Os projetos genoma têm fornecido uma grande quantidade de informação sobre a arquitetura gênica e sobre a configuração física de suas respectivas regiões flanqueadoras (RF). Estas RF contêm informações com o potencial de auxiliar na elucidação de vários processos biológicos, como os mecanismos de expressão gênica e de sua regulação. Estes mecanismos são de extrema importância para a compreensão do correto funcionamento dos organismos e das patologias que os afetam. Uma parte significativa dos mecanismos de controle de expressão gênica atuam na fase transcricional. Na base destes mecanismos está o recrutamento de proteínas que se ligam às regiões promotoras da transcrição, as quais são segmentos específicos de DNA que podem estar localizados tanto próximos à região de início da transcrição (TSS) quanto a centenas ou até a milhares de pares de bases dela. Essas proteínas compõem a maquinaria transcricional e podem ativar ou inibir o processo de transcrição. Experimentalmente, os segmentos regulatórios podem ser identificadas utilizando métodos complexos de biologia molecular, tais como SELEX, ChiP-ChiP, ChIP-Seq, dentre outros. Uma estratégia alternativa aos métodos experimentais é a utilização de metodologias computacionais. Análises computacionais tendem a ser mais rápidas, baratas e flexíveis do que protocolos experimentais, além de poderem ser utilizadas em larga escala. Atualmente, os métodos computacionais disponíveis necessitam de informações experimentais para a definição de padrões globais de preferências de sequências de DNA para a ligação de fatores de transcrição (TFBS, em inglês transcription factor binding sites). Entretanto, esses métodos apresentam uma elevada taxa de falso positivos e, por vezes, apresentam também taxas significativas de falso negativos, além de serem limitados ao estudo de fatores de transcrição de espécies bem conhecidas, o que diminui a área de aplicação dos mesmos. Diante deste cenário, o uso de métodos computacionais que não necessitem da informação referente aos sítios de ligação, bem como os que utilizem parâmetros mais robustos de detecção dos resultados, em detrimento dos escores de pontuação provindos de alinhamentos, podem acrescentar uma sensível melhoria ao processos de predição de regiões regulatórias. Neste projeto, foi desenvolvido um novo modelo computacional (TFBSAnalyzer) para análise e identificação de TFBS em elementos regulatórios, que utiliza técnicas de modelagem molecular para a construção de complexos entre um fator de transcrição ancorado a estruturas de DNA com sequências variáveis de bases e, através de cálculos termodinâmicos de entalpia de ligação, determina uma função de pontuação baseada na energia de ligação e realiza a predição de sítios de ligação ao DNA para o fator de transcrição em análise. Esta abordagem foi testada com três fatores de transcrição como sistemas-modelo, pertencentes à família dos receptores nucleares, a saber: o receptor de estrógeno ER-alfa (Estrogen Receptor Alpha), o receptor de ácido retinoico RAR-beta (Retinoid Acid Receptor Beta) e o receptor X retinóico RXR (Retinoid X Receptor). Os modelos previstos computacionalmente foram comparados aos dados experimentais disponíveis para estes receptores nucleares, os quais apresentaram as seguintes taxas de FP/FN: 10%/0 para RAR-beta e RXR, 21%/6% para ER-alfa. Também simulamos um experimento de ChIP-seq do ER-alfa no genoma humano, cujos genes selecionados foram submetidos a uma análise de enriquecimento de fatores de transcrição curados experimentalmente, que fazem sua regulação, revelando que o receptor de estrógeno está realmente envolvido no processo. Para mostrar a aplicabilidade geral de nosso método, nós modelamos a distribuição de energia de ligação para o receptor NHR-28 isoforma a de Caenorhabditis elegans com DNA . Obtivemos distribuições de energia semelhantes àquelas encontradas para os NRs modelos, portanto seria possível aplicar o método para buscar possíveis TFBSs para este receptor no genoma de C. elegans. Os dados gerados e as metodologias desenvolvidas neste projeto devem acrescentar uma sensível melhoria aos processos de predição de regiões regulatórias e consequentemente auxiliar no entendimento dos mecanismos envolvidos no processo de expressão gênica e de sua regulação. / The genome projects have provided a lot of information about the genetic architecture, as well as on the physical configuration of their flanking regions (FR). These FR have the potential to aid in the elucidation of many biological processes, such as the mechanisms involved in gene expression and its regulation. These mechanisms are extremely important for undeerstanfind the correct functioning of organisms as well as the pathologies that affect them. A significant part of the control mechanisms of gene expression act during transcription. On the basis of this mechanisms is the recruitment of proteins that bind to promoter regions of transcription, which are specific segments of DNA that can be located either near the transcription start site or at hundreds or even thousands of base pairs away. These proteins form the transcription machinery, which can activate or inhibit the transcription process. The regulatory segments can be identified experimentally using complex methods of molecular biology, such as SELEX, ChIP-chip, ChIP-seq, among others. An alternative strategy to these experimental methods is the use of computational methodologies for predicting regulatory regions. Computational analysis tend