Orientador: Fátima Pereira de Souza / Banca: Karina Alves de Toledo / Banca: Tereza Cristina Cardoso / Resumo: As infecções respiratórias agudas (IRAs) constituem a principal causa de mortalidade infantil no mundo, e o Vírus Respiratório Sincicial Humano (hRSV - Human Respiratory Syncytial Virus) é um dos principais agentes etiológicos das IRAs. Este vírus pertencente à família Paramyxoviridae, é envelopado, de simetria helicoidal, cujo genoma é RNA de fita simples não segmentada. A infectividade do vírus está relacionada com suas proteínas de membrana e dentre elas a glicoproteína G, que é responsável pela ligação do vírus à célula hospedeira e conseqüente instalação da infecção. Esta glicoproteína exerce um importante papel como antígeno de reconhecimento, sendo alvo para identificação do RSV através de anticorpos. Existem evidências de que esta proteína se liga a receptores glicosilados na célula hospedeira, porém ainda não foi descrito um receptor para a proteína G na célula. Para elucidar estes mecanismos de interação, foram realizados estudos experimentais e teóricos desta proteína. Os domínios solúveis da região N-terminal (1 a 38 aa) e C-terminal (67 a 298 aa), com 231 aminoácidos da glicoproteína G do hRSV foram clonados e a região N-terminal foi expressa em bactéria BL21 pLysS. Em paralelo, foi realizada a caracterização teórica desta proteína, e foram avaliados os possíveis sítios de interação da mesma com glicosaminoglicanos (heparina). Foram obtidos dois modelos teóricos para a proteína G do hRSV, bem como dois modelos de interação com heparina, determinando portanto, um possível sítio de ocorrência de interação. O conhecimento da estrutura da proteína G é de grande importância para elucidar a composição da estrutura e os mecanismos de interação com potenciais ligantes e deste modo, em um passo posterior, propor mecanismos de reconhecimento celular pelo hRSV, através de glicosaminoglicanos / Abstract: Acute Respiratory Infections (ARI) are the leading cause of infant mortality in the world, and the Human Respiratory Syncytial Virus (hRSV) is one of the main agents of ARI. This virus belongs to Paramyxoviridae family, has a lipidic envelope, helical symmetry and its genome is a single-stranded RNA. The viral infectivity is related to its membrane proteins and among them the G glycoprotein, which is responsible for binding the virus to the host cell and consequent infection. This glycoprotein plays an important role as antigen recognition, being the target for hRSV identification through antibodies. There are evidences that this protein binds to host cell glycosylated receptors, but it has not been described a receptor for G protein in the cell yet. To elucidate these interaction mechanisms and understand the process of viral infectivity, we performed experimental and theoretical studies of this protein. The soluble domains of the N-terminal (1-38 aa) and C-terminal regions (67-298 aa), with 231 amino acids of the hRSV G glycoprotein have been cloned and the N-terminal region was expressed in BL21 pLysS bacteria. In a later trial these peptides will be purified and biophysical tests will be done. It was also performed a theoretical characterization of this protein, to assess the possible interaction sites with glycosaminoglycan (heparin). It were obtained two theoretical models for the hRSV G protein as well as two interaction models with heparin, in order to determine a possible site of occurrence of interaction. Knowledge of G protein structure is of great importance to elucidate the mechanism of viral infectivity and interaction mechanisms with potential ligants, and the results obtained in this work will allow us, in a later step, to propose mechanisms of cellular recognition by hRSV through glycosaminoglycans / Mestre

Aberrant JNK signaling can result in two main forms of disease in humans: 1) neurological, coronary, hepatobiliary, and respiratory diseases and 2) autoimmune, inflammatory, and cancer conditions. Enantiomers of the lignan zuonin A, (-)-zuonin A and (+)-zuonin A, have been shown to bind to JNK isoforms with similar affinity and disrupt protein-protein interactions at JNK's D-recruitment site, making them a good candidate for specific non-ATP competitive inhibitors. However, (-)-zuonin A inhibits 80% of JNK catalyzed reactions at saturating levels, while (+)-zuonin A only inhibits 15%. Molecular docking and molecular dynamics simulations were performed to gain a better understanding of how these inhibitors interact JNK. The results of this study provide an alternative binding mode for (-)-zuonin A, compared to one proposed in a previous study, that shows (-)-zuonin A interacting with JNK via an induced fit mechanism by forming a larger pocket for itself near the highly conserved [phi]A-X-[phi]B recognition site, a dynamic move not seen in (+)-zuonin A simulations, and may help explain their different inhibition patterns. / text

