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.

Antibiotics have transformed modern medicine in manifold ways. However, the misuse and over-consumption of antibiotics or antimicrobials have led to the rise in antimicrobial resistance (AMR). Unfortunately, robust tools or techniques for the detection of potential loci responsible for AMR before it happens are lacking. The emergence of resistance even when a strain lacks known AMR genes has puzzled researchers for a long time. Clearly, there is a critical need for the development of novel approaches for uncovering yet unknown resistance elements in pathogens and advancing our understanding of emerging resistance mechanisms. To aid in the development of new tools for deciphering AMR, here we propose a machine learning (ML) based framework that provides ML models trained and tested on (1) genotypic AMR and phenotypic antimicrobial susceptibility testing (AST) data, which can predict novel resistance factors in bacterial strains that lack already implicated resistance genes; and (2) complete gene set and AST phenotypic data, which can predict the most important genetic loci involved in resistance to specific antibiotics in bacterial strains. The validation of resistance loci prioritized by our ML pipeline was performed using homology modeling and in silico molecular docking.

Submitted by Irene Nascimento (irene.kessia@ufpe.br) on 2016-07-29T17:57:19Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) MMR-TESE.pdf: 16044503 bytes, checksum: 1c3b4963930b3b75a91e718bd1893e50 (MD5) / Made available in DSpace on 2016-07-29T17:57:19Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) MMR-TESE.pdf: 16044503 bytes, checksum: 1c3b4963930b3b75a91e718bd1893e50 (MD5) Previous issue date: 2016-02-29 / Facepe / Este trabalho apresenta uma metodologia in silico para o estudo de complexos de inclus o utilizados na inova o terap utica. Um complexo de inclus o formado por um host (hospedeiro), e por um guest (h spede). Neste trabalho, o host estudado a ciclodextrina (e seus derivados) e o guest, um ligante (f rmaco, em potencial), formando o complexo host:guest. O objetivo desse projeto desenvolver uma plataforma (CycloMolder) capaz de realizar estudos in silico dos complexos de inclus o de forma autom tica e precisa, fazendo uso de uma interface gr fica de usu rio. Esse objetivo foi tra ado para facilitar os estudos de modelagem molecular para este tipo de sistema qu mico, com interesse farmac utico. A plataforma composta por dois m dulos: CycloGen e CycloDock. O primeiro m dulo (CycloGen) constr i modelos com mais de uma estrutura para representar um derivado de ciclodextrina. O segundo m dulo (CycloDock) realiza o c lculo de docking molecular entre as mol culas host e guest, utilizando o programa Autodock Vina e apresenta os resultados obtidos, incluindo gr ficos que mostram a distribui o energ tica e as intera es intermoleculares do complexo. O programa CycloMolder foi testado atrav s de estudos de casos inspirados em problemas farmac uticos reais. Os testes realizados destacaram a import ncia da gera o de mais de uma configura o para representar significativamente um derivado de ciclodextrina, e tamb m mostrou o potencial anal tico do programa, proporcionado pela automa o do estudo de modelagem, execu o dos c lculos e an lise dos resultados. De forma geral, o programa CycloMolder atinge seus objetivos, automatizando e simplificando os estudos in silico dos complexos de inclus o, contribuindo desta forma para a inova o terap utica. / This work presents an in silico methodology for study of inclusion complexes used in therapeutic innovation. An inclusion complex is formed by a host and a guest. In this work, the host is the cyclodextrin and their derivatives and the guest is a potential drug, forming a host:guest complex. The goal of this work is to development a platform (CycloMolder) able to perform in silico studies of inclusion complexes in an automated and precise fashion, making use of a graphical user interface. The platform (CycloMolder) consists of two modules: CycloGen and CycloDock. The first module (CycloGen) builds models with more than one chemical structure to represent a cyclodextrin derivative. The second module (CycloDock) performs the molecular docking calculations between the host and guest molecules, using the AutoDock Vina program, and displays the results, including graphs showing the energy distribution and intermolecular interactions present in the host:guest complexes. The CycloMolder program was tested through case studies inspired by real pharmaceutical problems. The tests highlighted the importance of generating more than one chemical structure to better represent a ciclodextrin derivative, also showed the analytical potential of the program, provided by the automation of the modeling study, execution of calculations and analysis of results. Overall, the CycloMolder program achieves its goals by automating and simplifying the in silico studies of inclusion complexes, thus contributing to the therapeutic innovation.