to be faster, cheaper and more flexible than the experimental protocols, and can be used on a larger scale. Currently, the available computational methods require information previously obtained from experiments in order to define global standards of preference of DNA-Binding sequences for transcription factors (TFBS - Transcription Factor Binding Sites). However, these methods have a high rate of false positives and sometimes also have significant rates of false negatives, besides being limited to the study of transcription factors of well-known species, which decreases their application area. In this scenario, the use of computational methods that do not require previous information concerning the binding sites and use more robust parameters of results detection, instead of alignment scores, may add significant improvement to the processes of predicting regulatory regions. In this project, we developed a new computational model TFBSAnalyzer) for analysis and identification of regulatory elements using molecular modeling techniques for the construction of complexes between a transcription factor bound to specific DNA structures with variable sequences of bases and, by means of thermodynamic calculations of bond enthalpy, provides a scoring function based on the binding energy and predicts the DNA binding sites for the transcription factor in analysis. This approach was tested initially with three transcription factors as models, belonging to the nuclear receptor family, namely estrogen receptor ER-alpha (Estrogen Receptor Alpha), the retinoic acid receptor RAR-beta (Retinoid Acid Receptor Beta) and the retinoic X receptor RXR (Retinoid X Receptor). The computationally predicted models were compared to experimental data available for these nuclear receptors, and presented the following rates of FP/FN: 10%/0 for RAR-beta and RXR, 21%/6% for ER-alpha. We also simulated an experiment of ChIP-seq with ER-alpha with the human genome, where the selected genes were subjected to a transcription factor enrichment analysis, with curated information, revealing that the estrogen receptor is indeed involved in their regulation. To show that our method has a general applicability, we modeled the binding energy distribution for the NHR-28 receptor, isoform a, from Caenorhabditis elegans. The energy distributions obtained were similar to the ones obtained for the model NR, so it would be possible to use the method and search for possible TFBS in the C. elegans genome. The data generated and the methodologies developed in this project should add a significant improvement to the prediction processes of regulatory regions and, consequently, help to understand the mechanisms involved in the gene expression process and its regulation.

Os projetos genoma têm fornecido uma grande quantidade de informação sobre a arquitetura gênica e sobre a configuração física de suas respectivas regiões flanqueadoras (RF). Estas RF contêm informações com o potencial de auxiliar na elucidação de vários processos biológicos, como os mecanismos de expressão gênica e de sua regulação. Estes mecanismos são de extrema importância para a compreensão do correto funcionamento dos organismos e das patologias que os afetam. Uma parte significativa dos mecanismos de controle de expressão gênica atuam na fase transcricional. Na base destes mecanismos está o recrutamento de proteínas que se ligam às regiões promotoras da transcrição, as quais são segmentos específicos de DNA que podem estar localizados tanto próximos à região de início da transcrição (TSS) quanto a centenas ou até a milhares de pares de bases dela. Essas proteínas compõem a maquinaria transcricional e podem ativar ou inibir o processo de transcrição. Experimentalmente, os segmentos regulatórios podem ser identificadas utilizando métodos complexos de biologia molecular, tais como SELEX, ChiP-ChiP, ChIP-Seq, dentre outros. Uma estratégia alternativa aos métodos experimentais é a utilização de metodologias computacionais. Análises computacionais tendem a ser mais rápidas, baratas e flexíveis do que protocolos experimentais, além de poderem ser utilizadas em larga escala. Atualmente, os métodos computacionais disponíveis necessitam de informações experimentais para a definição de padrões globais de preferências de sequências de DNA para a ligação de fatores de transcrição (TFBS, em inglês transcription factor binding sites). Entretanto, esses métodos apresentam uma elevada taxa de falso positivos e, por vezes, apresentam também taxas significativas de falso negativos, além de serem limitados ao estudo de fatores de transcrição de espécies bem conhecidas, o que diminui a área de aplicação dos mesmos. Diante deste cenário, o uso de métodos computacionais que não necessitem da informação referente aos sítios de ligação, bem como os que utilizem parâmetros mais robustos de detecção dos resultados, em detrimento dos escores de pontuação provindos de alinhamentos, podem acrescentar uma sensível melhoria ao processos de predição de regiões regulatórias. Neste projeto, foi desenvolvido um novo modelo computacional (TFBSAnalyzer) para análise e identificação de TFBS em elementos regulatórios, que utiliza técnicas de modelagem molecular para a construção de complexos entre um fator de transcrição ancorado a estruturas de DNA com sequências variáveis de bases e, através de cálculos termodinâmicos de entalpia de ligação, determina uma função de pontuação baseada na energia de ligação e realiza a predição de sítios de ligação