From the reaction of ZnCl2 with the non-steroidal anti-inflammatory drug diflunisal (Hdifl), complex [Zn(difl-O)(2)(MeOH)(4)], 1 was formed, while in the presence of a N,N'-donor heterocyclic ligand 2,2'-bipyridylamine (bipyam), 2,2'-bipyridine (bipy), 1,10-phenanthroline (phen) and 2,2'-dipyridylketone oxime (Hpko), the complexes [Zn(difl-O,O')(2)(bipyam)], 2, [Zn(difl-O,O')(2)(bipy)], 3, [Zn(difl-O,O')(2)(phen)], 4 and [Zn(difl-O)2(Hpko)(2)], 5 were isolated, respectively. The complexes were characterized by physicochemical and spectroscopic techniques and the crystal structures of complexes 2, 3 and 5 were determined by X-ray crystallography. The ability of the complexes to scavenge 1,1-diphenyl-picrylhydrazyl, 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) and hydroxyl radicals and to inhibit soybean lipoxygenase was studied and the complexes were more active than free Hdifl. The interaction of the complexes with serum albumins was monitored by fluorescence emission spectroscopy and the corresponding binding constants were calculated. UV-vis spectroscopy, viscosity measurements and fluorescence emission spectroscopy for the competitive studies of the complexes with ethidium bromide were employed to investigate the interaction of the complexes with calf-thymus DNA and revealed intercalation as the most possible DNA-binding mode. Computational techniques were used to identify possible binding sites of albumins and DNA, and determine the druggability of human and bovine serum albumins with the five novel complexes. The majority of the complexes are stronger binders than the free Hdifl. This is the first study incorporating experimental and computational results to explore the binding activity of metal-NSAID complexes with DNA and serum albumins, suggesting their application as potential metallodrugs.

<p>Protein conformational transitions are fundamental to the functions of many proteins, and computational methods are valuable for elucidating the transitions that are not readily accessible by experimental techniques. Here we developed accelerated sampling methods to calculate optimized all-atom protein conformational transition paths. Adaptively biased path optimization (ABPO) is a computational simulation method to optimize the conformational transition path between two states. We first examined the transition paths of three systems with relatively simple transitions. The ways to define reduced variables were explored and transition paths were built at convergence of the optimizations. We constructed the all-atom conformational transition path between the active and the inactive states of the Src kinase domain. The C helix rotation was identified as the main free energy barrier in the all‑atom system, and the intermediate conformations and key interactions along the transition path were analyzed. This is the first demonstration of the robustness of a computational method for calculating protein conformational transitions without restraints to a specified path. We also evaluated protein‑peptide interactions using both molecular dynamics simulations and peptide docking. Long unbiased simulations were used to evaluate Src‑SSP interactions and complex stability in both implicit and explicit solvent. Molecular docking was used to build possible protein‑peptide interaction models, using both Src regulatory domain SH2 and the kinase domain. Possible Src‑SSP complexes were built as the first Src‑substrate complex structure models.</p>

O Atracamento Molecular é uma importante ferramenta utilizada no descobrimento de novos fármacos. O atracamento com ligante flexível é um processo computacionalmente custoso devido ao número alto de graus de liberdade do ligante e da rugosidade do espaço de busca conformacional representando a afinidade entre o receptor e uma molécula ligante. O problema é definido como a busca pela solução de menor energia de ligação proteína-ligante. Considerando uma função suficientemente acurada, a solução ótima coincide com a melhor orientação e afinidade entre as moléculas. Assim, o método de busca e a função de energia são partes fundamentais para a resolução do problema. Muitos desafios são enfrentados para a resolução do problema, o tratamento da flexibilidade, algoritmo de amostragem, a exploração do espaço de busca, o cálculo da energia livre entre os átomos, são alguns dos focos estudados. Esta dissertação apresenta uma técnica baseada em um Algoritmo Genético de Chaves Aleatórias Viciadas, incluindo a discretização do espaço de busca e métodos de agrupamento para a multimodalidade do problema de atracamento molecular. A metodologia desenvolvida