Submitted by JÚLIO HEBER SILVA (julioheber@yahoo.com.br) on 2016-11-22T16:54:28Z No. of bitstreams: 2 Dissertação - Raisa Melo Lima - 2016.pdf: 5394977 bytes, checksum: 7066675bd31ee0e72d9ecd420ea0d1e7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Jaqueline Silva (jtas29@gmail.com) on 2016-11-30T15:46:54Z (GMT) No. of bitstreams: 2 Dissertação - Raisa Melo Lima - 2016.pdf: 5394977 bytes, checksum: 7066675bd31ee0e72d9ecd420ea0d1e7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-11-30T15:46:54Z (GMT). No. of bitstreams: 2 Dissertação - Raisa Melo Lima - 2016.pdf: 5394977 bytes, checksum: 7066675bd31ee0e72d9ecd420ea0d1e7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-10-03 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Paracoccidioidomycosis (PCM) is a systemic mycosis endemic in Brazil, where are recorded about 80% of cases worldwide, and has Paracoccidioides sp. as the etiologic agent. Malate synthase (MLS) is an important enzyme related to the fungal metabolism, once it is essential in the glyoxylate cycle, a secondary metabolic pathway of the citric acid cycle exclusive to microorganisms and plants. Its absence in humans makes this enzyme an interesting subject to study, mainly in rational drug design. From recent in vitro studies, several interacting proteins of MLS (receiver) were classified, but the modes of interaction and key regions involved in protein-protein interfaces (PPIs) have not yet been described. In this work, six (6) binding proteins (BPs) were selected to describe the IP's of MLS. Their tridimensonal structures, as well as MLS, were predicted by homology modeling using I-TASSER server, and subsequent molecular dynamics simulations (MD). The most common conformational modes of each protein were obtained by cluster analysis of the trajectories generated by MD. Molecular docking simulations using Gramm-X were then performed for the conformational modes of MLS against the BP's, resulting in a total of 36 complexes. Based on the higher frequency of some small fragments of proteins observed in the IPP's, 57 peptides with sizes between 5 and 20 residues, were initially selected from 5 regions of MLS which are considered more frequent in protein-protein interaction. FlexPepDock simulations were performed to optimize the atomic coordinates of the peptide complexed with MLS, and concomitantly, PepFOLD simulations were performed to evaluate the stability of each peptide in solution. Based on the lower energy score of peptides linked to MLS, and the stability of their structures in solution (MLS-free), 6 peptides were selected as promising ligands to MLS mode 1. The stability and patterns of interactions of these peptides were evaluated in detail. / A paracoccidioidomicose (PCM) é uma micose sistêmica endêmica no Brasil, onde são registrados cerca de 80% dos casos mundiais, e possui como agente etiológico o fungo Paracoccidioides spp. A Malato sintase (MLS) é uma importante enzima relacionada ao metabolismo fúngico, uma vez que é essencial no ciclo do glioxilato, uma via metabólica importante de produção de glicose para parede celular, sendo exclusiva de micro-organismos e plantas. Sua ausência em humanos a torna um alvo interessante de estudo, principalmente, no desenho racional de fármacos. A partir de recentes estudos in vitro, várias proteínas que interagem com a PbMLS (receptor) foram classificadas, porém os modos de interação e as regiões chaves envolvidas nas interfaces proteína-proteína (IPP’s), não foram ainda descritas. Neste trabalho, 6 (seis) proteínas ligantes (PL) foram selecionadas para verificar suas interações com PbMLS. As estruturas tridimensionais dessas proteínas, bem como de PbMLS, foram preditas por homologia, usando o servidor I-TASSER. Simulações de dinâmica molecular (DM) foram realizadas pelo programa GROMACS, e os modos das conformações mais representativas de cada proteína foram determinados baseando-se nas análises de agrupamentos a partir das trajetórias geradas por DM. Simulações de ancoragem molecular com GRAMM-X foram então realizadas entre os modos conformacionais de PbMLS contra os obtidos de PL’s, resultando num total de 36 complexos. Baseado na frequência maior de alguns pequenos fragmentos de proteínas, observados nas IPP’s, 57 peptídeos de tamanhos entre 5 e 20 resíduos de aminoácidos, foram inicialmente selecionados a partir de 5 regiões da PbMLS consideradas mais frequentes na interação proteína-proteína. Simulações com FlexPepDock foram realizadas para otimizar as coordenadas atômicas dos peptídeos complexados com MLS e, concomitantemente, simulações com PepFOLD foram realizadas para avaliar a estabilidade de cada peptídeo em solução. Com base nos mais baixos scores de energia dos peptídeos ligados a MLS bem como na estabilidade de suas estruturas não ligadas em solução , 5 peptídeos foram selecionados como promissores ligantes ao modo 1 de MLS. A estabilidade e os padrões de interações destes peptídeos são avaliados em detalhes.

