Over the last twenty years, fragment-based drug discovery (FBDD) has emerged as a highly successful way to provide lead compounds for subsequent optimisation into drug candidates. Initial hits usually exhibit lower potency than those identified by more traditional techniques, such as High-Throughput Screening (HTS), but the optimisation phase of FBDD is highly efficient, thus providing superior lead-like compounds. The recent application of FBDD in a variety of protein kinase campaigns has successfully led to the identification of novel binding sites and highly efficient chemical ligands. This demonstrates the utility of the FBDD strategy against well-established kinase targets, where selectivity is otherwise challenging due to significant conservation of the ATP-binding site. Protein kinase CK2 is a ubiquitously expressed and constitutively active regulator of cell growth, proliferation and apoptosis. Elevated levels of CK2 protein and activity have historically been involved in human cancer, including lung, cervical and head and neck cancer types, and its overexpression is associated with worse prognosis. A number of CK2 inhibitors are currently available displaying activity against multiple cancers in vitro and in the clinic, however the majority of these candidates target the ATP-binding site and thus display poor selectivity in kinase panel assays. Here we explore the application of FBDD towards the development of potent and selective inhibitors of the catalytic α-subunit of CK2. This project exploits a novel, conserved binding site, named the αD pocket, for the generation of allosteric inhibitor molecules. Following structure-based optimisation of a potent inhibitor series, and characterisation of a previously unreported binding mode, a fragment linking strategy between the lead αD-site fragment and a low-affinity pseudosubstrate peptide is investigated. This work validates the utility of FBDD towards the discovery of new binding modes, presents a first in class CK2α allosteric inhibitor series and provides the first X-ray crystal structure of protein kinase CK2 in complex with a ligand binding in the substrate-binding channel.

Doenças causadas por agentes infecciosos e parasitários são chamadas negligenciadas por não despertarem interesse das indústrias farmacêuticas para o desenvolvimento de novas alternativas terapêuticas. Essas doenças são responsáveis por levar milhões de pessoas à morte todos os anos e afetam principalmente os países pobres e em desenvolvimento. Dentre estas, a doença de Chagas e as leishmanioses, parasitoses causadas por parasitas flagelados pertencentes à família Trypanosomatidae, T. cruzi e Leishmaina sp., respectivamente, se apresentam como um sério problema de saúde pública mundial. Endêmicas em vários países e causando milhões de mortes anualmente, ainda hoje não existem fármacos eficientes e seguros para o tratamento dessas doenças. Este panorama torna eminente a necessidade de pesquisa e desenvolvimento de novos fármacos para essas parasitoses. A busca por agentes quimioterápicos envolve a seleção de vias metabólicas essenciais à sobrevivência dos parasitas. Dentre estas, destacamse cisteíno-proteases presentes nesses tripanossomatídeos, deste modo a cruzaína no T. cruzi, e a CPB2.8 na Leishmania mexicana, se mostram como alvos bioquímicos promissores. A disponibilidade de estruturas cristalográficas da cruzaína e do sequenciamento genômico da CPB2.8, nos permite utilizar estratégias de planejamento de fármacos baseado no receptor (SBDD) na identificação de candidatos a fármacos para essas doenças. Entre as técnicas modernas de SBDD utilizadas, a triagem virtual possibilita identificar promissores candidatos a novos fármacos. Assim neste trabalho, obteve-se por meio da técnica de modelagem comparativa o modelo da enzima CPB2.8 de L. mexicana, visto a indisponibilidade da estrutura cristalográfica no Protein Data Bank (PDB). De modo a refinar o modelo construído realizou-se a simulação por dinâmica molecular de 100ns, apresentando estabilização a partir de 80ns. A simulação por dinâmica molecular foi validada por meio do gráfico de Ramachandran, gráfico de raio de giro, RMSD, gráfico de superfície hidrofóbica. Foram calculados os mapas de interação molecular no programa GRID das seguintes proteínas: cruzaína, CPB2.8, catepsina B e