ao DNA para o fator de transcrição em análise. Esta abordagem foi testada com três fatores de transcrição como sistemas-modelo, pertencentes à família dos receptores nucleares, a saber: o receptor de estrógeno ER-alfa (Estrogen Receptor Alpha), o receptor de ácido retinoico RAR-beta (Retinoid Acid Receptor Beta) e o receptor X retinóico RXR (Retinoid X Receptor). Os modelos previstos computacionalmente foram comparados aos dados experimentais disponíveis para estes receptores nucleares, os quais apresentaram as seguintes taxas de FP/FN: 10%/0 para RAR-beta e RXR, 21%/6% para ER-alfa. Também simulamos um experimento de ChIP-seq do ER-alfa no genoma humano, cujos genes selecionados foram submetidos a uma análise de enriquecimento de fatores de transcrição curados experimentalmente, que fazem sua regulação, revelando que o receptor de estrógeno está realmente envolvido no processo. Para mostrar a aplicabilidade geral de nosso método, nós modelamos a distribuição de energia de ligação para o receptor NHR-28 isoforma a de Caenorhabditis elegans com DNA . Obtivemos distribuições de energia semelhantes àquelas encontradas para os NRs modelos, portanto seria possível aplicar o método para buscar possíveis TFBSs para este receptor no genoma de C. elegans. Os dados gerados e as metodologias desenvolvidas neste projeto devem acrescentar uma sensível melhoria aos processos de predição de regiões regulatórias e consequentemente auxiliar no entendimento dos mecanismos envolvidos no processo de expressão gênica e de sua regulação. / The genome projects have provided a lot of information about the genetic architecture, as well as on the physical configuration of their flanking regions (FR). These FR have the potential to aid in the elucidation of many biological processes, such as the mechanisms involved in gene expression and its regulation. These mechanisms are extremely important for undeerstanfind the correct functioning of organisms as well as the pathologies that affect them. A significant part of the control mechanisms of gene expression act during transcription. On the basis of this mechanisms is the recruitment of proteins that bind to promoter regions of transcription, which are specific segments of DNA that can be located either near the transcription start site or at hundreds or even thousands of base pairs away. These proteins form the transcription machinery, which can activate or inhibit the transcription process. The regulatory segments can be identified experimentally using complex methods of molecular biology, such as SELEX, ChIP-chip, ChIP-seq, among others. An alternative strategy to these experimental methods is the use of computational methodologies for predicting regulatory regions. Computational analysis tend to be faster, cheaper and more flexible than the experimental protocols, and can be used on a larger scale. Currently, the available computational methods require information previously obtained from experiments in order to define global standards of preference of DNA-Binding sequences for transcription factors (TFBS - Transcription Factor Binding Sites). However, these methods have a high rate of false positives and sometimes also have significant rates of false negatives, besides being limited to the study of transcription factors of well-known species, which decreases their application area. In this scenario, the use of computational methods that do not require previous information concerning the binding sites and use more robust parameters of results detection, instead of alignment scores, may add significant improvement to the processes of predicting regulatory regions. In this project, we developed a new computational model TFBSAnalyzer) for analysis and identification of regulatory elements using molecular modeling techniques for the construction of complexes between a transcription factor bound to specific DNA structures with variable sequences of bases and, by means of thermodynamic calculations of bond enthalpy, provides a scoring function based on the binding energy and predicts the DNA binding sites for the transcription factor in analysis. This approach was tested initially with three transcription factors as models, belonging to the nuclear receptor family, namely estrogen receptor ER-alpha (Estrogen Receptor Alpha), the retinoic acid receptor RAR-beta (Retinoid Acid Receptor Beta) and the retinoic X receptor RXR (Retinoid X Receptor). The computationally predicted models were compared to experimental data available for these nuclear receptors, and presented the following rates of FP/FN: 10%/0 for RAR-beta and RXR, 21%/6% for ER-alpha. We also simulated an experiment of ChIP-seq with ER-alpha with the human genome, where the selected genes were subjected to a transcription factor enrichment analysis, with curated information, revealing that the estrogen receptor is indeed involved in their regulation. To show that our method has a general applicability, we modeled the binding energy distribution for the NHR-28 receptor, isoform a, from Caenorhabditis elegans. The energy distributions obtained were similar to the ones obtained for the model NR, so it would be possible to use the method and search for possible TFBS in the C. elegans genome. The data generated and the methodologies developed in this project should add a significant improvement to the prediction processes of regulatory regions and, consequently, help to understand the mechanisms involved in the gene expression process and its regulation.