Thyroid disruptions by xenobiotics have been associated with a broad spectrum of severe adverse human health effects, such as impaired brain development and metabolic syndrome. Ingestion of indoor dust and contact with industrial chemicals are two significant human exposure routes of thyroid hormone disrupting chemicals (THDCs), raising serious concerns for human health. However, it is a laborious and costly process to identify THDCs using conventional experimental methods, due to the number of chemicals in commerce and the varieties of potential disruption mechanisms. In this thesis, we are aimed at in silico identification of novel THDCs targeting transthyretin (TTR) and thyroid hormone receptor (THR) among dust contaminants and commonly used industrial chemicals. In vitro assays were used to validate the in silico prediction results. Co-crystallization and molecular dynamics (MD) simulations were applied to reveal binding modes of THDCs at the studied biological targets and to explain their intermolecular recognition. The main findings presented in this thesis are: 1. Over 144 environmental pollutants have been confirmed as TTR-binders in vitro and these cover a wide range of environmental pollutants and show distinct chemical profiles including a large group of halogenated aromatic compounds and a second group of per- and polyfluoroalkyl substances. (Paper I) 2. In total 485 organic contaminants have been reported to be detected in household dust. The developed QSAR classification model predicted 7.6% of these dust contaminants and 53.1% of their metabolites as potential TTR-binders, which emphasizes the importance of metabolic bioactivation. After in vitro validation, four novel TTR binders with IC50 ≤ 10 µM were identified, i.e. perfluoroheptanesulfonic acid, 2,4,2',4'-tetrahydroxybenzophenone (BP2), 2,4,5-trichlorophenoxyacetic acid, and 3,5,6-trichloro-2-pyridinol. (Paper II) 3. The development of a robust structure-based virtual screening (VS) protocol resulted in the prediction of 31 dust contaminants as potential binders to THRβ1 including musk compounds, PFASs, and bisphenol A derivatives. The in vitro experiments confirmed four compounds as weak binders to THRβ1, i.e. 2,4,5-trichlorophenoxyacetic acid, bisphenol A (3-chloro-2-hydroxypropyl) (2,3-dihydroxypropyl) ether, 2,4,2',4'-tetrahydroxybenzophenone, and 2,4-dichlorophenoxyacetic acid. (Paper III) 4. We revealed the binding conformations of perfluorooctanesulfonic acid, perfluorooctanoic acid, and BP2 in the thyroxine binding sites (TBSs) of TTR by co-crystallizing TTR with the three compounds. A VS protocol was developed based on the TTR complex structures that predicted 192 industrial chemicals as potential binders to TTR. Seven novel TTR binders were confirmed by in vitro experiments including clonixin, 2,6-dinitro-p-cresol (DNPC), triclopyr, fluroxypyr, bisphenol S, picloram, and mesotrione. We further co-crystallized TTR with PBS, clonixin, DNPC, and triclopyr, and their complex structures showed that the compounds bind in the TBSs as proposed by the VS protocol. In summary, 13 indoor dust contaminants and industrial chemicals were identified as THDCs using a combination of in silico and in vitro approaches. To the best of our knowledge, none of these compounds has previously been reported to bind to TTR or THR. The identifications of these THDCs improve our understanding on the structure-activity relationships of THDCs. The crystal structures of TTR-THDC complexes and the information on THDC-Target intermolecular interactions provide a better understanding on the mechanism-of-actions behind thyroid disruption. The dataset compiled and in silico methods developed serve as a basis for identification of more diverse THDCs in the future and a tool for guiding de novo design of safer replacements.