catepsina L, e, posteriormente, foi construído um modelo farmacofórico baseado no sítio ativo das enzimas cruzaína e CPB2.8. O modelo farmacofórico da cruzaína foi validado por curva ROC apresentando valor de AUC 61%. A triagem virtual foi realizada para ambas as proteínas e foram obtidos 369 compostos para a cuzaína e 225 compostos para a CPB2.8. Foi realizado o ancoramento molecular desses compostos obtidos pela triagem virtual a fim de diminuir a quantidade de compostos a serem avaliados experimentalmente. / Neglected diseases are caused by parasites and infectious agents and affect mainly people in poor areas being prevalent in 149 countries and causing 534,000 deaths per year. Among neglected diseases we can highlight Chagas Disease and Leishmaniasis, both have a high rate of morbidity and mortality and both are addressed in this project in the search of new drugs against a NTD. Nowadays, the search for new drugs involves the selection of biological pathways essential for parasite survival, in this class of parasites we can suggest the cysteine proteases, a proteases family present in Trypanosoma cruzi and and Leishmania ssp. In order to obtain a new agent against Neglected Disease in this work was obtained the model of the enzyme CPB2.8 of L. mexicana using the comparative modeling technique, due to the unavailability of the crystallographic structure in the Protein Data Bank (PDB). In order to refine the constructed model was performed the molecular dynamics simulation of 100ns, stabilization was achieved from 80ns. Molecular dynamics simulation was validated using the Ramachandran graph, radius of rotation graph, RMSD, hydrophobic surface area graph. The molecular interaction fields were calculated in the GRID program to cruzain, CPB2.8, cathepsin B and cathepsin L. Based on molecular interaction fields generated pharmacophoric models were constructed using information about the active site of the enzymes cruzain and CPB2.8. The pharmacophoric model of cruzain was validated by ROC curve presenting AUC value of 61%. Virtual screening was performed for both proteins and 369 compounds were obtained for cuzain and 225 compounds for CPB2.8. Docking studies of these compounds was performed in order to decrease the amount of compounds to be evaluated experimentally.

A doença de Chagas, causada pelo parasita Trypanosoma cruzi, acomete entre 6 a 8 milhões de pessoas em todo o mundo. Conhecida como tripanossomíase americana, por ter sido considerada endêmica apenas na América Latina, esta doença, se espalhou para outros continentes devido aos movimentos migratórios se tornando um problema de sáude mundial. Estima-se que 56.000 novos casos e cerca de 12.000 mortes por complicações relacionadas à doença de Chagas anualmente. A quimioterapia disponível para o tratamento é composta apenas por dois fármacos, nifurtimox e benznidazol, no entanto são pouco eficazes na fase crônica da doença. Estes fármacos apresentarem, ainda, efeitos adversos graves e resistência por parte de algumas cepas do parasita. Diante deste panorama, é iminente a necessidade da busca de novos fármacos contra T. cruzi. Para a busca racional de novos quimiterapicos antiparasitários é fundamental a identificação e caracterização de vias metabólicas essenciais à sobrevivência dos parasitas. Assim, a enzima sirtuína 2 - Silent Information Regulator 2 (Sir2), tem importante papel para a infecção por T. cruzi, pois está totalmente envolvida no seu ciclo celular do parasita. Esta é uma enzima NAD+ dependente da classe III histona desacetilases, e se mostra como um interessante alvo bioquímico para o desenvolvimento de antichagásicos. A disponibilidade do sequenciamento genômico da Sir2 nos permite utilizar estratégias de planejamento de fármaco baseado no receptor (SBDD - Structure Based Drug Design) na identificação de candidatos a fármacos para essa doença. Entre as técnicas modernas de SBDD utilizadas, a triagem virtual possibilita identificar e selecionar inibidores enzimáticos potentes e seletivos para o alvo escolhido. Assim, neste trabalho, foi construído por meio da técnica de modelagem comparativa o modelo da enzima Sir2 de T. cruzi. Uma simulação por dinâmica molecular de 200ns, foi realizada para averiguar a estabilidade do modelo obtido. Diante da estabilização do modelo a partir de 100ns, o mesmo