STAT5 est une protéine de la signalisation cellulaire normale, qui peut jouer un rôle important dans la transformation, la survie et à la résistance aux inhibiteurs de tyrosine kinase des cellules tumorales. Son activation constitutive par phosphorylation est liée à la présence de protéines oncogéniques comme la protéine de fusion BCR/ABL1 (leucémie myéloïde chronique) ou de formes mutées de KIT (mastocytoses), par exemple. L’inhibition pharmacologique de STAT5 constitue donc un enjeu thérapeutique majeur pour plusieurs pathologies malignes. Nous avons réalisé la première modélisation et les simulations de dynamique moléculaire des principales formes de STAT5 : la forme monomérique cytoplasmique phosphorylée ou non, et la forme dimérique phosphorylée et liée à l’ADN. Nous avons caractérisé les propriétés dynamiques et le réseau allostérique intramoléculaire des monomères de STAT5. Les résultats générés montrent des variations structurales et dynamiques liées à la différence de séquence primaire des isoformes de STAT5 et/ou à la présence du groupement phosphate. Deux poches à la surface des protéines ont également été caractérisées. Leur localisation à proximité de voies de communication allostériques suggère que ces poches pourraient constituer des sites de modulation des fonctions de STAT5. Nous avons également caractérisé les liaisons hydrogènes entre les monomères constituant les dimères de STAT5 et leur reconnaissance de l’ADN. En outre, nous avons identifié des résidus clés aux interfaces entre les entités moléculaires, nous permettant de mieux comprendre les effets de mutations de STAT5 observées en clinique dans certaines pathologies malignes. / STAT5 is a protein involved in normal cell signalling that is crucial for transformation, survival and resistance to tyrosine kinase inhibitors of tumour cells. The constitutive phosphorylation activates STAT5 and is related to oncogenic proteins like the hybrid protein BCR/ABL1 (chronic myeloid leukaemia) or mutated KIT receptor (mastocytosis). The pharmacologic inhibition of STAT5 is thus a major therapeutic concern in several malignant pathologies. We performed the first modelling and molecular dynamics simulations of the main cellular species of STAT5: the cytoplasmic phosphorylated or unphosphorylated monomer, and the phosphorylated dimer bound to DNA. We characterized the dynamical properties and the intramolecular allosteric network of the monomers. The generated results show structural and dynamic variations linked to the primary sequence changes between the two STAT5 isoforms and/or to the phosphate group. Two pockets were characterized at the surface of STAT5. Their location at close proximity of allosteric communication pathways suggests new putative inhibition sites to modulate STAT5 functions. We also described the hydrogen bonds network between the monomers of the dimeric species and the recognition of the DNA. We identified key residues at the interfaces, allowing us to better understand the effects of clinically relevant STAT5 mutations observed in malignancies.