付記する学位プログラム名: 充実した健康長寿社会を築く総合医療開発リーダー育成プログラム / 京都大学 / 0048 / 新制・課程博士 / 博士(薬科学) / 甲第22400号 / 薬科博第122号 / 新制||薬科||13(附属図書館) / 京都大学大学院薬学研究科医薬創成情報科学専攻 / (主査)教授 掛谷 秀昭, 教授 二木 史朗, 教授 土居 雅夫 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

A hipercolesterolemia familial (HF) é uma doença autossômica dominante considerada como uma das formas mais graves de hiperlipidemia, assim como, a principal causa de morbi-mortalidade por ser o principal fator desencadeante da aterosclerose. A alteração primária e mais freqüente da HF incide no gene do receptor da LDL (LDLr), sabe-se que mais de 1600 mutações são descritas na literatura e a principal consequência dessas alterações resultam no comprometimento da remoção da LDL, aumentando a concentração plasmática. Atualmente, o ultrasequenciamento genômico permite gerar muitos dados, que podem identificar novas mutações gênicas de forma eficiente, reprodutiva e rápida. No entanto, somente a validação da nova mutação por atividade funcional pode realmente estabelecer a associação com a doença. O presente estudo tem como objetivo realizar a análise da atividade do receptor da LDL, identificadas através do sequenciamento de alto rendimento, no gene LDLr realizado pelo nosso grupo de pesquisa e correlacionar com dados clínicos, in vitro, in silico e estrutural. Para cumprir esta meta, os linfócitos T dos portadores de HF foram isolados do sangue periférico, cultivados e submetidos a estímulo para a expressão de receptores da LDL, incubados com LDL marcada para avaliação de ligação e interiorização pelas células de cada paciente. Dos 30 pacientes selecionados para esse estudo, 63% apresentaram mutação no LDLR, sendo que quase todas as variantes (p.Gly373Asp, p.Asp601His, p.Ile488Thr, p.Gly549Asp, p.Gly592Glu e Gly681Asp) são localizadas no segundo domínio entre os éxons 7 ao 14. De acordo com o docking molecular a variante p.Gly592Glu (rs137929307), que já foi identificada na população polonesa, espanhola e brasileira, já relacionada com a HF, pode aumentar a interação do LDLr com a ApoB e consequentemente o modo de interação entre as proteínas, no estudo in vitro foi possível notar um aumento tanto na média de fluorescência da ligação e da ligação e interiorização em relação a quantidade de LDLr na superfície celular. / Familial hypercholesterolemia (HF) is an autosomal dominant disease considered as one of the most severe forms of hyperlipidemia, as well as the main cause of morbidity and mortality because it is the main triggering factor for atherosclerosis. The primary and more frequent alteration of the HF affects the LDL receptor gene (LDLr), it is known that more than 1600 mutations are described in the literature and the main consequence of these alterations results in the compromise of the LDL removal, increasing the plasma concentration. Nowadays, genomic ultrasequencing allows the generation of many data, which can identify new gene mutations efficiently, reproductively and rapidly. However, only the validation of the new functional activity mutation can actually establish association with the disease. The aim of the present study was to analyze LDL receptor activity, identified by high-throughput sequencing, in the LDLr gene performed by our research group and to correlate with clinical, in vitro, in silico and structural data. To meet this goal, the T lymphocytes from the HF carriers were isolated from the peripheral blood, cultured and challenged for the expression of LDL receptors, incubated with labeled LDL for binding assessment and internalization by the cells of each patient. Of the 30 patients selected for this study, 63% had a mutation in LDLR, and almost all variants (p.Gly373Asp, p.Asp601His, p.Ile488Thr, p.Gly549Asp, p.Gly592Glu and Gly681Asp) are located in the second domain between exons 7 to 14. According to the molecular docking the variant p.Gly592Glu (rs137929307), which has already been identified in the Polish, Spanish and Brazilian population, already related to HF, can increase the interaction of LDLr with ApoB and consequently the mode of interaction between proteins, in the in vitro study it was possible to note an increase in both the mean fluorescence of binding and binding and internalization in relation to the amount of LDLr on the cell surface.