foi validado utilizando análise de clusters, RMSD (Root-mean-square Deviation) e análises de frequência de ligações de hidrogênio com o Cofator (NAD+) e os aminoácidos do sítio de catálise foram observadas, estes passos de simulação e validação foram realizados no programa DESMOND. Com o modelo robusto, os campos de interações moleculares (MIFs) foram gerados no programa GRID (Molecular Discovery v2.1) com o intuito de elucidar as regiões favoráveis a interação com a enzima em relação a propriedades físico-químicas da Sir2. A partir dos MIFs favoráveis a Sir2 de T. cruzi foi possível a construção de dois modelos farmacofóricos, o qual se baseou nas interações do Cofator (NAD+) e o sítio de catálise (Nicotinamida). O mesmo foi apliacdo como filtro para Triagem Virtual no programa UNITY da plataforma SYBYL X 2.0, utilizando os bancos de dados ZINC15 e GSK. A triagem resultou na seleção de 8 compostos candidatos a inibidores. Destes foram adquiridos 6 compostos por serem considerados mais promissores devido a complementariedade molecular. Estes foram testados contra a enzima de T. cruzi Sri2. Após o ensaio foi possível avaliar a potência de 4 compostos, sendo o composto CDMS-01 (IC50 = 39,9uM) o mais promissor que será submetido à processos de otimização molecular. / Chagas disease, caused by the parasite Trypanosoma cruzi, affects between 6 and 8 million people worldwide. Also known as American trypanosomiasis, because it is considered endemic only in Latin America, but has spread to other continents due to migratory movements. It is estimated that 56,000 new cases and about 12,000 deaths from complications related to Chagas disease annually. The chemotherapy available for treatment consists of only two drugs, nifurtimox and benznidazole, however these are poorly effective in the chronic phase. These drugs also have serious adverse effects and resistance from strains of the parasite. Faced with this scenario, the need to search for new drugs against T. cruzi is imminent. For the drug planning for new antiparasitic chemotherapics, the identification and characterization of metabolic pathways essential to the survival of parasites is fundamental. Therewith, the sirtuin 2 - Silent Information Regulator 2 (Sir2) enzyme has an important role for T. cruzi infection, since Sir2 in the parasite is totally involved in its cell cycle. This is an NAD+-dependent enzyme of class III histone deacetylases, and it shows an interesting biochemical target for the development of antichagasic. The availability of Sir2 genomic sequencing allows us to use SBDD (Structure Based Drug Design) strategies in identifying drug candidates for this disease. Among the modern techniques of SBDD used, virtual screening makes it possible to identify and select potent and selective enzyme inhibitors for the chosen target. The model of the T. cruzi Sir2 enzyme was constructed using the comparative modeling technique. A molecular dynamics simulation of 200ns was performed to ascertain the stability of the obtained model. Considering the stabilization of the model from 100ns, it was validated using cluster analysis, Root-mean-square Deviation (RMSD) and hydrogen bond frequency analyzes with Cofator (NAD+) and the amino acids of the catalysis site were observed, these simulation and validation steps were performed in the DESMOND program. With the robust model, the molecular interaction fields (MIFs) were generated in the GRID program (Molecular Discovery v2.1) in order to elucidate the regions favorable to the interaction with the enzyme in relation to the physicalchemical properties of Sir2. From the MIFs favorable to Sir2 of T. cruzi it was possible to construct two pharmacophoric models, which was based on the interactions of Cofator (NAD+) and the catalysis site (Nicotinamide). It was also applied as a Virtual screening filter in the UNITY program of the SYBYL X 2.0 platform, using the ZINC15 and GSK databases. Screening resulted in the selection of 8 inhibitor candidate compounds. Six compounds were obtained from the screening, because they were considered more promising, and were tested against T. cruzi Sri2 enzyme. After the assay it was possible to evaluate the potency of 4 compounds, the most promising compound being CDMS-01 (IC50 = 39.9 µM) that will be submitted to molecular optimization processes.

