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

It is predicted that by 2050 there will be 10 million deaths annually due to super-resistant bacterial infections. Antimicrobial resistance (AMR) is already responsible for nearly 5 million deaths a year. Ribosomes serve as an ideal drug target being frequently targeted by antibiotics and having a highly conserved structure with few options for resistance. However, computer aided drug design (CADD) using ribosome crystal structures presents several challenges and is underutilized in the field. In this work we establish a successful protocol for antibiotic redocking and docking within the high interest sites of the peptidyl transferase center (PTC). Molecular visualization and interaction mapping were used to atomistically delineate binding patterns in the ribosomal PTC that could be used for CADD. Eleven ribosome crystal structures were validated for computational testing, which revealed derivative binding patterns in the A-site and P-site that can be used to increase antibiotic efficacy. Ribosome overlays revealed high interaction frequency nucleotides that were widely conserved throughout the different species and could be used to inform bidentate design to target two pockets at once. This work serves as a basis for methods to computationally explore drug optimization on ribosome targeting antibiotics to help combat the rapid expansion of AMR. / Master of Science in Life Sciences / Antimicrobial resistance (AMR) to antibiotics by bacteria is a rapidly increasing problem. Current trends predict that there will be more death due to super-resistant bacterial strains than cancer by 2050. Ribosomes are essential cellular machinery for bacteria and make an ideal antibiotic target. Using computational tools to optimize antibiotics with available ribosome crystal structural data presents several challenges and is underutilized throughout the field. In this work we establish a successful protocol for determining and exploiting antibiotic binding patterns within the functional center of the ribosome, the peptidyl transfer center (PTC). Nearly a dozen ribosome crystal structures were validated for computational testing, and binding patterns were revealed within the PTC that allowed antibiotic derivatives with increased efficacy to be developed. Ribosome validation also helped inform new drug class design so that multiple drug sites could be targeted at once, which were docked sharing high frequency nucleotide interactions with both parent antibiotics. This work serves as a basis for methods to computationally explore drug optimization on ribosome targeting antibiotics to help combat the rapid expansion of AMR.

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