A modelagem das interações entre macromoléculas e ligantes ainda se depara com diversos desafios na área de desenho de fármacos assistidos por computador. Apesar do crescimento da área, temas como a flexibilidade do receptor, funções de pontuação e solvatação ainda têm sido alvo de intensa investigação na comunidade científica. Com o objetivo de analisar a interação em milhares ou milhões de complexos, é imprescindível uma boa harmonização entre o custo computacional e a acurácia dos métodos computacionais que permitem a classificação de ligantes de acordo com a energia de interação. O LiBELa (Ligand Binding Energy Landscape) é um programa de docagem molecular com abordagem híbrida, ou seja, utiliza informações do ligante e do receptor durante o processo de docagem. Inicialmente, as características estéricas e eletrostáticas de um ligante de referência (cristalográfico, por exemplo) são utilizadas nos cálculos de similaridade e sobreposição, obtendo assim uma conformação inicial pré-otimizada do ligante testado. Em seguida, a energia de interação é minimizada no sítio ativo de receptor a partir de potenciais energéticos. Quatro funções de pontuação baseadas em campo de força foram testadas e otimizadas, compostas por potenciais de van der Waals, de Coulomb, e uma função empírica de solvatação denominada função de Stouten-Verkhivker (SV). A flexibilidade do sistema foi tratada através da geração de confôrmeros que amostram os graus de liberdade dos ligantes descritos como semi-rígidos e através de potenciais atenuados que suavizam a superfície de energia de interação, permitindo interações em distâncias interatômicas antes repulsivas. Como ponto de partida, os métodos implementados no programa LiBELa demonstraram resultados satisfatórios nos testes de cross- e self-docking, mostrando ser uma ferramenta eficiente em encontrar os modos de ligação cristalográficos de forma equivalente ou até melhor às dos programas comparados. Através de testes de enriquecimento nos conjuntos de dados DUD, DUDE e CM-DUD, foram otimizadas de forma sistemática as constantes dielétrica, do termo de solvatação, e dos termos de atenuação. Também foi realizado um paralelo entre as funções de pontuação, incluindo a atenuação e o termo de solvatação. Estes mesmos testes mostraram resultados superiores do LiBELa de 39% e 15% em comparação com um programa baseado puramente no receptor (DOCK 6.6), relativo à média da área sob a curva em escala semi-logarítmica nas bases de dados DUDE e DUD respectivamente. Apesar da função de solvatação SV implementada no LiBELa apresentar boa correlação com dados experimentais (r=0,72) e com o modelo Zou GB de solvatação (r=0,88), não apresentou correlação significativa com os métodos GB e PB implementados no pacote de programas disponível no AmberTools. Comparadas às funções de pontuação do LiBELa, as funções com correção para solvatação apresentaram pior enriquecimento, salvo alguns alvos específicos. Por fim, foram realizados ensaios de docagem molecular utilizando como alvo uma enzima β-galactosidase da família GH42, cuja estrutura fora resolvida em nosso grupo. Os resultados permitiram conclusões acerca de como o modo de ligação interfere na preferência de ligação entre dissacarídeos de ligações glicosídicas distintas, consistentes com dados experimentais de ensaios cinéticos de ligação. / Modeling the interactions between macromolecules and ligands still faces several challenges in the computer-aided drug design area. Despite the growth in the area, subjects such as receptor flexibility, scoring functions and solvation still have been widely explored in the scientific community. In order to analyze the interaction for thousands or millions of complexes, a good harmonization between the computational cost and the accuracy of the calculation methods in molecular docking programs is essential. LiBELa (Ligand Binding Energy Landscape) is a hybrid approach program that uses both ligand and receptor information for ligand docking. Initially, the steric and electrostatic characteristics from a reference binder (crystallographic, for example) are used to similarity and overlay calculations, thus obtaining an initial conformation of the ligand tested. Then, within the receptor´s active site, the interaction energy is minimized using energetic potentials. Four force field-based scoring functions were tested and optimized, composed of van der Waals and Coulomb potentials and an empirical solvation function called Stouten-Verkhivker (SV). Concerning the system flexibility, besides the confomers generation that sample the degrees of freedom for semi-rigid ligands, attenuated potentials smooth the energy surface allowing interactions between previously repulsive interatomic distances. As a starting point, LiBELa performed satisfactorily in the cross- and self-docking tests, showing that is an eficient tool to reproduce crystallographic binding modes equivalently to or even better than reference programs. Through enrichment of DUD, DUDE and CM-DUD datasets, the dielectric constant, solvation and softening terms were systematically optimized. It also allowed a parallel between scoring functions, including attenuation and solvation term. Finally, it revealed the LiBELa showed an enhancement of 39% and 15% as compared to the purely receptor-based program DOCK 6.6, relative to the mean of the area under the curve on a semi-logarithmic scale in the DUDE and DUD databases respectively. Although the SV solvation function implemented in LiBELa showed good correlations with experimental data (r = 0.72) and with the Zou GB / SA solvation method implemented in DOCK6 (r = 0.88), it did not show significant correlation with the GB/SA and PB/SA methods implemented in AmberTools. Comparing all the LiBELa tested scoring functions, those including solvation correction showed worse enrichments, except for some specific targets. Finally, molecular docking experiments using LiBELa were conducted with a β-galactosidase from GH42 family, whose structure was solved in our group. The results allowed conclusions concerning how the binding mode interferes the preference for some disaccharides of distinct glycosidic bonds, consistent with experimental data from kinetic assays.