Doenças causadas por agentes infecciosos e parasitários são chamadas negligenciadas por não despertarem interesse das indústrias farmacêuticas para o desenvolvimento de novas alternativas terapêuticas. Essas doenças são responsáveis por levar milhões de pessoas à morte todos os anos e afetam principalmente os países pobres e em desenvolvimento. Dentre estas, a doença de Chagas e as leishmanioses, parasitoses causadas por parasitas flagelados pertencentes à família Trypanosomatidae, T. cruzi e Leishmaina sp., respectivamente, se apresentam como um sério problema de saúde pública mundial. Endêmicas em vários países e causando milhões de mortes anualmente, ainda hoje não existem fármacos eficientes e seguros para o tratamento dessas doenças. Este panorama torna eminente a necessidade de pesquisa e desenvolvimento de novos fármacos para essas parasitoses. A busca por agentes quimioterápicos envolve a seleção de vias metabólicas essenciais à sobrevivência dos parasitas. Dentre estas, destacamse cisteíno-proteases presentes nesses tripanossomatídeos, deste modo a cruzaína no T. cruzi, e a CPB2.8 na Leishmania mexicana, se mostram como alvos bioquímicos promissores. A disponibilidade de estruturas cristalográficas da cruzaína e do sequenciamento genômico da CPB2.8, nos permite utilizar estratégias de planejamento de fármacos baseado no receptor (SBDD) na identificação de candidatos a fármacos para essas doenças. Entre as técnicas modernas de SBDD utilizadas, a triagem virtual possibilita identificar promissores candidatos a novos fármacos. Assim neste trabalho, obteve-se por meio da técnica de modelagem comparativa o modelo da enzima CPB2.8 de L. mexicana, visto a indisponibilidade da estrutura cristalográfica no Protein Data Bank (PDB). De modo a refinar o modelo construído realizou-se a simulação por dinâmica molecular de 100ns, apresentando estabilização a partir de 80ns. A simulação por dinâmica molecular foi validada por meio do gráfico de Ramachandran, gráfico de raio de giro, RMSD, gráfico de superfície hidrofóbica. Foram calculados os mapas de interação molecular no programa GRID das seguintes proteínas: cruzaína, CPB2.8, catepsina B e catepsina L, e, posteriormente, foi construído um modelo farmacofórico baseado no sítio ativo das enzimas cruzaína e CPB2.8. O modelo farmacofórico da cruzaína foi validado por curva ROC apresentando valor de AUC 61%. A triagem virtual foi realizada para ambas as proteínas e foram obtidos 369 compostos para a cuzaína e 225 compostos para a CPB2.8. Foi realizado o ancoramento molecular desses compostos obtidos pela triagem virtual a fim de diminuir a quantidade de compostos a serem avaliados experimentalmente. / Neglected diseases are caused by parasites and infectious agents and affect mainly people in poor areas being prevalent in 149 countries and causing 534,000 deaths per year. Among neglected diseases we can highlight Chagas Disease and Leishmaniasis, both have a high rate of morbidity and mortality and both are addressed in this project in the search of new drugs against a NTD. Nowadays, the search for new drugs involves the selection of biological pathways essential for parasite survival, in this class of parasites we can suggest the cysteine proteases, a proteases family present in Trypanosoma cruzi and and Leishmania ssp. In order to obtain a new agent against Neglected Disease in this work was obtained the model of the enzyme CPB2.8 of L. mexicana using the comparative modeling technique, due to the unavailability of the crystallographic structure in the Protein Data Bank (PDB). In order to refine the constructed model was performed the molecular dynamics simulation of 100ns, stabilization was achieved from 80ns. Molecular dynamics simulation was validated using the Ramachandran graph, radius of rotation graph, RMSD, hydrophobic surface area graph. The molecular interaction fields were calculated in the GRID program to cruzain, CPB2.8, cathepsin B and cathepsin L. Based on molecular interaction fields generated pharmacophoric models were constructed using information about the active site of the enzymes cruzain and CPB2.8. The pharmacophoric model of cruzain was validated by ROC curve presenting AUC value of 61%. Virtual screening was performed for both proteins and 369 compounds were obtained for cuzain and 225 compounds for CPB2.8. Docking studies of these compounds was performed in order to decrease the amount of compounds to be evaluated experimentally.