explora o espaço de busca gerando soluções diversificadas. O método proposto foi testado em uma seleção de complexos proteína-ligante e foi comparado com softwares existentes: AutodockVina e Dockthor. Os resultados foram estatisticamente analisados em termos estruturais. O método se mostrou eficiente quando comparado com outras ferramentas e uma alternativa para o problema de Atracamento Molecular. / Molecular Docking is a valuable tool for drug discovery. Receptor and flexible Ligand docking is a very computationally expensive process due to a large number of degrees of freedom of the ligand and the roughness of the molecular binding search space. A Molecular Docking simulation starts with a receptor and ligand unbounded structures and the algorithm tests hundreds of thousands of ligands conformations and orientations to find the best receptor-ligand binding affinity by assigning and optimizing an energy function. Despite the advances in the conception of methods and computational strategies for search the best protein-ligand binding affinity, the development of new strategies, the adaptation, and investigation of new approaches and the combination of existing and state-of-the-art computational methods and techniques to the Molecular Docking problem are clearly needed. We developed a Biased Random-Key Genetic Algorithm as a sampling strategy to search the protein-ligand conformational space. The proposed method has been tested on a selection of protein-ligand complexes and compared with existing tools AutodockVina and Dockthor. Compared with other traditional docking software, the proposed method has the best average Root-Mean-Square Deviation. Structural results were statistically analyzed. The proposed method proved to be efficient and a good alternative to the molecular docking problem.

O Atracamento Molecular é uma importante ferramenta utilizada no descobrimento de novos fármacos. O atracamento com ligante flexível é um processo computacionalmente custoso devido ao número alto de graus de liberdade do ligante e da rugosidade do espaço de busca conformacional representando a afinidade entre o receptor e uma molécula ligante. O problema é definido como a busca pela solução de menor energia de ligação proteína-ligante. Considerando uma função suficientemente acurada, a solução ótima coincide com a melhor orientação e afinidade entre as moléculas. Assim, o método de busca e a função de energia são partes fundamentais para a resolução do problema. Muitos desafios são enfrentados para a resolução do problema, o tratamento da flexibilidade, algoritmo de amostragem, a exploração do espaço de busca, o cálculo da energia livre entre os átomos, são alguns dos focos estudados. Esta dissertação apresenta uma técnica baseada em um Algoritmo Genético de Chaves Aleatórias Viciadas, incluindo a discretização do espaço de busca e métodos de agrupamento para a multimodalidade do problema de atracamento molecular. A metodologia desenvolvida explora o espaço de busca gerando soluções diversificadas. O método proposto foi testado em uma seleção de complexos proteína-ligante e foi comparado com softwares existentes: AutodockVina e Dockthor. Os resultados foram estatisticamente analisados em termos estruturais. O método se mostrou eficiente quando comparado com outras ferramentas e uma alternativa para o problema de Atracamento Molecular. / Molecular Docking is a valuable tool for drug discovery. Receptor and flexible Ligand docking is a very computationally expensive process due to a large number of degrees of freedom of the ligand and the roughness of the molecular binding search space. A Molecular Docking simulation starts with a receptor and ligand unbounded structures and the algorithm tests hundreds of thousands of ligands conformations and orientations to find the best receptor-ligand binding affinity by assigning and optimizing an energy function. Despite the advances in the conception of methods and computational strategies for search the best protein-ligand binding affinity, the development of new strategies, the adaptation, and investigation of new approaches and the combination of existing and state-of-the-art computational methods and techniques to the Molecular Docking problem are clearly needed. We developed a Biased Random-Key Genetic Algorithm as a sampling strategy to search the protein-ligand conformational space. The proposed method has been tested on a selection of protein-ligand complexes and compared with existing tools AutodockVina and Dockthor. Compared with other traditional docking software, the proposed method has the best average Root-Mean-Square Deviation. Structural results were statistically analyzed. The proposed method proved to be efficient and a good alternative to the molecular docking problem.