Vários problemas do mundo real podem ser analisados como problemas de otimização. Na bioinformática, em especial, como exemplos podem ser citados o alinhamento múltiplo de sequências, a filogenia, a predição de estruturas de proteínas e RNA, entre outros. As Meta-heurísticas Populacionais (MhP) são técnicas baseadas em interações de conjuntos de soluções candidatas, como elementos de uma população, utilizadas na otimização de funções. Seu uso é especialmente interessante na otimização de problemas onde há conhecimento parcial ou nenhum do espaço de busca. O objetivo deste trabalho é investigar o uso de auto-organização da população de um sistema imunológico artificial (AIS) a fim de aplicá-lo no problema de docking, que pode ser visto como um problema de otimização multimodal complexo. O AIS é um tipo de MhP inspirado na microevolução do sistema imunológico adaptativo de organismos complexos. Neste, as soluções candidatas representam células do sistema imunológico que busca se adaptar para a eliminação de um patógeno. O desenvolvimento do algoritmo foi baseado no opt-aiNet, que utiliza dos princípios das teorias de seleção clonal e maturação de afinidade para realizar a otimização de funções. Adicionalmente, o opt-aiNet, inspirado na teoria de redes imunológicas, realiza uma etapa de supressão, que busca eliminar soluções semelhantes, aumentando assim a diversidade populacional. Esta etapa é computacionalmente custosa, dado que é feito o cálculo da distância entre todos os possíveis pares de células (soluções) afim de eliminar aquelas próximas de acordo com um dado critério. A proposta deste trabalho é o desenvolvimento de um algoritmo de supressão auto-organizável, inspirado no fenômeno da criticalidade auto-organizada, buscando diminuir a influência da seleção de parâmetros e a complexidade da etapa de supressão. O algoritmo proposto foi testado em um conjunto de funções contínuas conhecidas e comumente utilizadas pela comunidade de computação evolutiva. Os resultados obtidos foram comparados com aqueles de uma implementação do opt-aiNet. Em adição, foi proposta a utilização de operadores de mutação com distribuição q-gaussiana nos AISs desenvolvidos. O algoritmo foi também aplicado no problema de docking rígido baseado em complementaridade de superfícies e minimização de colisões, especificamente no docking de proteínas. Os resultados foram comparados com aqueles de um algoritmo genético, resultando em um melhor desempenho obtido pelo algoritmo proposto. / Many real world problems can be described as optimization problems. In bioinformatics in special, there is multiple sequence alignment, filogeny and RNA and Protein structure prediction, among others. Population based metaheuristics are techniques based in the interaction of a set of candidate solutions as elements of a population. Its use is specially interesting in optimization problems where there is little or no knowledge of the search space. The objective of this work is to study the use of self-organization of population in an artificial imune system for use in the docking problem, considered a complex multimodal optimization problem. The artificial imunme system is a type of population based methaheuristics inspired in the microevolution of the adaptive immune system of complex organisms. Candidate solutions represent cells of the immune system adapting its antibodies to eliminate a pathogen. The development of the algorithm was based in the opt-aiNet, based in the principles of clonal selection and affinity maturation for function optimization. Additionally, the opt-aiNet, inspired in theories of immune network, makes a suppression stage to eliminate similiar solutions and control diversity. This stage is computationally expensive as it calculates the distance between every possible pair of cells (solutions) eliminating those closer than a threshold. This work proposes a self-organized suppression algorithm inspired in the self-organized criticality, looking to minimize the influence of parameter selection and complexity of the suppression stage in opt-aiNet. The proposed algorithm was tested in a set of well-known functions in the evolutionary computation community. The results were compared to those of an implementation of the opt-aiNet. In addition, we proposed a mutation operator with q-Gaussian distribution for the artificial immune systems. The algorithm was then applied in the rigid protein docking problem based in surface complementarity and colision avoidance. The results were compared with a genetic algorithm and achieved a better performance.