A doença de Chagas, causada pelo parasita Trypanosoma cruzi, acomete entre 6 a 8 milhões de pessoas em todo o mundo. Conhecida como tripanossomíase americana, por ter sido considerada endêmica apenas na América Latina, esta doença, se espalhou para outros continentes devido aos movimentos migratórios se tornando um problema de sáude mundial. Estima-se que 56.000 novos casos e cerca de 12.000 mortes por complicações relacionadas à doença de Chagas anualmente. A quimioterapia disponível para o tratamento é composta apenas por dois fármacos, nifurtimox e benznidazol, no entanto são pouco eficazes na fase crônica da doença. Estes fármacos apresentarem, ainda, efeitos adversos graves e resistência por parte de algumas cepas do parasita. Diante deste panorama, é iminente a necessidade da busca de novos fármacos contra T. cruzi. Para a busca racional de novos quimiterapicos antiparasitários é fundamental a identificação e caracterização de vias metabólicas essenciais à sobrevivência dos parasitas. Assim, a enzima sirtuína 2 - Silent Information Regulator 2 (Sir2), tem importante papel para a infecção por T. cruzi, pois está totalmente envolvida no seu ciclo celular do parasita. Esta é uma enzima NAD+ dependente da classe III histona desacetilases, e se mostra como um interessante alvo bioquímico para o desenvolvimento de antichagásicos. A disponibilidade do sequenciamento genômico da Sir2 nos permite utilizar estratégias de planejamento de fármaco baseado no receptor (SBDD - Structure Based Drug Design) na identificação de candidatos a fármacos para essa doença. Entre as técnicas modernas de SBDD utilizadas, a triagem virtual possibilita identificar e selecionar inibidores enzimáticos potentes e seletivos para o alvo escolhido. Assim, neste trabalho, foi construído por meio da técnica de modelagem comparativa o modelo da enzima Sir2 de T. cruzi. Uma simulação por dinâmica molecular de 200ns, foi realizada para averiguar a estabilidade do modelo obtido. Diante da estabilização do modelo a partir de 100ns, o mesmo foi validado utilizando análise de clusters, RMSD (Root-mean-square Deviation) e análises de frequência de ligações de hidrogênio com o Cofator (NAD+) e os aminoácidos do sítio de catálise foram observadas, estes passos de simulação e validação foram realizados no programa DESMOND. Com o modelo robusto, os campos de interações moleculares (MIFs) foram gerados no programa GRID (Molecular Discovery v2.1) com o intuito de elucidar as regiões favoráveis a interação com a enzima em relação a propriedades físico-químicas da Sir2. A partir dos MIFs favoráveis a Sir2 de T. cruzi foi possível a construção de dois modelos farmacofóricos, o qual se baseou nas interações do Cofator (NAD+) e o sítio de catálise (Nicotinamida). O mesmo foi apliacdo como filtro para Triagem Virtual no programa UNITY da plataforma SYBYL X 2.0, utilizando os bancos de dados ZINC15 e GSK. A triagem resultou na seleção de 8 compostos candidatos a inibidores. Destes foram adquiridos 6 compostos por serem considerados mais promissores devido a complementariedade molecular. Estes foram testados contra a enzima de T. cruzi Sri2. Após o ensaio foi possível avaliar a potência de 4 compostos, sendo o composto CDMS-01 (IC50 = 39,9uM) o mais promissor que será submetido à processos de otimização molecular. / Chagas disease, caused by the parasite Trypanosoma cruzi, affects between 6 and 8 million people worldwide. Also known as American trypanosomiasis, because it is considered endemic only in Latin America, but has spread to other continents due to migratory movements. It is estimated that 56,000 new cases and about 12,000 deaths from complications related to Chagas disease annually. The chemotherapy available for treatment consists of only two drugs, nifurtimox and benznidazole, however these are poorly effective in the chronic phase. These drugs also have serious adverse effects and resistance from strains of the parasite. Faced with this scenario, the need to search for new drugs against T. cruzi is imminent. For the drug planning for new antiparasitic chemotherapics, the identification and characterization of metabolic pathways essential to the survival of parasites is fundamental. Therewith, the sirtuin 2 - Silent Information Regulator 2 (Sir2) enzyme has an important role for T. cruzi infection, since Sir2 in the parasite is totally involved in its cell cycle. This is an NAD+-dependent enzyme of class III histone deacetylases, and it shows an interesting biochemical target for the development of antichagasic. The availability of Sir2 genomic sequencing allows us to use SBDD (Structure Based Drug Design) strategies in identifying drug candidates for this disease. Among the modern techniques of SBDD used, virtual screening makes it possible to identify and select potent and selective enzyme inhibitors for the chosen target. The model of the T. cruzi Sir2 enzyme was constructed using the comparative modeling technique. A molecular dynamics simulation of 200ns was performed to ascertain the stability of the obtained model. Considering the stabilization of the model from 100ns, it was validated using cluster analysis, Root-mean-square Deviation (RMSD) and hydrogen bond frequency analyzes with Cofator (NAD+) and the amino acids of the catalysis site were observed, these simulation and validation steps were performed in the DESMOND program. With the robust model, the molecular interaction fields (MIFs) were generated in the GRID program (Molecular Discovery v2.1) in order to elucidate the regions favorable to the interaction with the enzyme in relation to the physicalchemical properties of Sir2. From the MIFs favorable to Sir2 of T. cruzi it was possible to construct two pharmacophoric models, which was based on the interactions of Cofator (NAD+) and the catalysis site (Nicotinamide). It was also applied as a Virtual screening filter in the UNITY program of the SYBYL X 2.0 platform, using the ZINC15 and GSK databases. Screening resulted in the selection of 8 inhibitor candidate compounds. Six compounds were obtained from the screening, because they were considered more promising, and were tested against T. cruzi Sri2 enzyme. After the assay it was possible to evaluate the potency of 4 compounds, the most promising compound being CDMS-01 (IC50 = 39.9 µM) that will be submitted to molecular optimization processes.