O Atracamento Molecular é uma importante ferramenta utilizada no descobrimento de novos fármacos. O atracamento com ligante flexível é um processo computacionalmente custoso devido ao número alto de graus de liberdade do ligante e da rugosidade do espaço de busca conformacional representando a afinidade entre o receptor e uma molécula ligante. O problema é definido como a busca pela solução de menor energia de ligação proteína-ligante. Considerando uma função suficientemente acurada, a solução ótima coincide com a melhor orientação e afinidade entre as moléculas. Assim, o método de busca e a função de energia são partes fundamentais para a resolução do problema. Muitos desafios são enfrentados para a resolução do problema, o tratamento da flexibilidade, algoritmo de amostragem, a exploração do espaço de busca, o cálculo da energia livre entre os átomos, são alguns dos focos estudados. Esta dissertação apresenta uma técnica baseada em um Algoritmo Genético de Chaves Aleatórias Viciadas, incluindo a discretização do espaço de busca e métodos de agrupamento para a multimodalidade do problema de atracamento molecular. A metodologia desenvolvida explora o espaço de busca gerando soluções diversificadas. O método proposto foi testado em uma seleção de complexos proteína-ligante e foi comparado com softwares existentes: AutodockVina e Dockthor. Os resultados foram estatisticamente analisados em termos estruturais. O método se mostrou eficiente quando comparado com outras ferramentas e uma alternativa para o problema de Atracamento Molecular. / Molecular Docking is a valuable tool for drug discovery. Receptor and flexible Ligand docking is a very computationally expensive process due to a large number of degrees of freedom of the ligand and the roughness of the molecular binding search space. A Molecular Docking simulation starts with a receptor and ligand unbounded structures and the algorithm tests hundreds of thousands of ligands conformations and orientations to find the best receptor-ligand binding affinity by assigning and optimizing an energy function. Despite the advances in the conception of methods and computational strategies for search the best protein-ligand binding affinity, the development of new strategies, the adaptation, and investigation of new approaches and the combination of existing and state-of-the-art computational methods and techniques to the Molecular Docking problem are clearly needed. We developed a Biased Random-Key Genetic Algorithm as a sampling strategy to search the protein-ligand conformational space. The proposed method has been tested on a selection of protein-ligand complexes and compared with existing tools AutodockVina and Dockthor. Compared with other traditional docking software, the proposed method has the best average Root-Mean-Square Deviation. Structural results were statistically analyzed. The proposed method proved to be efficient and a good alternative to the molecular docking problem.