As plantas medicinais são consideradas importantes fontes de compostos biologicamente ativos. Para muitas doenças crônicas, como as neurodegenerações, substâncias que apresentam atividades simultâneas em mais de um alvo relacionado à etiopatologia dessas desordens, constituem potenciais agentes terapêuticos. Nesse contexto, os objetivos deste trabalho foram a avaliação química e biológica de três espécies de samambaias de ocorrência no sul do Brasil: Blechnum binervatum, B. brasiliense e B. occidentale. O isolamento bioguiado foi utilizado para a identificação da(s) substância(s) com potencial atividade em modelos in vitro e in silico relacionados às desordens neurodegenerativas. A avaliação dos extratos e frações permitiu destacar a fração acetato de etila de B. brasiliense como a mais ativa na estabilização de radicais hidroxila (CI50: 12,5 μg/mL) e na inibição da lipoperoxidação (CI50: 10,4 μg/mL), sendo uma das mais ativas frente ao óxido nítrico (CI50: 55,6 μg/mL). Adicionalmente, na inibição da isoforma A da enzima monoamina oxidase (MAO), esta fração foi a que apresentou menor valor de CI50 (28,6 μg/mL). As frações diclorometano também apresentaram bons resultados na inibição da MAO-A, sendo algumas ativas igualmente como antioxidantes. Frente à MAO-B, os extratos e frações das três espécies vegetais demonstraram menores efeitos e foram inativos frente às enzimas acetil e butiril colinesterase, não demonstrando toxicidade em células polimorfonucleares (PMN) de ratos Wistar, na concentração de 1 mg/mL. Utilizando células-tronco cultivadas, os extratos e frações selecionadas, nas concentrações de 100 a 500 μg/mL, não afetaram a viabilidade celular e não apresentaram efeitos tóxicos. Análises químicas por cromatografia líquida de alta eficiência, acoplada a detector de arranjo de diodos e espectrometria de massas, permitiram a identificação de isômeros dos ácidos cafeoil quínico e cafeoil chiquímico nos extratos das três espécies estudadas. B. binervatum apresentou, ainda, ácido cafeico glicosilado, ácido isosalvianólico A e ácido rosmarínico sulfatado. Ácido salvianólico F foi identificado em B. binervatum e B. occidentale, bem como isômeros do ácido brainico. Ácido rosmarínico foi caracterizado em B. binervatum e B. brasiliense. Quercetina 3-O-glicosídeo e vicenina-2 também foram identificadas. As análises por cromatografia gasosa, acoplada à espectrometria de massas demonstraram que o diterpeno neofitadieno foi o composto majoritário nas frações diclorometano de Blechunm e nas frações hexano de B. occidentale e B. binervatum. Para a fração hexano de B. brasiliense, β-sitosterol foi o principal componente. A partir da fração acetato de etila de B. brasiliense foi isolado o ácido rosmarínico, o qual se mostrou ativo nos ensaios de atividade antioxidante, na inibição da catecol-O-metil transferase (CI50: 26,7 μM) e da MAO-A (CI50: 50,1 μM), sendo proposto mecanismo reversível de inibição desta enzima. Nos estudos de docking foram verificadas as interações moleculares entre o composto e as enzimas MAO-A e COMT, por ligações de hidrogênio e interações hidrofóbicas nos sítios ativos enzimáticos. O composto não apresentou efeitos tóxicos em células PMN de roedores, nas concentrações de 0,5 e 5 mM. Os ácidos rosmarínico e clorogênico isolados do extrato de B. binervatum não influenciaram na viabilidade celular e não induziram efeito tóxico sobre células-tronco (100 a 500 μM), sendo que o ácido rosmarínico foi capaz, ainda, de induzir proliferação celular. Capacidade protetora contra danos celulares causados por H2O2 (1400 μM) foi observada para ambas as substâncias, nas concentrações de 10-100 μM, sendo os resultados corroborados pelas imagens de microscopia celular. O ácido rosmarínico apresentou melhores respostas quando comparado ao ácido clorogênico, sendo mais efetivo na inibição dos danos por H2O2. O conjunto dos resultados obtidos ressalta a importância dos estudos associados químico-biológico de espécies vegetais e aponta para as potencialidades de samambaias como fontes de produtos