A esquistossomose é uma das principais causas de morbidade em países Tropicais e Subtropicais, gerando graves consequências socioeconômicas. Atualmente, os fármacos disponíveis para o tratamento da desta doença são praziquantel e oxamniquina, porém relatos de baixa susceptibilidade do parasita a esses medicamentos sugerem a necessidade de novas estratégias terapêuticas para o tratamento da doença. Todavia, existe pouco interesse da indústria farmacêutica no desenvolvimento de fármacos contra doenças tropicais e negligenciadas, entre as quais se encontra a esquistossomose. Devido a estes fatores, o presente trabalho teve por objetivo geral utilizar ferramentas computacionais para identificar inibidores da SmDHFR candidatos a novos fármacos. Avaliou-se as características exclusivas para a proteína de S. mansoni por meio de uma análise das sequências FASTA em comparação com a DHFR de outros organismos. A fim de garantir a ação seletiva dessas moléculas frente a enzima do parasita, os campos moleculares de interação seletivos para SmDHFR foram calculados e empregados na construção do modelo farmacofórico, o qual foi utilizado na triagem virtual de inibidores de SmDHFR. Os estudos computacionais realizados nos permitiram a seleção de 20 moléculas com uma boa complementariedade com o modelo farmacofórico gerado e com potencial para serem inibidores de SmDHFR. / Schistosomiasis is one of morbidity\'s main causes in tropical and subtropical countries, which leads to serious socioeconomic consequences. Praziquantel and oxamniquina are the drugs currently available for treating this disease, but reports points that the parasite has been resistant to both drugs, which suggests the need for new therapeutic strategies for the treatment of this disease. However, there is little interest in the pharmaceutical industry in developing drugs against neglected tropical diseases, including schistosomiasis. Due to these factors, the present work has the general objective to use computational tools to identify SmDHFR inhibitors which could be good candidates for developing new drugs. Evaluation of the exclusive characteristics of the S. mansoni protein were performed by FASTA sequence analyses in comparison to DHFR from other organisms. In order to guarantee the selective action of these molecules against the parasite enzyme, the molecular interaction fields selective for SmDHFR were calculated and used in the construction of the pharmacophoric model, which was further used in the virtual screening of SmDHFR inhibitors. Computational studies were performed and those led us to 20 molecules with a good complementarity with the pharmacophoric model that was previously generated and with potential to be SmDHFR inhibitors.

A esquistossomose é uma das principais causas de morbidade em países Tropicais e Subtropicais, gerando graves consequências socioeconômicas. Atualmente, os fármacos disponíveis para o tratamento da desta doença são praziquantel e oxamniquina, porém relatos de baixa susceptibilidade do parasita a esses medicamentos sugerem a necessidade de novas estratégias terapêuticas para o tratamento da doença. Todavia, existe pouco interesse da indústria farmacêutica no desenvolvimento de fármacos contra doenças tropicais e negligenciadas, entre as quais se encontra a esquistossomose. Devido a estes fatores, o presente trabalho teve por objetivo geral utilizar ferramentas computacionais para identificar inibidores da SmDHFR candidatos a novos fármacos. Avaliou-se as características exclusivas para a proteína de S. mansoni por meio de uma análise das sequências FASTA em comparação com a DHFR de outros organismos. A fim de garantir a ação seletiva dessas moléculas frente a enzima do parasita, os campos moleculares de interação seletivos para SmDHFR foram calculados e empregados na construção do modelo farmacofórico, o qual foi utilizado na triagem virtual de inibidores de SmDHFR. Os estudos computacionais realizados nos permitiram a seleção de 20 moléculas com uma boa complementariedade com o modelo farmacofórico gerado e com potencial para serem inibidores de SmDHFR. / Schistosomiasis is one of morbidity\'s main causes in tropical and subtropical countries, which leads to serious socioeconomic consequences. Praziquantel and oxamniquina are the drugs currently available for treating this disease, but reports points that the parasite has been resistant to both drugs, which suggests the need for new therapeutic strategies for the treatment of this disease. However, there is little interest in the pharmaceutical industry in developing drugs against neglected tropical diseases, including schistosomiasis. Due to these factors, the present work has the general objective to use computational tools to identify SmDHFR inhibitors which could be good candidates for developing new drugs. Evaluation of the exclusive characteristics of the S. mansoni protein were performed by FASTA sequence analyses in comparison to DHFR from other organisms. In order to guarantee the selective action of these molecules against the parasite enzyme, the molecular interaction fields selective for SmDHFR were calculated and used in the construction of the pharmacophoric model, which was further used in the virtual screening of SmDHFR inhibitors. Computational studies were performed and those led us to 20 molecules with a good complementarity with the pharmacophoric model that was previously generated and with potential to be SmDHFR inhibitors.