This dissertation presents a collection of biological simulations and predictions in collaboration with experiment to support and elucidate the trends observed in various protein–ligand systems. Within the model systems, there is a strong focus on the support for the development of peptidomimetic inhibitors for post-translational reader proteins (CBX proteins). The systems studied throughout this document each present their own unique challenges but fall under the general theme of protein flexibility and the difficulties of sampling such systems. As part of this work, methodological advances were made to address the challenges of structural prediction on flexible proteins and ultimately form the method Selective Ligand-Induced Conformational Ensemble (SLICE). The development, validation, and future directions of the SLICE method are also discussed. Ultimately, the collaborative efforts presented in this dissertation bring forward a greater understanding of the drug design challenges on the CBX proteins as well a new methodology in the field of structure-based drug design. / Graduate

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第20065号 / 農博第2194号 / 新制||農||1045(附属図書館) / 学位論文||H28||N5021(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)准教授 赤松 美紀, 教授 植田 和光, 教授 宮川 恒 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Disease caused by antibiotic resistant (ABR) bacteria has become a widespread global public health issue as humanity's existing collection of effective antibiotics dwindles. ABR bacteria are responsible for approximately 5 million deaths worldwide annually, which is predicted to reach 10 million yearly by 2050. Antivirulence therapeutics have been explored in recent times as another approach to tackling the global ABR pandemic by disrupting the function of virulence factors that promote disease development. The bacterial type IV pilus (T4P) is a prevalent virulence factor in many ABR pathogens, contributing to bacterial pathogenesis by facilitating cell motility, surface adhesion, and biofilm formation. Critically, the T4P facilitates early stages of disease, providing a means to invade and colonize a host. T4P assembly is driven by the PilB/PilF motor ATPase that localizes to the cytoplasmic face of the inner membrane to drive pilus biogenesis by ATP hydrolysis. The thesis work here explores computational and experimental methods for the discovery of antivirulence therapeutics targeting the T4P assembly ATPase PilB. A computational model of Chloracidobacterium thermophilum PilB was generated by homology modeling and molecular docking was performed to analyze the binding characteristics of six anti-PilB inhibitory compounds identified in previous studies. Computational docking aligns with the existing body of work and reveals important protein-ligand interactions and characteristics, particularly involving the ATP binding domain of PilB. This work supports the use of PilB in structure-based virtual screening to identify novel compounds targeting PilB. Additionally, through heterologous expression and chromatography methods, the ATPase core of Neisseria gonorrhoeae PilF was successfully expressed and purified as an active ATPase. This work optimized conditions for its ATPase activity in vitro. Additionally, this thesis documents the experimental attempt to express and purify Clostridioides difficile PilB as an active ATPase. Two of the seven C. difficile PilB variant proteins expressed led to soluble protein while one construct remains to be explored. The results of these studies provide insight for future methodology design for antivirulence therapeutic research targeting the T4P assembly ATPase using both in silico and in vitro methods. / Master of Science / Antibiotic resistant bacterial infections are responsible for nearly 5 million deaths worldwide every year. These infections are becoming increasingly more difficult to treat as bacterial pathogens acquire greater means to overcome our dwindling antibiotic repertoire. This has prompted researchers to explore alternative therapeutic strategies, including the antivirulence approach that aims to disable the function or production of bacterial virulence factors. Virulence factors serve as arms and armor that help bacteria cause disease, but they may be disrupted in such a way that renders potentially pathogenic bacteria harmless to humans. One major virulence factor in many antibiotic resistant bacteria is the type IV pilus (T4P), which is important in the early stages of host invasion by mediating adhesion and biofilm formation. This work explores both computational and experimental strategies to antivirulence drug discovery targeting the T4P, specifically the primary motor protein PilB/PilF. Newly identified PilB inhibitors were evaluated by molecular docking and molecular dynamics simulation to assess the use of PilB for drug discovery via virtual screening in silico. This revealed key characteristics and protein-ligand interactions that contribute to successful PilB inhibition and supports the use of CtPilB for structure-based virtual screening. Additionally, the PilF motor protein from Neisseria gonorrhoeae was successfully purified and demonstrated to be active for inhibitor discovery in the future. This work also covers efforts to establish Clostridioides difficile PilB as potential model enzyme for inhibitor discovery in the future.