During the past decade the effects of macromolecular crowding on reaction pathways is gaining in prominence. The stress is to move out of the realms of ideal solution studies and make conceptual modifications that consider non-ideality as a variable in our calculations. In recent years it has been shown that molecular crowding exerts significant effects on all in vivo processes, from DNA conformational changes, protein folding to DNA-protein interactions, enzyme pathways and signalling pathways. Both thermodynamic as well as kinetic parameters vary by orders of magnitude in uncrowded buffer system as compared to those in the crowded cellular milieu. Ignoring these differences will restrict our knowledge of biology to a “model system” with few practical understandings. The recent expansion of the genome database has stimulated a study on numerous previously unknown proteins. This has whetted our thirst to model the cellular determinants in a more comprehensive manner. Intracellular extract would have been the ideal solution to re-create the cellular environment. However, studies conducted in this solution will be contaminated by interference with other biologically active molecule and relevant statistical data cannot be extracted out from it. Recent advances in methodologies to mimic the cellular crowding include use of inert macromolecules to reduce the volume occupancy of target molecules and the use of immobilization techniques to increase the surface density of molecules in a small volumetric region. The use of crowding agents often results in non-specific interaction and side-reactions like aggregation of the target molecules with the crowding agents themselves. Immobilization of one of the interacting partners reduces the probability of aggregation and precipitation of bio-macromolecules by restricting their degrees of freedom. Covalent linkage of molecules on solid support is used extensively in research for creating a homogeneous surface of bound molecules which can be interrogated for their reactivity. However, when it comes to biomolecules, direct immobilization on solid support or use of organic linkers often results in denaturation. The use of bio-affinity immobilization techniques can help us overcome this problem. Since mild conditions are needed to regenerate such a surface, it finds universal applicability as bio-memory chips. This thesis focuses on our attempts to design a physiologically viable immobilization technique for following rotein-protein/protein-DNA interactions. The work explores the mechanism for biological interactions related to transcription process in E. coli. Chapter 1 deals with the literary survey of the importance and effects of molecular crowding on biological reactions. It gives a brief history of the efforts been made so far by experimentalists, to mimic macromolecular crowding and the methods applied. The chapter tries to project an all-round perspective of the pros and cons of different immobilization techniques as a means to achieve a high surface density of molecules and the advancements so far. Chapter 2 deals with the detailed technicality and applicability of the Langmuir-Blodgett method. It discusses the rationale behind our developing this technique as an alternate means of bio-affinity immobilization, under physiologically compatible conditions. It then goes on to describe our efforts to follow the sequence-specific and sequential assembly process of a functional RNA polymerase enzyme with one immobilized partner and also explore the role of omega subunit of RNAP in the reconstitution pathway. This chapter uses the assembly process of a multi-subunit enzyme to evaluate the efficiency of the LB system as a universal two-dimensional scaffold to follow sequence-specific protein-ligand interaction. Chapter 3 discusses the application of LB technique to quantitatively evaluate the kinetics and thermodynamics of promoter-RNA polymerase interaction under conditions of reduced dimensionality. Here, we follow the interaction of T7A1 phage promoter with Escherichia coli RNA polymerase using our Langmuir-Blodgett technique. The changes in mechanistic pathway and trapping of kinetic intermediates are discussed in detail due to the imposed restriction in the degrees of freedom of the system. The sensitivity of this detection method is compared vis-a-vis conventional immobilization methods like SPR. This chapter firmly establishes the universal application of LB technique as a means to emulate molecular crowding and as a sensitive assay for studying the effects of such crowding on vital biological reaction pathway. Chapter 4 describes the mechanistic pathway for the physical binding of MsDps1 protein with long dsDNA in order to physically protect DNA during oxidative stress. The chapter describes in detail the mechanism of physical sequestering of non-specific DNA strands and compaction of the genome under conditions where a kinetic bottleneck has been applied. The data obtained is compared with results obtained in the previous chapter for the sequence-specific DNA-protein interaction in order to understand the difference in recognition process between regulatory and structural proteins binding to DNA. Chapter 5 deals with the evaluation of the σ-competition model in E. coli for three different sigma factors (all belonging to the σ-70 family). Here again, we have evaluated the kinetic and thermodynamic parameters governing the binding of core RNAP with its different sigma factors (σ70, σ32and σ38) and performed a comparative study for the binding of each sigma factor to its core using two different non-homogeneous immobilization techniques. The data has been analyzed globally to resolve the discrepancies associated with establishing the relative affinity of the different sigma factors for the same core RNA polymerase under physiological conditions. Chapter 6 summarizes the work presented in this thesis. In the Appendix section we have followed the unzipping of promoter DNA sequence using Optical Tweezers in an attempt to follow the temporal fluctuations occurring in biological reactions in real time and at a single molecule level.

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