Locating a ligand-binding site is an important first step in structure-guided drug discovery, but current methods typically assess the pocket as a whole, doing little to suggest which regions and interactions are the most important for binding. This thesis introduces Fragment Hotspot Maps, a grid-based method that samples atomic propensities derived from interactions in the Cambridge Structural Database (CSD) with simple molecular probes. These maps specifically highlight fragment-binding sites and their corresponding pharmacophores, offering more precision over other binding site prediction methods. The method is validated by scoring the positions of 21 fragment and lead pairs. Fragment atoms are found in the highest scoring parts of the map corresponding to their atom type, with a median percentage rank of 98%. This is reduced to 72% for lead atoms, showing that the method can differentiate between the hotspots, and the warm spots later used during fragment elaboration. For ligand-bound structures, they provide an intuitive visual guide within the binding site, directing medicinal chemists where to grow the molecule and alerting them to suboptimal interactions within the original hit. These calculations are easily accessible through a simple to use web application, which only requires an input PDB structure or code. High scoring specific interactions predicted by the Fragment Hotspot Maps can be used to guide existing computer aided drug discovery methods. The Hotspots Python API has been created to allow these work flows to be executed programmatically through a single Python script. Two of the functions use scores from the Fragment Hotspot Maps to guide virtual screening methods, docking and field-based ligand screening. Docking virtual screening performance is improved by using a constraint selected from the highest scoring polar interaction. The field-based ligand screener uses modified versions of the Fragment Hotspot Maps directly to predict and score the binding pose. This workflow gave comparable results to docking, and for one target, Glucocorticoid receptor (GCR), showed much better results, highlighting its potential as an orthogonal approach. Fragment Hotspot Maps can be used at multiple stages of the drug discovery process, and research into these applications is ongoing. Their utility in the following areas are currently being explored: to assess ligandability for both individual structures and across proteomes, to aid in library design, to assess pockets throughout a molecular dynamics trajectory, to prioritise crystallographic fragment hits and to guide hit-to-lead development.