bioativos, capazes de atuar sobre múltiplos alvos enzimáticos e não-enzimáticos relacionados a doenças neurodegenerativas. / Medicinal plants are considered important sources of biologically active compounds. For many chronic diseases, such as neurodegenerations, substances that present simultaneous activities in more than one target related to the etiopathology of these disorders are considered potential therapeutic agents. In this context, the aims of the study were the chemical and biological evaluation of three fern species, occurring in south Brazil: Blechnum binervatum, B. brasiliense and B. occidentale. The bioguided isolation was employed to identify compound(s) with potential activities at in vitro and in silico models associated to neurodegenerative disorders. The biological evaluation of extracts and fractions allowed to highlight the ethyl acetate fraction of B. brasiliense, which was the most active in the stabilization of hydroxyl radicals (IC50: 12.5 μg/mL) and on lipoperoxidation inhibition (IC50: 10.4 μg/mL), being one of the most active sample against nitric oxide (IC50: 55.6 μg/mL). Furthermore, on the inhibition of isoform A from monoamine oxidase (MAO), this fraction showed the lowest IC50 value (28.6 μg/mL). The dichloromethane fractions also presented good results in the MAO-A inhibition, being some of them active as antioxidants, too. Against MAO-B, extracts and fractions of the three species demonstrated reduced effects and all samples were inactive in the acetyl and butyryl cholinesterase inhibition, showing no toxic effects to polymorphonuclear cells (PMN) from Wistar rats, at 1 mg/mL. Using cultured stem cells, the selected extracts and fractions at 100 to 500 μg/mL did not affect cell viability and absence of cytotoxic effects was observed. The chemical analysis by high performance liquid chromatography, coupled to photodiode array detector and mass spectrometry, allowed the identification of caffeoyl quinic acid and caffeoyl shikimic acid isomers in the three studied fern species. B. binervatum presented also glycosilated caffeic acid, isosalvianolic acid A and sulphated rosmarinic acid. Salvianolic acid F was identified in B. binervatum and B. occidentale, as well as, brainic acid isomers. Rosmarinic acid was characterized in B. binervatum and B. brasiliense. Quercetin 3-O-glycoside and vicenin-2 were also found. Analysis by gas chromatography with mass spectrometry showed the diterpene neophytadiene was the major compound in dichloromethane fractions of Blechunm and in hexane fractions of B. occidentale and B. binervatum. For the hexane fraction from B. brasiliense, β-sitosterol was majority. From the ethyl acetate fraction of B. brasiliense was isolated rosmarinic acid, which was shown to be active in antioxidant assays, in the inhibition of catechol-O-methyltransferase (IC50: 26.7 μM) and MAO-A (IC50: 50.1 μM), being suggested a reversible inhibition mechanism against this enzyme. In docking studies were observed molecular interactions between the compound and MAO-A and COMT enzymes, via hydrogen bonds and hydrophobic interactions in enzyme active sites. The compound did not induce toxic effects to rodent PMN cells, at concentrations of 0.5 and 5 mM. The rosmarinic and chlorogenic acids, isolated from B. binervatum extract, did not influence cell viability and did not induce toxicity to stem cells (100 to 500 μM), the rosmarinic acid was also capable to induce cell proliferation. Protective ability against H2O2-induced cell damage (1400 μM) was observed for both substances at concentrations of 10-100 μM, and the results were supported by cellular microscopy images. Rosmarinic acid presented better responses compared with chlorogenic acid, being powerful inhibitor against H2O2-induced damage. The overall results highlights the importance of associated chemical-biological studies of plant species and points at the potential of ferns as sources of bioactive compounds, capable of modulating multiple enzymatic and non-enzymatic targets related to neurodegenerative diseases.