em extratos vegetais. O primeiro estudo foi elaborado com 39 lactonas sesquiterpênicas (LST) analisadas em dois diferentes sistemas de solventes (MeOH-H2O 55:45 e MeCN-H2O 35:65), três grupos de descritores estruturais (2D-descr, 3D-1conf e 3D-weigh), dois diferentes conjuntos para treino e teste (26:13 e 29:10), quatro algoritmos para seleção de descritores (best first, linear forward - LFS, greedy stepwise e algoritmo genético - GA), três diferentes tamanhos de modelos (quatro, cinco e seis descritores) e dois métodos de modelagem (mínimos quadrados parciais - PLS e redes neurais artificiais - ANN). O segundo foi desenvolvido com 50 substâncias de diferentes classes químicas com intuito de avaliar as diferenças entre substâncias analisadas individualmente e em mistura em três diferentes equipamentos e dois métodos cromatográficos. O terceiro foi elaborado com 2.635 estruturas químicas com um teste externo comum a todos os modelos (25%, n = 656), três métodos de separação para teste e treino (partição baseada na resposta e baseada nos preditores 2D e 3D), três diferentes tamanhos de modelos selecionados por GA e dois métodos de modelagem (MLR e redes neurais feed-forward com regularização bayesiana - BRNN). O banco de dados AsterDB foi desenvolvido para ser preenchido de forma gradual e atualmente possui cerca de 2.000 estruturas químicas. O primeiro estudo de QSRR gerou bons modelos capazes de estimar o logaritmo do fator de retenção (logk) das LST com P2>0,81 para o sistema MeCN-H2O. O segundo estudo mostrou que não houve diferença estatística entre as substâncias analisadas individualmente e em mistura (p-valor>0,95) e que a correlação entre os dois métodos cromatográficos e equipamentos utilizados foi reprodutível (R>0,95). Estas análises mostraram que foi possível desenvolver modelos de QSRR para um método cromatográfico e equipamento e transpô-los para outro equipamento seguindo o uso de substâncias em comum. O terceiro estudo produziu modelos com boa capacidade de predição (P2>0,81) utilizando alta amplitude de espaço químico e rigor estatístico. Conclui-se que, estas informações podem ser utilizadas como uma plataforma piloto para análises de dados com objetivo de auxiliar na desreplicação de extratos de plantas em estudos metabolômicos / After the emergence of the computing era with special application in chemistry, all substances from natural sources might have their information stored in databases. Therefore, the opportunity arises to employ natural product databases and some chemoinformatic tools such as QSRR studies to speed up the identification of substances from metabolomic studies. This paper proposes the development of three QSRR studies as well as the building of a database (AsterDB) with chemical structures from the Asteraceae family and related information (i.e.: botanical and taxonomic occurrences, biological activity, analytical information, etc.) aiming to assist the dereplication of substances in plant extracts. The first study was carried out with 39 sesquiterpene lactones (STLs) analysed using two different solvent systems (MeOH-H2O 55:45 and MeCN-H2O 35:65), three groups of structural descriptors (2D-descr, 3D-1conf, and 3D-weigh), two different sets for training and testing (26:13 and 29:10), four algorithms for selection of descriptors (best first, LFS, greedy stepwise, and GA), three different model sizes (four, five, and six descriptors) and two modelling methods (PLS and ANN). The second study was developed with 50 compounds of different chemical classification in order to assess the differences between individual and mixed compounds analysed in three different equipments and two chromatographic methods. The third was elaborated with 2,635 chemical structures with a common external test to all models (25%, n = 656), three separation methods for testing- and training-set (based on response and on 2D and 3D predictors partitions), three different sizes of models selected by GA and two modelling methods (MLR and BrNN). The AsterDB database was developed to be populated gradually and currently, it has about 2,000 chemical structures. The first QSRR study generated good models, able to estimate the logarithm of the retention factor (logk) of STLs with P2>0.81 for the MeCN-H2O system. The second study showed that there was no statistical difference between the substances analysed individually and mixed (p-value>0.95) and the correlation between the two chromatographic methods and equipments used was reproducible (R>0.95). These analyses showed that it was possible to develop QSRR models for a chromatographic method and equipment and translate them into other equipment following the use of substances in common. The third study produced models with good predictive capacity (P2>0.81) using a high range of chemical space and statistical accuracy. In conclusion, this information can be used as a pilot platform for data analysis in order to assist in plant dereplication in metabolomics studies

Le paludisme est causé par cinq espèces du genre Plasmodium, P. falciparum étant le plus mortel. Des résistances de certaines souches du parasite ont été rapportées pour tous les médicaments mis sur le marché. Les moustiques vecteurs du parasite sont résistants aux insecticides et aucun vaccin n'est disponible. Cette maladie est un problème économique et de santé publique pour les pays en voie de développement. Mes travaux de thèses visent à identifier de nouveaux traitements contre le paludisme, en ciblant deux nouvelles protéines. Les Apicomplexes ont développé un mécanisme unique d'invasion, impliquant une interaction forte entre la cellule hôte et la surface du parasite, appelée jonction mobile. La caractérisation structurale et fonctionnelle du complexe AMA1-RON2 a ouvert la voie à la découverte de petites molécules capables d'empêcher l'interaction AMA1-RON2 et de ce fait, l'invasion. Le parasite a aussi besoin de phospholipides pour construire sa membrane durant le cycle érythrocytaire. Il y a six fois plus de phospholipides dans les érythrocytes infectés que dans les érythrocytes sains. Notre stratégie est d'inhiber la voie de synthèse de novo Kennedy et plus précisément, son étape limitante catalysée par la PfCCT. Des filtres basés sur le ligand (LBVS) et sur la structure (SBVS) ont été utilisés pour tester virtuellement les chimiothèques commerciales que j'ai préparées. Pour chaque projet, des molécules ont été sélectionnées pour leurs scores de docking et les interactions qu'elles établissent avec les résidus clés de la protéine. En combinant la bioinformatique structurale et la chémoinformatique, nous avons identifié des inhibiteurs potentiels des deux cibles protéiques. / Human malaria is caused by five parasitic species of the genus Plasmodium, P. falciparum being the most deadly. Drug resistance of some parasite strains has been reported for commercial drugs. Vector mosquitoes are resistant to perythroid insecticides and no successful vaccine is available. This disease is a public and economic health issue for developing countries. My PhD projects investigate new treatments for malaria, by targeting two new proteins. Apicomplexa parasites have developed a unique invasion mechanism involving a tight interaction formed between the host cell and the parasite surfaces called Moving Junction. The structural and functional characterization of the AMA1-RON2 complex pave the way for the design of low molecular weight compounds capable of disrupting the AMA1-RON2 assembly and thereby invasion. The parasite also needs phospholipids to build its membrane during the erythrocytic cycle. There are six times more phospholipids in infected erythrocytes compared to healthy ones. Our strategy is to inhibit the de novo Kennedy pathway and more precisely its rate-limiting step catalysed by the enzyme PfCCT. Filters were used for ligand-based (LBVS) and structure-based virtual screening (SBVS) of commercial chemical databases that I have prepared. For each project, molecules were selected in terms of their docking scores and their interactions with key active site residues. By combining structural bioinformatics and cheminformatics, we identified potential inhibitors of the two protein targets.