Le cancer représente l'un des problèmes majeurs en santé publique. Jusqu'à présent, en parallèle de l'intervention chirurgical, plusieurs traitements ont été mis au point et largement utilisés en thérapie clinique telles que les chimiothérapies. Cependant, leur efficacité est parfois limitée et couplée à des effets secondaires très néfastes, laissant les patients dans une impasse thérapeutique. Par conséquent, de nouvelles approches thérapeutiques doivent être développées sur de nouvelles cibles avérées en oncologie afin d'apporter des soins personnalisés aux patients. La première partie de mon travail de thèse a été dédiée à la compréhension des mécanismes moléculaires de la nucléotidase cytosolique de type II (cN-II), une enzyme du métabolisme des purines dont l'implication dans des phénomènes de résistance à des traitements anticancéreux a pu être démontrée. Aussi, une étude sur la cinétique enzymatique à l'état pré-stationnaire et stationnaire a été entreprise sur la forme sauvage et une forme mutée de l'enzyme lui conférant une activité accrue fortement impliquée dans les cas de résistance. Par cette approche, il a été possible de décortiquer le mécanisme cinétique, de définir l'étape cinétiquement limitant afin d'identifier les intermédiaires prépondérants de la réaction pouvant être ciblés pour le développement de nouveaux inhibiteurs. Cette étude cinétique est présentée dans ce premier volet de la thèse. En second lieu, mon travail s'est focalisée sur un second membre de cette famille d'enzyme qui est l'ecto-5'-nucléotidase (CD73). Cette enzyme exprimée sous forme dimérique à la surface extracellulaire régule la concentration en adénosine extracellulaire (par hydrolyse de l'adénosine monophosphate), ce dernier étant un puissant immunosuppresseur de la réponse immune anticancéreuse. L'objectif de mon travail de thèse a été de développer de nouveaux inhibiteurs de type allostérique en utilisant une approche basée sur la structure tridimensionnelle et la dynamique moléculaire. Une des étapes clés a été tout d'abord de mettre au point un système expression hétérologue afin d'obtenir l'enzyme recombinante en quantité suffisante pour les études enzymatiques ultérieures. Différents systèmes d'expression ont été testés et seul le système en cellules d'insecte infectées par le baculovirus a permis d'obtenir l'enzyme active en grande quantité. En parallèle, une étude in silico a permis de reproduire la dynamique fonctionnelle de l'enzyme requise pour sa fonction. A partir de ses données, un criblage virtuel d'une chimiothèque de 324 000 molécules a été réalisé sur le site de dimérisation et a permis d'identifier 33 composés chefs de files. Parmi, ces composés, dix molécules se sont avérés être de puissants inhibiteurs de CD73 (Ki < 1 µM) avec un mécanisme d'inhibition de type allostérique ou non-compétitif. La cytotoxicité des composés a été évaluée sur des lignées cellulaires transformées ou tumorales montrant un effet uniquement à des concentrations très élevées (supérieures à 100 µM). L'étude des relations structure-fonction devrait permettre à présent de proposer de voies d'optimisation afin d'améliorer l'efficacité des composés les plus actifs afin d'aboutir à de nouveaux candidats médicaments. / Cancer burden still remains a major worldwide health problem. To date, several types of conventional anticancer treatments are widely used in clinical. However, the alternative effects of these treatments often leave patients impaired. Therefore, it is required to understand the unique medical needs of individual patients and to conduct effective, high–quality research focusing on the not yet identified oncotargets.The first part of my thesis is dedicated to decipher molecular basis of cN-II reaction. This study characterizes the steady state and transient state kinetics of cN-II wild type and hyperactive mutant which involved in cancer treatment resistance. Furthermore, the characterization of the rate-limiting step and reaction intermediates gave insights into the binding mechanisms and the development of small molecules inhibitors of cN-II.In the second part of this work, we aimed to investigate allosteric inhibitors of CD73 using structure-based drug design approach. In this study the suitable protein expression system was established for the production of sufficient quantities of fully active CD73. This work followed by in silico studies, including molecular dynamics, virtual screening, and hits identification and in vitro hits validations and kinetics characterizations. The cytotoxicity of the most powerful inhibitors exhibited on different cell types was determined. SAR studies gave insights into the binding mode of best compounds and function.

<p>G-protein coupled receptors (GPCRs) are membrane proteins that constitute ~30% of the FDA-approved drug targets. Opioid receptors are a subtype of GPCRs with four different receptor types: delta, kappa, mu, and nociception opioid receptors. Opioids such as morphine have been used for thousands of years and are deemed the most effective method for treating pain. However, opioids can have detrimental effects if used illicitly or over an extended period of time. Intriguingly, most of the clinically used opioids act on the mu opioid receptor (µOR). Hence, efforts in recent decades have focused on other opioid receptors to treat pain and other disorders. The delta opioid receptor (δOR) is one of four opioid receptors expressed in the central and peripheral nervous system. The δOR has attracted much attention as a potential target for a multitude of diseases and disorders including substance and alcohol use disorders, ischemia, migraine, and neurodegenerative diseases. However, to date, no δOR agonists, or drugs that act directly at the δOR, have been successful as clinical candidates. Nonetheless, the therapeutic potential of the δOR necessitates the targeting its pharmacologically. In this dissertation, I highlight peptide-based modulation as well as the identification of novel agonists at the δOR. I report research findings in the context of biased agonism at δOR, which is a hypothesized cellular signaling mechanism with potential therapeutic benefits. The focus on this work is the molecular determinants of biased agonism, which were investigated using a combination of cellular and computational approaches.  </p>